Deploying six-dimensional movable antenna (6DMA) systems in Internet-of-Vehicles (IoV) scenarios can greatly enhance spectral efficiency. However, the high mobility of vehicles causes rapid spatio-temporal channel variations, posing a significant challenge to real-time 6DMA optimization. In this work, we pioneer the application of 6DMA in IoV and propose a low-complexity, instantaneous channel state information (CSI)-free dynamic configuration method. By integrating vehicle motion prediction with offline directional response priors, the proposed approach optimizes antenna positions and orientations at each reconfiguration epoch to maximize the average sum rate over a future time window. Simulation results in a typical urban intersection scenario demonstrate that the proposed 6DMA scheme significantly outperforms conventional fixed antenna arrays and simplified 6DMA baseline schemes in terms of total sum rate.

Massive access or communication, emerging as one of six usage scenarios in 6G, has attracted considerable recent attention due to its potential to empower next-generation industrial cyber-physical systems such as smart grids, factory automation, industrial internet-of-things (IIoT), etc. However, to guarantee its QoS, the associated power control becomes computationally intractable with a huge number of users. In this paper, we present a tractable algorithm for power control in massive access, based on mean-field approximations. In particular, our aim is to maximize the overall throughput in each scheduling period, at the beginning of which each user has a finite number of backlogged bits. To achieve this goal and overcome the curse of dimensionality, a mean-field game (MFG) is formulated. Unfortunately, the formulated MFG is still a non-convex optimization problem. Enlightened by MAPEL, an efficient solver for non-convex power control problem, we leverage multiplicative linear fractional programming (MLFP) to tackle the non-convexity in our formulated MFG. Furthermore, the mean-field approximation assisted power control strategy requires low signaling overhead consumed for estimation and feedback of channel state information (CSI). Simulation results demonstrate that the proposed tractable power control attains substantial performance gains in both the overall throughput and computational complexity.

Movable antenna (MA) systems have emerged as a promising technology for future wireless communication systems. The movement of antennas gives rise to mutual coupling (MC) effects, which have been previously ignored and can be exploited to enhance the capacity of multiple-input multiple-output (MIMO) systems. To this end, we first model an MA-enabled point-to-point MIMO communication system with MC effects using a circuit-theoretic framework. The capacity maximization problem is then formulated as a non-concave optimization problem and solved via a block coordinate ascent (BCA)-based algorithm. The subproblem of optimizing MA positions is challenging due to the presence of the analytically intractable MC matrices. To overcome this difficulty, we develop a trust region method (TRM)-based algorithm to optimize MA positions, wherein Sylvester equations are employed to compute the derivatives of the inverse square roots of the MC matrices. Simulation results show significant capacity gains from leveraging MC effects, primarily due to customizable MC matrices and superdirectivity.

Integrated sensing and communications (ISAC) is a key enabler for 6G applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilots and random data payloads, poses challenges for target detection, since 1) these components jointly affect both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in bistatic systems. To address these, we develop a generalized likelihood ratio test (GLRT)-based detector that exploits the known pilots and the statistical properties of the unknown payloads. Given the exact performance is analytically intractable, an asymptotic analysis of the false alarm probability is conducted. Simulation results validate the theoretical derivations and demonstrate the superiority of the proposed detector, which highlights the importance of tailored ISAC detection that fully leverages data payload resources.

Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliver global-scale computing services. However, the intensive computation within SCPN incurs a significant "unseen cost": the frequent charge-discharge cycles accelerate the physical degradation of satellites’ life-limiting and high-cost batteries, thereby threatening the long-term operational viability of such a system. Existing approaches, often relying on indirect metrics like Depth of Discharge (DoD) and neglecting the complex, nonlinear degradation process of battery aging, fail to accurately quantify this cost. To address this, we introduce a high-fidelity, physics-driven model that quantitatively links computational workload parameters to the nonlinear battery degradation. Building on this model, we formulate a degradation-aware scheduling problem and analyze heuristic policies across different energy regimes. Simulations reveal that the optimal strategy should be adaptive: in solar-rich conditions, a myopic policy maximizing instantaneous solar utilization is superior, whereas under energy scarcity, a reactive policy leveraging real-time battery state significantly extends lifetime.

A version of the accepted manuscript is available in arXiv at arXiv:2603.04898v1 [cs.LG] (https://arxiv.org/abs/2603.04898). Comments: This paper has been accepted by infocom. The source code has been released at: https://github.com/qiongwu86/U-Parking . Submission history: From: Qiong Wu: [v1] Thu, 5 Mar 2026 07:38:51 UTC (499 KB).

Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam management with low training overhead. This paper proposes an interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments. Our approach utilizes multimodal data to construct virtual base stations (VBSs), which are geometrically defined as mirror images of the base station across reflecting surfaces reconstructed from 3D LiDAR points. These VBSs provide a sparse and spatial representation of the dominant features of the wireless environment. Based on the constructed VBSs, we develop a VBS-assisted beam alignment scheme comprising coarse channel reconstruction followed by partial beam training. Numerical results demonstrate that the proposed method achieves near-optimal performance in terms of spectral efficiency.

Low Earth orbit (LEO) satellite constellations have become a critical enabler for global coverage, utilizing numerous satellites orbiting Earth at high speeds. By decomposing complex network services into lightweight service functions, network function virtualization (NFV) transforms global network services into diverse service function chains (SFCs), coordinated by resource-constrained LEOs. However, the dynamic topology of satellite networks, marked by highly variable inter-satellite link delays, poses significant challenges for designing efficient routing strategies that ensure reliable and low-latency communication. Many existing routing methods suffer from poor scalability and degraded performance, limiting their practical implementation. To address these challenges, this paper proposes a novel SFC routing approach that leverages the statistical properties of network link states to mitigate instability caused by instantaneous modeling in dynamic satellite networks. Through comprehensive simulations on end-to-end shortest-path propagation delays in LEO networks, we identify and validate the statistical stability of multi-hop routes. Building on this insight, we introduce the Stability-Aware Multi-Stage Graph Routing (SA-MSGR) algorithm, which incorporates pre-computed average delays into a multi-stage graph optimization framework. Extensive simulations demonstrate the superior performance of SA-MSGR, achieving significantly lower and more predictable end-to-end SFC delays compared to representative baseline strategies.

Integrating communication, sensing, and power transfer (ICSPT) is an emerging network paradigm for the sixth-generation (6G) systems, which is able to provide concurrent communication and sensing functions while simultaneously wirelessly powering low-power Internet of Things (IoT) devices with shared spectrum and hardware resources. To enhance the performance of ICSPT in fading channels, the outage probability (OP)constrained robust transmit beamforming design (OP-RTBD) is proposed, and a transmit power minimization problem is formulated with imperfect channel state information (CSI) by jointly optimizing information, sensing, and energy beam vectors at the base station (BS), subject to OP constraints on the communication rate, sensing Cramér-Rao bound, and energy transfer. To solve the non-convex problem, we propose a Bernstein-type inequality (BTI)-based method to conservatively approximate the probabilistic constraints to handle the CSI uncertainty. Then, a semi-positive definite relaxation-based method is proposed to solve the approximated problem. Simulation results show that the proposed OP-RTBD achieves near-optimal performance compared to the exhaustive search method with only less than 4% deviation, and it also significantly reduces the transmit power compared to baselines. Moreover, OP-RTBD exhibits strong robustness, achieving performance very close to that in perfect CSI scenarios, with a deviation of only less than 10%. Besides, the simulation results indicate that the BS’s transmit power should be allocated with priority to communication requirements over sensing and power transfer demands. Additionally, they further demonstrate that to simultaneously meet communication, sensing, and power transfer requirements, our proposed OP-RTBD in ICSPT is more energy-efficient, reducing energy consumption by approximately 10% and 20% compared to SWIPT and ISAC, respectively.

Integrated sensing and communication (ISAC) represents a key paradigm for future wireless networks. However, existing approaches often require waveform modifications, dedicated pilots, or additional overhead that complicates standards integration. We propose “sensing for free”—performing multi-source localization without pilots by reusing random and unknown uplink data symbols, where sensing happens simultaneously with data transmission, making it directly compatible with the 3GPP 5G NR and 6G specifications. With the ever-increasing number of devices in dense 6G networks, this approach becomes particularly compelling when combined with sparse arrays, which can localize a much larger number of sources compared to uniform arrays through the enlarged virtual array. However, existing pilot-free multi-source localization algorithms for sparse arrays have numerous drawbacks. They mostly first reconstruct an extended covariance matrix and then apply subspace methods, which incur prohibitive cubic complexity while being limited to second-order statistics. Performance degrades under non-Gaussian modulated data symbols in cellular wireless networks as the higher-order statistics that could further enhance the localization capability remain unexploited. We address these challenges with an attention-only transformer that directly processes raw signal snapshots for grid-less end-to-end direction-of-arrival (DOA) estimation. The model efficiently captures higher-order statistics while being permutation-invariant and adaptive to varying numbers of snapshots. Our algorithm greatly outperforms state-of-the-art artificial intelligence (AI)-based benchmarks with over 30× reduction in parameters and runtime, and enjoys excellent generalization under practical mismatches. In addition, it can effectively handle multipath propagation and mixed modulation types. Beyond localization, our algorithm can also enhance multi-user MIMO beam training through angular reciprocity. The estimated DOAs in the uplink data transmission stage can significantly prune downlink beam sweeping candidates and enhance system throughput via sensing-assisted beam management. Overall, this work demonstrates how reusing existing random data payloads for sensing can enhance both multi-source localization and beam management, two key AI-for-communication use cases in 3GPP efforts towards 6G.

This paper presents a mobile computing-based framework for distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenarios under intelligent transportation systems (ITS). A centralized trajectory planning problem is first formulated to optimize merging efficiency and safety. To eliminate reliance on a central controller, a distributed solution is developed using ADMM algorithm based on V2X communication, enabling CAVs to collaboratively compute trajectories in parallel by leveraging their onboard computing power. Building on this, a multi-vehicle model predictive control (MPC) problem is proposed to enhance system stability under strict constraints. To solve it efficiently, a Distributed Cooperative Iterative MPC (DCIMPC) method is introduced, which decomposes and reformulates the problem for real-time distributed execution across CAVs. Together, these methods form a mobile edge computing-driven control framework. Simulations and experiments demonstrate significant improvements in computational efficiency and system performance, highlighting the potential of mobile computing in cooperative CAV control.

This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.

Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in the literature. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We subsequently introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics. We obtain mathematical relationships of these metrics with the computational resources required by GenAI models via experimentation. Afterward, we decompose the formulation into a model caching subproblem on a long-timescale and a resource allocation subproblem on a short-timescale. Since the variables to be solved are discrete and continuous, respectively, we leverage a double deep Q-network (DDQN) algorithm to solve the former subproblem and propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm to solve the latter. The proposed D3PG algorithm makes an innovative use of diffusion models as the actor network to determine optimal resource allocation decisions. Consequently, we integrate these two learning methods within the overarching two-timescale deep reinforcement learning (T2DRL) algorithm, the performance of which is studied through comparative numerical simulations.

In federated learning (FL), although the original intention of “available but not visible” data is to allay data privacy concerns, it potentially brings new security threats, particularly poisoning attacks that target such “not visible” local data. Intuitively, such data poisoning attacks have great potential in stealthily degrading global FL outcomes, and are expected to be even stealthier if being enhanced by generative models like generative adversarial networks (GANs). However, existing defense methods have not been thoroughly challenged in this regard and generally fail to be aware of a local generation of seemingly legitimate poisoned data. With a growing concern on potentially stealthier attacks, in this paper, a cost-effective defense mechanism named Model Consistency-Based Defense (MCD) is proposed, which offers a comprehensive examination of available local models across multiple feature dimensions, providing an indirect yet effective means of identifying hidden data poisoning attackers. To push the limit of MCD against stealthier attacks, we propose a new GAN-based data poisoning attack model named VagueGAN and an unsupervised variant of it, which can be flexibly deployed to generate seemingly legitimate but noisy poisoned data. The consistency of GAN outputs revealed by VagueGAN helps strengthen MCD to work against stealthier GAN-based attacks as well as other mainstream ones. Extensive experiments on multiple open datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and Mini-Imagenet) indicate that our attack method better balances the trade-off between attack effectiveness and stealthiness with low complexity. More importantly, our defense mechanism is shown to be more competent in identifying a variety of poisoned data, particularly stealthier GAN-poisoned ones.

In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL.

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.

This paper investigates the active reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and power transfer (ISCPT) networks, where rate-splitting multiple access (RSMA) scheme is employed to serve multiple downlink communication users. To promote the energy efficiency (EE) of such a system, we formulate an EE maximization problem by jointly optimizing the beamforming matrix, the sensing matrix, the active RIS matrix, the power splitting (PS) ratio vector, and the common rate allocation vector. Due to the non-convexity of the problem, we first design a successive convex approximation scheme with alternating optimization method (named SCA-AO) to solve it. As SCA-AO operates in an iterative manner, which is with relatively high computational complexity, we then design a mixture of experts (MoE)-based deep reinforcement learning (DRL) scheme with smooth clipping function (named MoE-SCF). In comparison, SCA-AO is able to achieve higher solution accuracy, while MOE-SCF has a shorter online execution response time. In order to integrate the advantages of both presented SCA-AO and MoE-SCF simultaneously, we further propose a hybrid MoE (H-MoE) scheme, where both the SCA-AO and the MoE-SCF are employed as expert strategies, and an opportunistic activator (OPA) is designed to dynamically select the best strategy generated by all expert combinations according to the performance evaluation function. Simulation results demonstrate that the proposed H-MoE promotes the system's EE by about 18.14% compared to traditional MoE, with similar response time. Additionally, compared to the SCA-AO, H-MoE significantly decreases the response time by approximately 56.17%, while only marginally compromising the EE performance by less than 3.1%.

The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

Recent advancements in large-scale position-reconfigurable antennas have opened up new dimensions to effectively utilize the spatial degrees of freedom (DoFs) of wireless channels. However, the deployment of existing antenna placement schemes is primarily hindered by their limited scalability and frequently overlooked near-field effects in large-scale antenna systems. In this article, we propose a novel antenna placement approach tailored for near-field massive multiple-input multiple-output systems, which effectively exploits the spatial DoFs to enhance spectral efficiency. For that purpose, we first reformulate the antenna placement problem in the angular domain, resulting in a weighted Fekete problem. We then derive the optimality condition and reveal that the optimal antenna placement is in principle an electrostatic equilibrium problem. To further reduce the computational complexity of numerical optimization, we propose an ordinary differential equation (ODE)-based framework to efficiently solve the equilibrium problem. In particular, the optimal antenna positions are characterized by the roots of the polynomial solutions to specific ODEs in the normalized angular domain. By simply adopting a two-step eigenvalue decomposition (EVD) approach, the optimal antenna positions can be efficiently obtained. Furthermore, we perform an asymptotic analysis when the antenna size tends to infinity, which yields a closed-form solution. Simulation results demonstrate that the proposed scheme efficiently harnesses the spatial DoFs of near-field channels with prominent gains in spectral efficiency and maintains robustness against system parameter mismatches. In addition, the derived asymptotic closed-form solution closely approaches the theoretical optimum across a wide range of practical scenarios.

Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) are being explored for sixth-generation (6G) wireless networks. A promising configuration for their deployment is within cell-free massive multiple-input multiple-output (MIMO) systems. However, despite the advantages that STAR-RISs could bring, challenges such as electromagnetic interference (EMI) and phase errors may lead to significant performance degradation. In this paper, we investigate the impact of EMI and phase errors on STAR-RIS-assisted cell-free massive MIMO systems and propose techniques to mitigate these effects. We introduce a tailored projected gradient descent (GD) algorithm for STAR-RIS coefficient matrix design by minimizing the local channel estimation normalized mean square error (NMSE). We also derive the novel closed-form expressions of the uplink and downlink spectral efficiency (SE) to analyze system performance with EMI and phase errors, in which fractional power control methods are introduced for performance improvement. The results reveal that the projected GD algorithm can effectively tackle EMI and phase errors to improve estimation accuracy and compensate for performance degradation with nearly 30% NMSE improvement and over 10% SE improvement. Moreover, increasing the number of access points (APs), antennas per AP, and STAR-RIS elements can also improve SE performance. However, the advantages of employing STAR-RIS are reduced when EMI and phase errors are severe. Notably, compared to conventional RISs, the incorporation of STAR-RIS in the proposed system yields better performance and presents less performance degradation in highly impaired environments.

To support efficient and accurate multi-task inference in edge environments, we propose a task-oriented broadcast communication system that enables an edge transmitter to serve multiple edge devices with heterogeneous inference tasks. The proposed system adopts a two-phase design inspired by Marton’s channel coding with rate splitting and the information bottleneck principle. In the first phase, a common feature vector is extracted to capture the shared information across tasks. In the second phase, task-specific private feature vectors are generated conditioned on the common feature to preserve unique task-relevant information. To facilitate interference-robust task execution, our scheme leverages the intrinsic structural alignment between the task correlations and broadcast channel properties; specifically, the common and private features are mapped directly to Marton’s common and private codewords. A variational approximation method is introduced to optimize the feature extraction process in both phases, allowing for compact and informative representations while reducing redundant data transmission. Extensive experiments on a real-world multi-label dataset demonstrate that the proposed method achieves superior inference accuracy and robustness over wireless networks, compared to traditional digital compression and deep learning-based joint source-channel coding schemes. These results confirm the potential of task-oriented design for scalable and reliable edge intelligence.

Federated learning (FL) necessitates that edge devices conduct local training and communicate with a parameter server, resulting in significant energy consumption. A key challenge in practical FL systems is the rapid depletion of battery-limited edge devices, which limits their operational lifespan and impacts learning performance. To tackle this issue, we implement energy harvesting techniques in FL systems to capture ambient energy, thereby providing continuous power to edge devices. We first establish the convergence bound for the wireless FL system with energy harvesting devices, illustrating that the convergence is affected by partial device participation and packet drops, both of which depend on the energy supply. To accelerate the convergence, we formulate a joint device scheduling and power control problem and model it as a Markov decision process (MDP). By solving this MDP, we derive the optimal transmission policy and demonstrate that it possesses a monotone structure with respect to the battery and channel states. To overcome the curse of dimensionality caused by the exponential complexity of computing the optimal policy, we propose a low-complexity algorithm, which is asymptotically optimal as the number of devices increases. Furthermore, for unknown channels and harvested energy statistics, we develop a structure-enhanced deep reinforcement learning algorithm that leverages the monotone structure of the optimal policy to improve the training performance. Finally, extensive numerical experiments on real-world datasets are presented to validate the theoretical results and corroborate the effectiveness of the proposed algorithms.

Decentralized federated learning (DFL) enables edge devices to collaboratively train models through local training and fully decentralized device-to-device (D2D) model exchanges. However, these energy-intensive operations often rapidly deplete limited device batteries, reducing their operational lifetime and degrading the learning performance. To address this limitation, we apply energy harvesting technique to DFL systems, allowing edge devices to extract ambient energy and operate sustainably. We first derive the convergence bound for wireless DFL with energy harvesting, showing that the convergence is influenced by partial device participation and transmission packet drops, both of which further depend on the available energy supply. To accelerate convergence, we formulate a joint device scheduling and power control problem and model it as a multi-agent Markov decision process (MDP). Traditional MDP algorithms (e.g., value or policy iteration) require a centralized coordinator with access to all device states and exhibit exponential complexity in the number of devices, making them impractical for large-scale decentralized networks. To overcome these challenges, we propose a fully decentralized policy iteration algorithm that leverages only local state information from two-hop neighboring devices, thereby substantially reducing both communication overhead and computational complexity. We further provide a theoretical analysis showing that the proposed decentralized algorithm achieves asymptotic optimality. Finally, comprehensive numerical experiments on real-world datasets are conducted to validate the theoretical results and corroborate the effectiveness of the proposed algorithm.

Advancements in artificial intelligence and low-earth orbit satellites have promoted the application of large remote sensing foundation models (FMs) for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large FMs, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links. To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time to 33% of on-board training time.

Fluid antenna systems (FAS) offer enhanced spatial diversity for next-generation wireless systems. However, acquiring accurate channel state information (CSI) remains challenging due to the large number of reconfigurable ports and the limited availability of radio-frequency (RF) chains— particularly in high-dimensional FAS scenarios. To address this challenge, we propose an efficient posterior sampling-based channel estimator that leverages a diffusion model (DM) with a simplified U-Net architecture to capture the spatial correlation structure of two-dimensional FAS channels. The DM is initially trained offline in an unsupervised way and then applied online as a learned implicit prior to reconstruct CSI from partial observations via posterior sampling through a denoising diffusion restoration model (DDRM). To accelerate the online inference, we introduce a skipped sampling strategy that updates only a subset of latent variables during the sampling process, thereby reducing the computational cost with minimal accuracy degradation. Simulation results demonstrate that the proposed approach achieves significantly higher estimation accuracy and over 20× speedup compared to state-of-the-art compressed sensing-based methods, highlighting its potential for practical deployment in high-dimensional FAS.

Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing methods prioritize reducing this overhead during inference, often assuming feasible local training or minimal training communication resources. However, in real-world wireless systems with dynamic connection topologies, training models locally for each new connection is impractical, and task-specific information is often unavailable before establishing connections. Therefore, minimizing training overhead and enabling label-free, task-agnostic pre-training before the connection establishment are essential for effective task-oriented communication. In this paper, we tackle these challenges by employing a mutual information maximization approach grounded in self-supervised learning and information-theoretic analysis. We propose an efficient strategy that pre-trains the transmitter in a task-agnostic and label-free manner, followed by joint fine-tuning of both the transmitter and receiver in a task-specific, label-aware manner. Simulation results show that our proposed method reduces training communication overhead to about half that of full-supervised methods using the SGD optimizer, demonstrating significant improvements in training efficiency.

Large language models (LLMs) have achieved remarkable success across various artificial intelligence tasks. However, their enormous sizes and computational demands pose significant challenges for the deployment on edge devices. To address this issue, we present a distributed on-device LLM inference framework based on tensor parallelism, which partitions neural network tensors (e.g., weight matrices) of LLMs among multiple edge devices for collaborative inference. Nevertheless, tensor parallelism involves frequent all-reduce operations to aggregate intermediate layer outputs across participating devices during inference, resulting in substantial communication overhead. To mitigate this bottleneck, we propose an over-the-air computation method that leverages the analog superposition property of wireless multipleaccess channels to facilitate fast all-reduce operations. To minimize the average transmission mean-squared error, we investigate joint model assignment and transceiver optimization, which can be formulated as a mixed-timescale stochastic non-convex optimization problem. Then, we develop a mixed-timescale algorithm leveraging semidefinite relaxation and stochastic successive convex approximation methods. Comprehensive simulation results will show that the proposed approach significantly reduces inference latency while improving accuracy. This makes distributed ondevice LLM inference practical for resource-constrained edge devices.

Large language models (LLMs) have demonstrated remarkable success across various domains. However, the substantial computational and memory demands pose significant challenges for deploying LLMs at the network edge. To address this issue, the existing studies mainly focus on either reducing the size of LLMs or distributing LLMs across multiple devices. However, the former approach suffers from performance degradation, while the latter incurs high communication cost due to the transmission of high-dimensional intermediate features. To tackle these issues, we propose a novel collaborative framework to support vision-language model (VLM) inference at the network edge, expanding speculative decoding into a multi-device scenario. In this framework, each edge device first utilizes its small VLM to generate draft tokens in an auto-regressive manner. These tokens are then transmitted to an edge server, where they are corrected in parallel by a large VLM. By benefiting from speculative decoding, the number of calls to the large VLM is reduced without degrading the inference performance. Furthermore, we minimize the average latency of inference tasks by developing a deep reinforcement learning algorithm to optimize the number of draft tokens generated at each iteration. Simulation results confirm that the proposed algorithm achieves a lower average latency compared to other baselines.

Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental awareness for next-generation wireless networks. However, the narrow width of THz beams makes them prone to misalignment and necessitates frequent beam prediction in dynamic environments. Multimodal sensing, which integrates complementary modalities such as camera images, positional data, and radar measurements, has recently emerged as a promising solution for proactive beam prediction. Nevertheless, existing multimodal approaches typically employ static fusion architectures that cannot adjust to varying modality reliability and contributions, thereby degrading predictive performance and robustness. To address this challenge, we propose a novel and efficient multimodal mixtureof-experts (MoE) deep learning framework for proactive beam prediction in THz ISAC systems. The proposed multimodal MoE framework employs multiple modality-specific expert networks to extract representative features from individual sensing modalities, and dynamically fuses them using adaptive weights generated by a gating network according to the instantaneous reliability of each modality. Simulation results in realistic vehicle-to-infrastructure (V2I) scenarios demonstrate that the proposed MoE framework outperforms traditional static fusion methods and unimodal baselines in terms of prediction accuracy and adaptability, highlighting its potential in practical THz ISAC systems with ultra-massive multiple-input multiple-output (MIMO).

Orthogonal time frequency space (OTFS) modulation is a promising technology for mitigating severe Doppler effects in high-mobility scenarios. However, existing OTFS channel estimation methods neglect the Doppler Squint Effect (DSE), which incurs serious performance loss. In this paper, we propose a channel estimation algorithm based on Newton's method to accurately estimate fractional delay and Doppler in OTFS systems with DSE. In particular, we obtain the maximum delay and Doppler grid spacing for codebook design to guarantee the convergence of the algorithm. Additionally, we derive the Cramér-Rao lower bound (CRLB) for testing channel parameter estimation performance of our proposed algorithm. Simulation results demonstrate that our proposed algorithm outperforms the orthogonal matching pursuit (OMP) algorithm in terms of normalized mean square error (NMSE), surpassing the Newtonized OMP algorithm with traditional dictionary matrix and approaching the CRLB performance.

In the Internet of Vehicles (IoV), federated learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated self-supervised learning (FSSL) utilizes self-supervised learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to base station (BS), which poses a risk of privacy leakage. Simplified contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a deep reinforcement learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the central processing unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods.

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, cellular vehicle-to-everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultralow latency and high reliability in vehicle-to-vehicle (V2V) communication. This article proposes a method that integrates graph neural networks (GNNs) with deep reinforcement learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the graph sample and aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on vehicle-to-infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of Internet of Vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of reconfigurable intelligent surface (RIS), which provide alternative communication pathways to assist vehicle communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider an RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the deep deterministic policy gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the multiagent DDPG (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, twin delayed DDPG (TD3), and some typical stochastic schemes.

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collaborative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms.

Federated learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles’ local models instead of the local data. The gradients of vehicles’ local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error (QE), which further affects the model accuracy and training time. To do so, the total training time and QE become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this article, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.

Real-time computer vision (CV) is expected to play a vital role in virtual/augmented reality, factory automation, digital twin, and metaverse. To fully unlock its potential, latency- or freshness-constrained communications with a fidelity criterion has attracted considerable recent attention. One of its fundamental limits is the fidelity-timeliness tradeoff (FTT) that still remains open. In this paper, we investigate real-time CV oriented video streaming over AWGN and fading channels with fixed/variable-length lossy compression or even burst arrivals, in which reinforcement learning (RL) inspired intelligent cross-layer scheduling is adopted to minimize the distortion while satisfying a hard-delay or age-of-information (AoI) constraint. A low-complexity algorithm based on constrained Markov decision process (CMDP) and binary search is presented to compute the optimal FTT numerically. To shed more light on FTT, simple analytical upper and lower bounds are derived by leveraging the structural Markovian analysis of conceived bounding policies and saddle-point approximations. An asymptotic analysis will demonstrate that the gap between two bounds vanishes as the signal-to-noise-ratio (SNR) increases, thereby allowing the squeeze theorem to give an optimal but yet analytical FTT in the high-SNR regime. Finally, we find a distortion floor, below which the target AoI/latency becomes infinite, no matter how we increase the power supply.

Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, theunpredictabledistribution shifts between training and test data, includingdomain shiftandsemantic shift, can dramatically undermine the system performance. In order to tackle these challenges, it is crucial to ensure that the encoded features can generalize todomain-shifteddata and detectsemantic-shifteddata, while remaining compact for transmission. In this paper, we propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effectivedomain-shift generalizationand accuratesemantic-shift detectionwithout any knowledge of the test data during training. Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework fordomain-shiftgeneralization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature. Furthermore, we enhance the task-oriented communication with the label-dependent feature encoding approach forsemantic-shift detectionwhich achieves joint gains in IB optimization and detection performance. To avoid the intractable computation of the IB-based objective, we leverage variational approximation to derive a tractable upper bound for optimization. Extensive simulation results on image classification tasks demonstrate that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff.

Semantic communications have demonstrated exceptional performance across various tasks, yet they are susceptible to semantic impairments due to the inherent vulnerability of deep neural networks. This paper focuses on semantic impairments in images, particularly those stemming from adversarial perturbations. We introduce a novel metric for quantifying the level of semantic impairment and create a semantic impairment dataset. Furthermore, we propose a deep learning enabled semantic communication system for robust image transmission, termed as DeepSC-RI. The proposed system harnesses a multi-scale semantic extractor with a dual-branch design tailored for extracting semantics with varying granularity, thereby boosting the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics through self-attention score manipulations, while the coarse-grained branch adopts a hierarchical approach for progressively capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results highlight the superior performance of DeepSC-RI under diverse channel conditions, across various levels of semantic impairment intensity, and in multiple tasks.

Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This situation necessitates cross-model communication between different inference systems, enabling edge devices from one service provider to collaborate effectively with edge servers from another. Independent optimization of diverse edge systems often leads to incoherent feature spaces, which hinders the cross-model inference for existing task-oriented communication. To facilitate and achieve effective cross-model task-oriented communication, this study introduces a novel framework that utilizes shared anchor data across diverse systems. This approach addresses the challenge of feature alignment in both server-based and on-device scenarios. In particular, by leveraging the linear invariance of visual features, we propose efficient server-based feature alignment techniques to estimate linear transformations using encoded anchor data features. For on-device alignment, we exploit the angle-preserving nature of visual features and propose to encode relative representations with anchor data to streamline cross-model communication without additional alignment procedures during the inference. The experimental results on computer vision benchmarks demonstrate the superior performance of the proposed feature alignment approaches in cross-model task-oriented communications. The runtime and computation overhead analysis further confirm the effectiveness of the proposed feature alignment approaches in real-time applications.

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality.

Artificial intelligence (AI) is envisioned to be natively integrated into the sixth-generation (6G) mobile networks to support a diverse range of intelligent applications. Federated edge learning (FEEL) emerges as a vital enabler of this vision by leveraging the sensing, communication, and computation capabilities of geographically dispersed edge devices to collaboratively train AI models without sharing raw data. This article explores the pivotal role of FEEL in advancing both the “wireless for AI” and “AI for wireless” paradigms, thereby facilitating the realization of scalable, adaptive, and intelligent 6G networks. We begin with a comprehensive overview of learning architectures, models, and algorithms that form the foundations of FEEL. We, then, establish a novel task-oriented communication principle to examine key methodologies for deploying FEEL in dynamic and resource-constrained wireless environments, focusing on device scheduling, model compression, model aggregation, and resource allocation. Furthermore, we investigate the domain-specific optimizations of FEEL to facilitate its promising applications, ranging from wireless air-interface technologies to mobile and the Internet of Things (IoT) services. Finally, we highlight key future research directions for enhancing the design and impact of FEEL in 6G.

Semantic communications have emerged as a new communication paradigm that extracts and transmits meaningful information relevant to receiver tasks. The trendy semantic coding framework, namely, learning-based joint source-channel coding (JSCC), lies on data-driven principles, with its efficacy depending on the employed neural networks (NNs). This paper introduces a codebook-assisted semantic coding method to improve JSCC performance for image transmission. Notably, a well-constructed codebook is employed to map each source image into a codeword, which subsequently provides shared prior information to assist semantic coding with general NN architectures. The main novelty is two-fold. First, we propose a general semantic-aware codebook construction method based on weighted data-semantic distance. In the case where the semantic information is characterized by discrete labels, this method is refined by encapsulating the labels into codeword indexes. Second, we derive a novel information-theoretic loss function via variational approximation for end-to-end training of the semantic encoder and decoder. This loss function includes a penalty term to mitigate redundancy in the received signals concerning codewords. Extensive experiments conducted over both additive noisy channels and fading channels validate the superior performance of the proposed method with even small-sized codebooks in both image reconstruction and classification accuracy.

Low-latency communication plays an increasingly important role in delay-sensitive applications by ensuring the real-time information exchange. However, due to the constraint on the maximum instantaneous power, guaranteeing bounded latency is challenging. In this paper, we investigate the reliability-latency-rate tradeoff in low-latency communication systems with finite-blocklength coding (FBC). Specifically, we are interested in the fundamental tradeoff between error probability, delay-violation probability (DVP), and service rate. Based on the effective capacity (EC), we present the gain-conservation equations to characterize the reliability-latency-rate tradeoffs in low-latency communication systems. In particular, we investigate the low-latency transmissions over an additive white Gaussian noise (AWGN) channel and a Nakagami-$m$fading channel. By defining the service rate gain, reliability gain, and real-time gain, we conduct an asymptotic analysis to reveal the fundamental reliability-latency-rate tradeoff of ultra-reliable and low-latency communications in the high signal-to-noise-ratio (SNR) regime. To analytically evaluate and optimize the quality-of-service-constrained throughput of low-latency communication systems adopting FBC, an EC-approximation method is conceived to derive the closed-form expression of that throughput. Our results may offer some insights into the efficient scheduling of low-latency wireless communications, in which statistical latency and reliability metrics are crucial.

The classical Rician Weichselberger channel and the emerging holographic multiple-input multiple-output (MIMO) channel share a common characteristic of non-separable correlation, which captures the interdependence between transmit and receive antennas. However, this correlation structure makes it very challenging to characterize the fundamental limits of non-centered (Rician), non-separable MIMO channels. In fact, there is a dearth of existing literature that addresses this specific aspect, underscoring the need for further research in this area. In this paper, we investigate the mutual information (MI) of non-centered non-separable MIMO channels, where both the line-of-sight and non-line-of-sight components are considered. By utilizing random matrix theory (RMT), we set up a central limit theorem for the MI and give the closed-form expressions for its mean and variance. The derived results are then utilized to determine the ergodic MI and outage probability of holographic MIMO channels. Numerical simulations validate the accuracy of the theoretical results.

Cell-free massive MIMO is one of the core technologies for next-generation wireless networks. It is expected to bring enormous benefits, including ultra-high reliability, data throughput, energy efficiency, and uniform coverage. However, the radically distributed architecture of cell-free massive MIMO necessitates new paradigms for transceiver design, especially by exploiting efficient distributed processing algorithms. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO, which consists of two modules: a nonlinear module at the central processing unit (CPU) and a linear module at each access point (AP). The turbo principle in iterative channel decoding is utilized to compute and pass the extrinsic information between the two modules. An analytical framework is provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Furthermore, a distributed iterative channel estimation and data detection (ICD) algorithm is developed to handle the practical scenario with imperfect channel state information (CSI). Simulation results will show that the proposed method outperforms existing detectors for cell-free massive MIMO systems in terms of the bit-error rate and the developed theoretical analysis can be utilized as an asymptotic lower bound. Finally, it is shown that with imperfect CSI, the proposed ICD algorithm can significantly improve the system performance and reduce the pilot overhead.

Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.

Multiuser multicarrier communication, e.g. orthogonal frequency division multi-access (OFDMA), has been extensively investigated since the 4G era and applied in several mainstream mobile networking standards, because it holds the potential of high-throughput provision, low complexity, and flexible bandwidth allocation. In the upcoming 6G era, mobile networks are newly expected to provide deadline or hard-delay assurance for latency-sensitive traffics generated from factory automation, smart grids, telesurgery, and automatic driving, etc. However, whether the emerging hard-delay constraint can be effectively satisfied in multiuser multicarrier systems, where subcarriers are shared by users, remains open. As a result, a unified framework for realtime multiuser multicarrier communications is presented in this paper, based on the bipartite-graph model of OFDMA. In particular, we conceive a$\mathcal {H}$-matching empowered joint subcarrier allocation and power adaptation strategy, which is shown to meet the deadline requirements deterministically over frequency-selective channels with finite average transmission power. Furthermore, we leverage the theory of random bipartite graph matching to analyze the delay-constrained capacity as a function of the average transmission power, based on the approximate outage probability of the embedded matching diversity. To gain more insights, asymptotic analysis is adopted to obtain the deadline-constrained throughput when the number of independent subcarriers is huge.

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for downlink channel state information (CSI). To address this challenge, state-of-the-art (SOTA) fully data-driven deep learning (DL)-based CSI feedback schemes have been proposed. However, the high computational complexity and memory requirements of these methods hinder their practical deployment on resource-constrained devices like mobile phones. To solve the problem, we propose a model-driven DL-based CSI feedback approach by integrating the wisdom of compressive sensing and learning to optimize (L2O). Specifically, only a linear learnable projection is adopted at the encoder side to compress the CSI matrix, thereby significantly cutting down the user-side complexity and memory expenditure. On the other hand, the decoder incorporates two specially designed components, i.e., a learnable sparse transformation and an element-wise L2O reconstruction module. The former is developed to learn a sparse basis for CSI within the angular domain, which explores channel sparsity effectively. The latter shares the same long short term memory (LSTM) network across all elements of the optimization variable, eliminating the retraining cost when problem scale changes. Simulation results show that the proposed method achieves a comparable performance with the SOTA CSI feedback scheme but with much-reduced complexity, and enables multiple-rate feedback.

Pre-trained foundation models (FMs), with extensive number of neurons, are key to advancing next-generation intelligence services, where personalizing these models requires massive amount of task-specific data and computational resources. The prevalent solution involves centralized processing at the edge server, which, however, raises privacy concerns due to the transmission of raw data. Instead, federated fine-tuning (FedFT) is an emerging privacy-preserving fine-tuning (FT) paradigm for personalized pre-trained foundation models. In particular, by integrating low-rank adaptation (LoRA) with federated learning (FL), federated LoRA enables the collaborative FT of a global model with edge devices, achieving comparable learning performance to full FT while training fewer parameters over distributed data and preserving raw data privacy. However, the limited radio resources and computation capabilities of edge devices pose significant challenges for deploying 3 LoRA over wireless networks. To this paper, we propose a split federated LoRA framework, which deploys the computationally-intensive encoder of a pre-trained model at the edge server, while keeping the embedding and task modules at the edge devices. The information exchanges between these modules occur over wireless networks. Building on this split framework, the paper provides a rigorous analysis of the upper bound of the convergence gap for the wireless federated LoRA system. This analysis reveals the weighted impact of the number of edge devices participating in FedFT over all rounds, motivating the formulation of a long-term upper bound minimization problem. To address the long-term constraint, we decompose the formulated long-term mixed-integer programming (MIP) problem into sequential sub-problems using the Lyapunov technique. We then develop an online algorithm for effective device scheduling and bandwidth allocation. Simulation results demonstrate the effectiveness of the proposed online algorithm in enhancing learning performance.

In the typical radio frequency (RF)-based wireless power transfer (WPT) system, the wireless power station (WPS) connected to the grid transmits energy to charge low-power sensors via radio signals. Such a system may not be green and also difficult to deploy in some special areas including deserts and mountainous areas, because it depends on the grid. To achieve a green RF WPT system design, this paper considers that the WPS is powered by natural energy sources rather than the grid. To explore the maximal total amount of the energy that can be harvested by the sensors, we focus on the offline setting, so similar to many existing works on offline optimization, we assume that the WPS knows prior knowledge about energy arrivals and channel changes, and then formulate an optimization problem to maximize the total harvested energy via optimizing the WPS’s time-domain transmit power subject to multiple constraints, including the finite battery capacity at the WPS, the causal relationship between the natural energy harvesting and the WPT, and the transmit power budget of the WPS, where for practicality, the nonlinear energy harvesting (EH) model is also taken into account. To solve this non-convex problem, we first equivalently transform it by using the epigraph reformulation and the variable substitution, and then use the first-order Taylor expansion to get an approximate convex version. Then, we present a successive convex approximation (SCA)-based algorithm to improve the accuracy of the obtained solution for approaching the optimal one. For the special case with a single sensor, we further propose a branch and bound (BB)-based algorithm that is able to get a more accurate solution with lower complexity than the SCA-based one. Numerical results demonstrate that the proposed algorithms are able to achieve the near-global optimal solution. As the average recharge rate increases, compared with the other two baselines, i.e., the greedy power (GP) policy and the constant power (CP) policy, the total harvested energy achieved by the SCA-based algorithm is up to about 2.48 times and 1.37 times that of the baselines respectively. For the single-sensor case, the BB-based algorithm always outperforms the SCA-based one in terms of the total harvested energy while reducing the running time required for solving by about 90% on average.

This study addresses the core challenges in federated learning (FL), namely achieving optimal model utility, safeguarding local data privacy, and maintaining efficient communication. While previous research has focused on either the privacy-utility or communication-utility trade-offs, the investigation of simultaneously considering utility, privacy protection, and communication efficiency has been largely overlooked. In this paper, we propose a novel training framework for FL that combines communication efficiency and differential privacy. Specifically, we employ quantization and binomial noise on model updates to enhance privacy protection and communication efficiency concurrently. Through convergence and privacy analysis, we formulate an optimization problem that maximizes model utility while adhering to privacy and communication constraints. Additionally, we introduce an adaptive algorithm to determine key system parameters, including the level of quantization and privacy noise. Simulation results validate the effectiveness of our proposed FL framework and parameter optimization algorithm.

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity.

Low-rank adaptation (LoRA) is an emerging fine-tuning method for personalized large language models (LLMs) due to its capability of achieving comparable learning performance to full fine-tuning by training a much smaller number of parameters. Federated fine-tuning (FedFT) combines LoRA with federated learning (FL) to enable collaborative fine-tuning of a global model with edge devices, leveraging distributed data while ensuring privacy. However, limited radio resources and computation capabilities of edge devices pose critical challenges on deploying FedFT over wireless networks. In this paper, we propose a split FedFT framework to separately deploy the computationally-intensive encoder of a pre-trained model at the edge server while reserving the embedding and the task modules at the edge devices, where the information exchanges between these modules are carried out over wireless networks. By exploiting the low-rank property of LoRA, the proposed FedFT framework reduces communication overhead by aggregating the gradient of the task module with respect to the output of a low-rank matrix. To enhance learning performance under stringent resource constraints, we formulate a joint device scheduling and bandwidth allocation problem while considering average transmission delay. By applying the Lyapunov technique, we decompose the formulated long-term mixed-integer programming (MIP) problem into sequential subproblems, followed by developing an online algorithm for effective device scheduling and bandwidth allocation. Simulation results demonstrate the effectiveness of our proposed online algorithm in enhancing learning performance.

Real-time wireless communication techniques are expected to play a vital role in emerging Time-Sensitive Networking, deterministic networking, and tactile internet. Although there has been much work on the optimal cross-layer scheduling with discrete-state fading models, how to minimize the average power given both throughput and deadline constraints over wireless channels with continuous fading states remains open. In this paper, the optimal joint channel and queue-aware scheduling policy for continuous fading channels is presented based on a joint quantization and fitting approach. In particular, by quantifying both channel and queue states of the fluid-model-based communications over continuous fading channels, we formulate its approximate Markov model, the optimal scheduling policy of which is then revealed by the Constrained Markov Decision Process (CMDP). As our previous work showed the above discretized policy has a threshold-based structure, it can be easily approximated by a deterministic scheduling policy. As a result, we leverage the fitting approach to find the asymptotically optimal deterministic scheduling. The deadline-constrained capacity, as a function of both the average power and maximum delay, is also determined. Numerical results validate the effectiveness of our analysis.

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.

The rapid proliferation of artificial intelligence (AI) services gives rise to the development of federated learning (FL), enabling the cooperative learning among wireless devices (WDs) with only local model parameters communicated. Nevertheless, the current emergence of large AI models renders the existing FL approaches inefficient, due to the huge communication overhead. In this paper, we propose a novel over-the-air federated distillation (FD) framework by synergizing the strength of FL and knowledge distillation to avoid the heavy local model transmission. Instead of sharing model parameters, only WDs' model outputs, referred to as knowledge, are shared and aggregated over-the-air by exploiting the superposition property of the multiple-access channel. Accordingly, we study the communication-learning co-design in over-the-air FD, aiming to maximize the learning convergence rate while meeting the power constraints of the transceivers. The main challenge lies in the intractability of the learning performance analysis, as well as the non-convex nature and the optimization spanning the whole FD training period. To tackle this problem, we propose an efficient algorithm to jointly optimize the transmit power of the WDs, estimator for over-the-air aggregation, and receiver beamforming per training round. Numerical results demonstrate that the proposed over-the-air FD achieves significant communication overhead reduction, with only a slight compensation of testing accuracy compared to conventional FL benchmarks.

To meet the stringent requirements of future communication systems, ultra-massive multiple-input and multiple-output (UM-MIMO) technology has garnered significant attention as a key enabling technology for 6G. However, the deployment of UM-MIMO introduces new challenges, particularly the near-field effect. In this paper, by leveraging the unique characteristics of near-field channels, we propose a novel near-field channel estimation algorithm based on the Newton's method. We also design a near-field codebook that meets the requirements for convergence guarantee. Our algorithm overcomes the limitations of existing approaches by offering a low-complexity, tuning-free, and convergence-guaranteed solution. Simulation results show that our proposed algorithm outperforms state-of-the-art baselines in terms of estimation accuracy, establishing its effectiveness in near-field channel estimation for UM-MIMO systems.

This paper considers cell-free multiple-input multiple-output (MIMO) systems with multi-antenna access points (APs) and multi-antenna users in constrained spaces. Two main effects emerge in such a space-constrained design: spatial correlation and mutual coupling. We analytically study the resulting performance with conjugate beamforming and reducing inter-antenna distance. We derive the closed-form expression for the spectral efficiency (SE) as a function of the spatial correlation and mutual coupling matrices. Additionally, we consider a practical power consumption model to investigate the energy efficiency (EE) with power control coefficients. The obtained ergodic and analytical results will show essential insights into the system performance. The decreasing inter-antenna distance introduces performance loss, while an appropriate number of APs could balance the SE and EE trade-off. The number of users improves the SE performance, while an optimal number of antennas per user can be selected to achieve maximum SE performance. Moreover, more users will introduce a smaller optimal number of antennas per user.

Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconceptualize the FL system from the perspective of network information theory, and formulate an original FL communication framework, FedNC, which is inspired by Network Coding (NC). The main idea of FedNC is mixing the information of the local models by making random linear combinations of the original parameters, before uploading for further aggregation. Due to the benefits of the coding scheme, both theoretical and experimental analysis indicate that FedNC improves the performance of traditional FL in several important ways, including security, efficiency, and robustness. To the best of our knowledge, this is the first framework where NC is introduced in FL. As FL continues to evolve within practical network frameworks, more variants can be further designed based on FedNC.

This article investigates rate-splitting multiple access (RSMA)-based visible light communication (VLC) networks with simultaneous lightwave information and power transfer (SLIPT). To effectively enhance the fairness among information decoding users (IDUs), we formulate an optimization problem to maximize the minimum data rate by optimizing the direct current bias vector, the common message rates of RSMA, and the transmit precoding vectors. In the problem, the IDUs’ minimum energy harvesting (EH) requirements, the total power budget of the light-emitting diode (LED) transmitters, and the linear operation region of LEDs are also considered as the system constrains. To solve the formulated nonconvex problem, epigraph reformulation is first employed to transform the nonconvex objective function. Then, a series of transformations is proposed and the semi-definite relaxation (SDR) method is adopted to address the rank-one precoding matrix constraint. After that, an iterative algorithm is proposed to obtain an effective suboptimal solution by applying the successive convex approximation. Extensive simulations show that the max–min rate (MMR) is inversely proportional to the number of IDUs and it decreases as the minimum EH requirement becomes more stringent, especially in the high-EH region. Moreover, the value of the maximum drive current imposes a significant impact on the system performance, particularly, the MMR becomes saturated for a given maximum drive current even if the total power budget is sufficient. Besides, RSMA can contribute to both spectral efficiency and energy efficiency greatly in comparison to the traditional multiple access scheme.

This article investigates the age of information (AoI) performance in a wireless powered communication network (WPCN), where a sensor node (SN) harvests energy from an energy transmitter (ET) and then transmits status information to its data receiver (DR) by using the harvested and accumulated energy. The AoI penalty is used as a performance metric to characterize the nonlinear feature of dissatisfaction with data obsolescence at the DR. We derive the closed-form expressions of the average AoI penalty and the average peak AoI (PAoI) penalty with the Rician fading model. To further explore the system performance limit in terms of AoI penalty, we formulate two optimization problems to minimize the average AoI penalty and the average PAoI penalty with respect to the battery discharge threshold. Particularly, we reveal the conditions for the existence of the average AoI penalty and average PAoI penalty. Simulation results show that there exists a unique optimal battery discharge threshold that minimizes the system’s average AoI penalty and a unique optimal battery discharge threshold that minimizes the system’s average PAoI penalty. Moreover, as expected, the average AoI penalty and the average PAoI penalty decrease with the increment of the Rician$K$-factor, and increase with the increment of the distance between ET and SN. Besides, the average AoI penalty and the average PAoI penalty first decrease with the increment of transmit power of ET and then tend to be flat. Additionally, a smaller data size of SN yields better system performance.

Hard-delay constrained wireless communication has attracted considerable recent attention because it holds the promise of playing a vital role in wireless Internet of Things (IoT) systems for supporting time-sensitive tasks. Hard-delay constrained communication refers to the case in which the delay-violation probability of packets is zero with a given deadline. How to strike the optimal power-latency-throughput tradeoff with a hard-delay constraint remains open. In this paper, we aim to characterize the hard-delay constrained capacity with a deadline which covers one or multiple coherence time of the fading channel. Specifically, we first conceive a low-complexity and yet sub-optimal cross-layer scheduling scheme to obtain an analytical bound of the hard-delay constrained capacity. Saddlepoint approximation is adopted to obtain its hard-delay constrained throughput as a function of the tolerated delay and average power, which provides us with properties of the achievable bound of the hard-delay constrained capacity. Further, we investigate the optimal joint channel and queue-aware scheduling to derive the hard-delay constrained capacity. In particular, by quantifying channel and queue states and recalling the optimality of threshold-based policies, we approximate the optimal hard-delay constrained scheduling by a deterministic policy obtained based on the quantized Constrained Markov Decision Process. More structures of this optimal scheduling policy are revealed through curve fitting by dividing system states into distinct regions. Finally, the hard-delay constrained capacity is presented as a function of both the average power and maximum delay, with the assistance of both theoretical analysis from the achievable bound and curve fitting.

This article studies an intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) network, where the ground users (GUs) desire to receive information from a UAV. Downlink nonorthogonal multiple access (NOMA) is considered typically with two GUs being selected according to whether a Line-of-Sight (LoS) link between GUs and UAV exists. As the accurate channel information of LoS or Non-LoS (NLoS) links for multiple GUs is difficult to acquire, an approximate LoS region-based method is designed to select GUs as an alternative. In order to enhance the communication quality of the far GU, an IRS is deployed to assist the NLoS transmission. For such a system, we evaluate its outage performance in Nakagami-m fading. First, the central limit theorem (CLT) and Laplace transform (LT) are employed to derive the channel statistics of the UAV- IRS-user link. Then, asymptotic closed-form expressions of the outage probabilities are derived for the selected GUs based on Gaussian–Chebyshev quadrature approximation. Monte Carlo simulations validate the validness of our derived outage probabilities. It shows that the approximate LoS region-based scheme provides similar outage performance laws as the accurate LoS region-based one. Moreover, the outage probabilities of selected GUs in terms of NOMA-based protocol and orthogonal multiple access (OMA)-based protocol are analyzed. Simulation results confirm that the proposed NOMA-based protocol is capable of achieving superior performance compared with the OMA-based protocol by setting power allocation factor and targeted acrlong SINR thresholds of near GU and far GU properly. Specifically, when the rate threshold of near GU is relatively large or the rate threshold of far GU is relatively small, the outage performance derived by NOMA-based protocol performs better than OMA-based protocol in most of cases.

Federated learning (FL) is a promising paradigm for privacy-preserving deep learning using data distributed on Internet of Things devices. Traditional model sharing-based methods, e.g., federated averaging (FedAvg), suffer from high communication overhead and difficulty in accommodating heterogeneous model architectures. Federated distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, as well as heterogeneous client models. However, there is a lack of theoretical understanding of FD-based methods, and their design guidelines remain elusive. This article presents a generic meta-algorithm for FD, generalizing most existing FD training algorithms. By studying a linear classification problem, we show that, with sufficient distillation samples, the training performance of the meta-algorithm is the same as the vanilla FedAvg. To guide the algorithm design and improve communication efficiency, we further investigate the binary classification problem with a Gaussian mixture model, which shows that more distillation data and sampling data with higher confidence improve the training performance. Furthermore, we propose an effective distillation data sampling technique to improve the performance of the FD-meta algorithm, which also reduces communication overhead. Simulations on the benchmark data sets validate the theoretical findings and demonstrate that our proposed algorithm effectively reduces the communication overhead while achieving a satisfactory performance.

This article investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted downlink multiuser multiple-input–single-output (MU-MISO) networks with the rate splitting multiple access (RSMA) scheme. A base station (BS) desires to simultaneously transmit messages to multiple users with the assistance of an STAR-RIS to enhance communication quality as well as extend the coverage of users. An optimization problem is formulated to maximize the achievable sum rate of the networks on the premise of satisfying the constraints on power budget at the BS, total common-stream rate of users, and individual users’ minimum rate requirements, via jointly optimizing the beamforming vectors, the common-stream rate allocation vector, and the transmission and reflection coefficients (TARCs) matrix. Due to the dynamic changes of communication links and the coupling of multiple variables, it is challenging to solve such a nonconvex optimization problem by utilizing traditional methods. Therefore, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are designed properly. A constraint-satisfaction-processing (CSP) method is employed to further adjust the optimized transmit power to make sure that the obtained optimized results satisfy the power budget constraint. Simulation results show that the proposed PPO-based DRL algorithm converges well and achieves much better performance than several baselines, such as the soft actor–critic (SAC), the deep deterministic policy gradient (DDPG), the genetic algorithm (GA), the maximum ratio transmission (MRT), the zero-forcing (ZF), and the random methods. Moreover, it demonstrates that deploying STAR-RIS greatly enhances the system sum rate and user fairness compared to deploying traditional reflecting-only RIS (RO-RIS) and without RIS. Besides, it also shows that adopting the RSMA scheme achieves more notable performance gains than the nonorthogonal multiple access (NOMA) scheme in such a network.

This article investigates semantic communication in high-speed mobile Internet of Vehicles (IoV), focusing on spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We propose a semantic-aware spectrum-sharing (SSS) algorithm using deep reinforcement learning (DRL) with a soft actor-critic (SAC) approach. We start with semantic information extraction, redefining metrics for V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). We then apply the SAC algorithm to optimize decisions V2V and V2I spectrum-sharing decisions on semantic information. This optimization aims to maximize HSSE and enhance the success rate of effective semantic information transmission (SRS), including determining the optimal V2V and V2I sharing strategies, transmission power, and the length of transmitted semantic symbols. Experimental results show that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum-sharing algorithms and spectrum-sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

Federated Learning (FL) is a promising distributed learning mechanism that revolutionizes our interaction with data in the IoT ecosystem. Due to the rapidly growing scale of smart devices and the limited transmission resources of networks, a simple, consistent and scalable FL framework aiming to address the communication bottleneck is urgently needed. In this work, we propose an efficient approach with Selective Aggregation of Models (SAM) to mitigate the communication overload in FL systems. The introduction of SAM enables each local client to upload its model with a certain probability, resulting in a significant reduction in costly communication expenses. We design the algorithm for SAM, analyze the convergence bound on non-convex objectives for heterogeneous data, which illustrates the impact of the selection probability as well as the set size of participating clients on the system performance, and assess the conservation for the network resource utilization by modeling queuing systems. We conduct various experiments to evaluate the performance of SAM, whose outcomes suggest that significant alleviation of the communication bottleneck can be accomplished with marginal cost of performance loss. It will also be shown that SAM is a communication-efficient method that can be freely applied to other frameworks.

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from “compute-after-communicate” to “compute-when-communicate”. By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.

As a promising learning paradigm integrating computation and communication, federated learning (FL) proceeds the local training and the periodic sharing from distributed clients. Due to the non-i.i.d. data distribution on clients, FL model suffers from the gradient diversity, poor performance, bad convergence, etc. In this work, we aim to tackle this key issue by adopting importance sampling (IS) for local training. We propose importance sampling federated learning (ISFL), an explicit framework with theoretical guarantees. Firstly, we derive the convergence theorem of ISFL to involve the effects of local importance sampling. Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions. We also employ a water-filling method to calculate the IS weights and develop the ISFL algorithms. The experimental results on CIFAR-10 fit the proposed theorems well and verify that ISFL reaps better performance, convergence, sampling efficiency, as well as explainability on non-i.i.d. data. To the best of our knowledge, ISFL is the first non-i.i.d. FL solution from the local sampling aspect which exhibits theoretical compatibility with neural network models. Furthermore, as a local sampling approach, ISFL can be easily migrated into other emerging FL frameworks.

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (AirComp), which is proposed to reduce the communication overhead for FL over wireless networks at the cost of compromising in the learning performance due to model aggregation error arising from channel fading and noise. We first provide a comprehensive study on the convergence of AirComp-based FEDAVG (AIRFEDAVG) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity. Through convergence and asymptotic analysis, we characterize the impact of aggregation error on the convergence bound and provide insights for system design with convergence guarantees. Then we derive convergence rates for AIRFEDAVG algorithms for strongly convex and non-convex objectives. For different types of local updates that can be transmitted by edge devices (i.e., local model, gradient, and model difference), we reveal that transmitting local model in AIRFEDAVG may cause divergence in the training procedure. In addition, we consider more practical signal processing schemes to improve the communication efficiency and further extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes. Extensive simulation results under different settings of objective functions, transmitted local information, and communication schemes verify the theoretical conclusions.

Sensing and edge artificial intelligence (AI) are two key features of the sixth-generation (6G) mobile networks. Their natural integration, termed Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging Internet-of-Ting (IoT) applications. To achieve a high sensing accuracy, features of multiple sensor views are uploaded to an edge server for aggregation and inference using a large-scale AI model. The view aggregation is realized efficiently using over-the-air computing (AirComp), which also aggregates channels to suppress channel noise. As ISEA is at its nascent stage, there still lacks an analytical framework for quantifying the fundamental performance gains from view-and-channel aggregation, which motivates this work. Our framework is based on a well-established distribution model of multi-view sensing data where the classic Gaussian-mixture model is modified by adding sub-spaces matrices to represent individual sensor observation perspectives. Based on the model and linear classification, we study the End-to-End sensing (inference) uncertainty, a popular measure of inference accuracy, of the said ISEA system by a novel, tractable approach involving designing a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. As a result, we prove that the E2E sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, where the rate is proportional to global discriminant gain. Given AirComp and channel distortion, we further show that the exponential scaling remains but the rate is reduced by a linear factor representing the channel induced discriminant loss. Furthermore, in the case of many spatial degrees of freedom, we benchmark AirComp against equally fast, traditional analog orthogonal access. The comparative performance analysis reveals a sensing-accuracy crossing point between the schemes corresponding to equal receive array size and sensor number. This leads to the proposal of a scheme for adaptive access-mode switching to enhance ISEA performance. Last, the insights from our framework are validated by experiments using a convolutional neural network model and real-world dataset.

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency (EE), ultralow latency, and ultrahigh reliability. Cell-free (CF) massive multiple-input-multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this article, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments (HIs) and electromagnetic interference (EMI). We summarize the corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as millimeter wave (mmWave) and terahertz (THz), simultaneous wireless information and power transfer (SWIPT), next-generation multiple access (NGMA), and unmanned aerial vehicle (UAV). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.

Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel form of generative adversarial network (GAN) using message importance measure (MIM) as the information distance, into mutual information estimation, and develop a novel method, named MIM-based mutual information estimator (MMIE). Then, we design a generalized cooperative framework for channel capacity learning, in which a generator is regarded as an encoder producing the channel input, while a discriminator is the mutual information estimator that assesses the performance of the generator. Through the adversarial training, the generator automatically learns the optimal codebook and the discriminator estimates the channel capacity. Numerical experiments will demonstrate that compared with several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability.

This paper studies the high-speed railway mobile networks (HSRMN), where multiple railway-side sensors (RSs) are deployed along the track to sense environmental data, and multiple train-mounted sensors (TSs) are deployed on the train to collect train data. Both RSs and TSs are scheduled to transmit their sensed data respectively to the ground base station (BS) in a time division multiple access (TDMA) mode. To keep the data received at the BS from the RSs as fresh as possible and also ensure that the TSs complete the given uploading tasks, an optimization problem is established to minimize the average age of information (AoI) of the data gathered from RSs by jointly optimizing sensors’ scheduling and transmission power control constrained by the maximum transmission power budget of RSs and TSs. Since the problem is non-convex and lacks an explicit expression of the objective function and the prior information about future channel state, we present a deep Q-learning network (DQN)-based method to solve it. Particularly, the BS is viewed as the agent, and the action space is constructed by scheduling policy and power control. To further accelerate the convergence speed of the presented DQN-based solution framework, an action space-reduced (ASR) version of the DQN-based method, i.e., the ASR-DQN-based method, is designed by deriving a closed-form solution to the optimal transmission power for a given sensors’ scheduling policy. Numerical simulations show that, compared to the DQN-based method, the ASR-DQN-based method decreases the number of episodes required for convergence by about 23% and reduces the running time by about 41%. Moreover, compared with three baselines, i.e., the random method, the round-robin method, and the deep-Sarsa method, our presented ASR-DQN-based method achieves the lowest average AoI and has the best robustness among these compared methods.

This paper investigates the radio-frequency-energy-harvesting-powered (RF-EH-powered) wireless Industrial Internet of Things (IIoT) networks, where multiple sensor nodes (SNs) are first powered by a wireless power station (WPS), and then collect status updates from the industrial environment and finally transmit the collected data to the monitor with their harvested energy. To enhance the timeliness of data, age of information (AoI) is used as a metric to optimize the system. Particularly, an expected sum AoI (ESA) minimization problem is formulated by optimizing the power adjustment policy for the SNs under multiple practical constraints, including the EH, the minimal signal-to-noise-plus-interference ratio (SINR) and the battery capacity constraints. To solve the non-convex problem with no explicit AoI expression, we transform it into a Markov decision problem (MDP) with continuous state space and action space. Then, inspired by the Soft Actor-Critic (SAC) framework in deep reinforcement learning, a SAC-based age-aware power adjustment (SAPA) method is proposed by modeling the power adjustment as a stochastic strategy. Furthermore, to reduce the communication overhead of SAPA, a multi-agent version of SAPA, i.e., MSAPA, is proposed, with which each SN is able to adjust its transmit power based on its local observations. The communication overhead of SAPA and MSAPA is also analyzed theoretically. Simulation results show that the proposed SAPA and MSAPA converge well with different numbers of SNs. It is also shown that the ESA achieved by the proposed SAPA and MSAPA is lower than that achieved by the baseline methods.

This paper investigates the energy-efficient coordinated beamforming design for multi-pair multiple-input single-output (MISO) networks with passive eavesdroppers. To be practical, it is assumed that only channel distribution information (CDI) of the network is known by the transmitters/sources, and the dynamic energy consumption model (DECM) is employed. In order to achieve a green network design, an energy efficiency (EE) maximization problem is formulated subjecting to the individual available power constraints, the rate outage probability constraints, and the information leakage probability constraints. To solve the formulated non-convex problem, semidefinite relaxation (SDR) and first-order lower bound are applied to transform the problem, and then an efficient algorithm is proposed based on successive convex approximation (SCA) and Dinkelbach's approaches. The proposed algorithm is theoretically proved to converge to a stationary point of the considered problem. Further, a distributed version of the proposed algorithm is designed, with which each transmitter is able to optimize its own beamforming vector with local CDI. Moreover, the computational complexities and the signaling overheads of the two developed algorithms are analyzed and compared. Simulation results show that both algorithms achieve good EE performance, and the EE performance achieved by the distributed algorithm is very similar to that achieved by the centralized one. Additionally, it is shown that similar to the conventional scenarios without eavesdroppers, the achieved system EE also has a saturation point w.r.t. the available power of the transmitters, and by employing our proposed algorithms, the network security is significantly enhanced.

Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In this paper, we fill this gap by investigating a cooperative resource allocation problem with unknown system dynamics of MRPs. This problem can be modeled as a multi-agent online RMAB problem, where multiple agents collaboratively learn the system dynamics while maximizing their accumulated rewards. We devise a federated online RMAB framework to mitigate the communication overhead and data privacy issue by adopting the federated learning paradigm. Based on this framework, we put forth a Federated Thompson Sampling-enabled Whittle Index (FedTSWI) algorithm to solve this multi-agent online RMAB problem. The FedTSWI algorithm enjoys a high communication and computation efficiency, and a privacy guarantee. Moreover, we derive a regret upper bound for the FedTSWI algorithm. Finally, we demonstrate the effectiveness of the proposed algorithm on the case of online multi-user multi-channel access. Numerical results show that the proposed algorithm achieves a fast convergence rate of$\mathcal {O}(\sqrt{T\log (T)})$and better performance compared with baselines. More importantly, its sample complexity reduces sublinearly with the number of agents.

Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users’ requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users’ personal information. Traditional federated learning (FL) can protect users’ privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then a popular content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.

Vehicles in platoons need to process many tasks to support various real-time vehicular applications. When a task arrives at a vehicle, the vehicle may not process the task due to its limited computation resource. In this case, it usually requests to offload the task to other vehicles in the platoon for processing. However, when the computation resources of all the vehicles in the platoon are insufficient, the task cannot be processed in time through offloading to the other vehicles in the platoon. Vehicular fog computing (VFC)-assisted platoon can solve this problem through offloading the task to the VFC which is formed by the vehicles driving near the platoon. Offloading delay is an important performance metric, which is impacted by both the offloading strategy for deciding where the task is offloaded and the number of the allocated vehicles in VFC to process the task. Thus, it is critical to propose an offloading strategy to minimize the offloading delay. In the VFC-assisted platoon system, vehicles usually adopt the IEEE 802.11p distributed coordination function (DCF) mechanism while having various computation resources. Moreover, when vehicles arrive and depart the VFC randomly, their tasks also arrive at and depart the system randomly. In this paper, we propose a semi-Markov decision process (SMDP) based offloading strategy while considering these factors to obtain the maximal long-term reward reflecting the offloading delay. Our research provides a robust strategy for task offloading in VFC systems, its effectiveness is demonstrated through simulation experiments and comparison with benchmark strategies.

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to its superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering thelow complexityof the AMP algorithm andadaptabilityof GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.

The emerging deterministic networking (DetNet) has stimulated an increasing enthusiasm for the investigation of supporting deterministic, ultra-reliable, and low-latency services. However, the time-varying channel characteristics and bursty data traffic bring uncertainties for transmission, thereby making the assurance of deterministic quality-of-service (QoS) a challenging issue in practice. In this paper, we are interested in supporting the deterministic QoS demand in a massive access scenario by reducing the bit dropping rate incurred by delay violation. To minimize the bit dropping rate, a cross-layer scheduling scheme with joint channel and buffer awareness is highly desired to efficiently adjust the resource allocation among users, whose complexity increases exponentially with the number of users. Fortunately, the complexity issue can be relieved by adopting the mean-field approximation approach, which can substantially simplify the design and analysis of the cross-layer scheduling scheme with massive users. Two threshold-based scheduling policies are proposed, which have low computational complexity. Besides, we also derive the deadline-constrained capacity for massive access, which is substantially superior to that of single user transmissions. Numerical results will demonstrate the effectiveness of the mean-field approximation based cross-layer scheduling scheme.

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn’t require CSI. In Ti-GSC, there are two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM), where SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, so no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term, are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, the BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. Besides, each term of the presented loss function has a great impact on the BLEU performance of Ti-GSC, where in Rician fading syntactic learning has the greatest impact, and in Rayleigh fading, the adversarial learning becomes important.

The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low datarate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.

With the growing popularity of wearable devices, body-centric networks have become the focus of Internet of Things (IoT) research. However, malicious eavesdroppers and attackers pose threats to user privacy and network secrecy. Fortunately, physical layer security (PLS) communication and covert communication (CC) have been introduced and can be used in body-centric networks. However, network performance of both secrecy techniques has not been analyzed with an accurate wireless fading model. To fill this research gap, we study a body-centric IoT communication system with information secrecy features based on PLS and CC techniques. By adopting the Alternate Rician Shadowed fading model, we derive closed-form expressions for the secrecy outage probability and secrecy rate in PLS, and the detection error probability and covert rate in CC. To provide more explicit guidance, an algorithm for the selection of PLS and CC techniques is proposed, which aims to reach a high transmission performance state while ensuring communication security and covertness. The secrecy rate and covert rate are analytically and empirically compared. We insightfully find that, with the help of a friendly jammer, CC performs better than PLS, especially with high transmit power. However, an upper boundary of transmit power exists to ensure communication covertness in CC.

Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information.

In this paper, we propose an online federated learning framework with massive random access, aiming to learn a sequence of global models using local data that are sequentially collected by massive edge devices. As only a subset of devices is capable of collecting data and performing local model update at any specific moment, the communication pattern between the edge server and devices is random and sporadic, which is referred to assporadic local updates. This motivates us to adopt a two-phase grant-free random access scheme that consists of the activity detection and model transmission phases to facilitate efficient communication between the edge server and devices. We first provide the regret analysis for online federated learning, and derive the optimality gap in terms of successful transmission probabilities. Then, we characterize the achievable transmission rate of each active device using random matrix theory and establish the relationship between the pilot length and the outage probability. Furthermore, we propose an optimal pilot length design by minimizing the optimality gap. To validate our scheme, we provide comprehensive experimental results that demonstrate the superiority of the proposed scheme over traditional schemes in various online tasks.

Radio frequency (RF) fingerprinting is a promising device authentication technique for securing the Internet of Things. It exploits the intrinsic and unique hardware impairments of the transmitters for device identification. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RF fingerprinting. Most existing DL-based RF fingerprinting models use a single representation of radio signals as the input, while the multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of RF fingerprints. In this work, we propose a multi-channel attentive feature fusion (McAFF) method for RF fingerprinting. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including in-phase and quadrature samples, carrier frequency offsets, fast Fourier transform coefficients and short-time Fourier transform coefficients. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features are learned during the model training. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a Wi-Fi dataset using commercial Wi-Fi end-devices as the transmitters and a Universal Software Radio Peripheral platform as the receiver. Experimental results show that the proposed McAFF method significantly outperforms the single-channel-based as well as the existing DL-based RF fingerprinting methods in terms of identification accuracy and robustness.

Federated multi-task learning (FMTL) is a promising edge learning framework to fit the data with non-independent and non-identical distribution (non-i.i.d.) by leveraging the statistical correlations among the personalized models. For many practical applications in wireless communications, the sensory data are not only heterogeneous but also non-stationary due to the mobility of terminals and the randomness of link connections. The non-stationary heterogeneous data may lead to model divergence and staleness in the training stage and poor test accuracy in the inference stage. In this paper, we shall develop an adaptive FMTL framework, which works well with non-stationary data. We further propose to optimize the model updating and cluster splitting schemes in the training stage to accelerate model convergence. We also design a low-complexity model selection and pruning schemes in both the training and inference stages to select the best model for fitting the current data and delete redundant models, respectively. The proposed framework is validated in the edge learning model, namely, the linear regression problem for indoor localization in wireless networks and GNN for wireless power control problems. Numerical results demonstrate that the proposed framework can accelerate the model training convergence and reduce the computation complexity while ensuring model accuracy.

This paper investigates simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) networks with power splitting (PS) receivers and non-orthogonal multiple access (NOMA). By exploiting the conjugated beamforming method, the closed-form expressions of the information rate and the total harvested power at each user equipment (UE) are derived. To improve the system spectral efficiency, a sum rate maximization problem is formulated subjecting to the quality of service requirement at each UE and the power budget constraint at each access point by optimizing the UE clustering, the power control coefficients, and the PS ratios. To solve the formulated non-convex and mixed combinatorial problem, a machine learning-based approach is designed. Particularly, the UE clustering is first optimized by using a K-means based method and then the power control coefficients and the PS ratios are jointly optimized by a proposed multi-agent deep Q-network (MA-DQN) based method. The impact of the discount factor of the MA-DQN based method on the derived result is discussed. It is proved that by setting the discount factor as zero, the performance loss is negligible. Based on this observation, a zero-discount MA-DQN (0-γ MA-DQN) based method is further proposed to improve the computational efficiency. Also, the computational complexity of the proposed machine learning-based approach is analyzed. Simulation results show that the proposed machine learning-based approach outperforms various existing approaches. Moreover, it indicates that CF-mMIMO and NOMA could enhance the propagation performance of SWIPT while the proposed machine learning-based approach could facilitate resource allocation.

The emerging real-time wireless communication systems are expected to provide deadline assurance for delay-sensitive traffics generated from factory automation, smart grids, and automatic driving, etc. Although there has been cutting edge research on diversity-enabled deadline assurance, real-time communications may suffer from poor energy efficiency. In this paper, we present a paradigm-shift real-time wireless communication method based on matching diversity that is judiciously designed for orthogonal frequency division multi-access (OFDMA) systems. In particular, we adopt bipartite graph to formulate a unified framework for OFDMA, based on which joint subcarrier matching and power adaptation policies are conceived to provide real-time transmissions, also referred to as just-in-time services (JITS). By bridging the average power and the formula of the outage probability, we show that the deadline constraints can be satisfied with a finite average power, when the number of users in the OFDMA system is not greater than that of subcarriers with independent channel gains. Furthermore, we also derive the approximate but yet analytical results on the required average power consumption. Numerical results indicate that a substantial energy efficiency gain can be attained in real-time OFDMA systems if the subcarrier interleaving based frequency diversity is replaced by matching diversity.

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world assumption, assuming either the same data distribution for training and testing, or the system could have access to a large out-of-distribution (OoD) dataset for retraining. However, in practical open-world scenarios, task-oriented communication systems will be exposed to unknown OoD data. The powerful approximation ability of learning methods may force the task-oriented communication systems to overfit the training data (i.e., in-distribution data). Therefore, these systems tend to provide overconfident judgments when encountering OoD data. Based on the information bottleneck (IB) framework, we propose a class conditional IB (CCIB) approach to address this problem, supported by information-theoretical insights. The idea is to extract distinguishable features from in-distribution data while keeping their compactness and informativeness. It is achieved by imposing the class conditional latent prior distribution and enforcing the latent of different classes to be far away from each other. Simulation results shall demonstrate that the proposed approach detects OoD data more efficiently than the baselines and state-of-the-art approaches, without compromising the rate-distortion tradeoff.

Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm and implement differential privacy with a Gaussian mechanism. However, the inherent privacy-utility trade-off in these systems severely degrades the training performance if a tight privacy budget is enforced. Besides, the Gaussian mechanism requires model weights to be of high-precision. To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee. Specifically, we propose to adopt binary neural networks (BNNs) and introduce discrete noise in the FL setting. Binary model parameters are uploaded for higher communication efficiency and discrete noise is added to achieve the client-level differential privacy protection. The achieved performance guarantee is rigorously proved, and it is shown to depend on the level of discrete noise. Experimental results based on MNIST and Fashion-MNIST datasets will demonstrate that the proposed training algorithm achieves client-level privacy protection with performance gain while enjoying the benefits of low communication overhead from binary model updates.

Delay-constrained communications have attracted considerable recent attention because it holds the promise of playing a vital role in time sensitive networking (TSN) and deterministic networking (DetNet). In this paper, we investigate the tradeoff between power, latency, and throughput, which characterizes a fundamental performance limit of diversity enabled delay-bounded communications over fading channels. In particular, we are interested in a typical cross-layer scheduling policy, namely, the age-aware non-FIFO strategy, which is capable of satisfying a hard delay constraint by exploiting the physical-layer diversity methods. Saddle point approximation is adopted to obtain the analytical approximation of the average transmission power as a function of the throughput and the delay threshold normalized to the coherence time. This analytical expression characterizes the power-latency-throughput tradeoff of the diversity enabled delay-bounded communications over the fading channel. To validate our analysis, we present a discretization-based method to conduct the numerical calculation. The numerical results match well with the simulation results, which also shows that the approximation error will vanish with sufficiently small discretization granularity.

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning. By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models. Two specific schemes of FedLP are designed for scenarios with homogeneous local models and heterogeneous ones. Both theoretical and experimental evaluations are developed to verify that FedLP relieves the system bottlenecks of communication and computation with marginal performance decay. To the best of our knowledge, FedLP is the first framework that formally introduces the layer-wise pruning into FL. Within the scope of federated learning, more variants and combinations can be further designed based on FedLP.

This paper investigates the joint optimization of the task offloading and resource allocation in small cell mobile edge computing (MEC) networks, where multiple small-cell base stations (SBSs) integrating MEC servers provide computing services for user devices (UDs) in their cells. In pursuit of green network design and also saving energy of the UDs, an optimization problem is formulated to minimize the total energy consumption of UDs subjecting to the delay constraints. Since the existing optimization schemes based on traditional optimization theory cannot adapt to the time-varying channel and highly dynamic UD requirements due to their complexity, we propose an efficient learning-enabled joint task offloading and resource allocation scheme based on proximal policy optimization (PPO) framework. Simulation results show that the total energy consumption of UDs is significantly reduced by our proposed PPO-based scheme, and also show the trade-off between the delay constraints satisfaction probability and the total energy consumption.

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.

Federated learning (FL) has recently emerged as an innovative paradigm to train models among distributed agents. Conventional FL considers the center as an aggregator and trains from distributed data, while the collected global information at the center is not effectively utilized. Thus, the restricted information from local observations may limit the model accuracy. If FL can introduce data sets from the network server, the distributed models may be largely improved by the extra global information. Since network agents may not be completely trusted, the center cannot directly broadcast its raw data for security concern. Then, how to combine the central sets with FL? In this article, we propose to add a learning model at the center, which obtains the central sets as input. The outputs can be transmitted to network agents and integrated into local models instead of the raw data. The central and local models could be trained to form an integration for intelligent inference. Then, what is the integrated performance gain comparing with the original horizontal FL (HFL) and how to implement it? To figure out these two problems, we propose the vertical-HFL (VHFL) scheme, where models of the center and agents are trained collaboratively. We further analyze its convergence and the related communication channel, proposing the theoretical bounds to guide the network implementation of VHFL. Some simulation results will demonstrate the effectiveness of our proposed VHFL scheme. It is expected that VHFL will be an important block for the next generation of smart services.

Distributed machine learning is envisioned as the bedrock of future intelligent networks, where agents exchange information with each other to train models collaboratively without uploading data to a central processor. Despite its broad applicability, a downside of distributed learning is the need for iterative information exchange between agents, which may lead to high communication overhead unaffordable in many practical systems with limited communication resources. To resolve this communication bottleneck, we need to devise communication-efficient distributed learning algorithms and protocols that can reduce the communication cost and simultaneously achieve satisfactory learning/optimization performance. Accomplishing this goal necessitates synergistic techniques from a diverse set of fields, including optimization, machine learning, wireless communications, game theory, and network/graph theory. This Special Issue is dedicated to communication-efficient distributed learning from multiple perspectives, including fundamental theories, algorithm design and analysis, and practical considerations.

Distributed learning is envisioned as the bedrock of next-generation intelligent networks, where intelligent agents, such as mobile devices, robots, and sensors, exchange information with each other or a parameter server to train machine learning models collaboratively without uploading raw data to a central entity for centralized processing. By utilizing the computation/communication capability of individual agents, the distributed learning paradigm can mitigate the burden at central processors and help preserve data privacy of users. Despite its promising applications, a downside of distributed learning is its need for iterative information exchange over wireless channels, which may lead to high communication overhead unaffordable in many practical systems with limited radio resources such as energy and bandwidth. To overcome this communication bottleneck, there is an urgent need for the development of communication-efficient distributed learning algorithms capable of reducing the communication cost and achieving satisfactory learning/optimization performance simultaneously. In this paper, we present a comprehensive survey of prevailing methodologies for communication-efficient distributed learning, including reduction of the number of communications, compression and quantization of the exchanged information, radio resource management for efficient learning, and game-theoretic mechanisms incentivizing user participation. We also point out potential directions for future research to further enhance the communication efficiency of distributed learning in various scenarios.

Sensing will become an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to incorporate sensing capability into current cellular networks. However, the interference management between sensing and communication, as well as the collaborative sensing by multiple sensing nodes (SNs), faces significant challenges. In this paper, we first propose a two-stage protocol to tackle the interference between two sub-systems, where the echoes created by communication signals, i.e., interference for sensing, are estimated in the clutter estimation (CE) stage and then utilized for interference management in the target sensing (TS) stage. Then, a networked sensing detector is derived to exploit the perspectives provided by multiple SNs for sensing the same target. The macro-diversity from multiple SNs, the array gain, and the higher angular resolution from multiple receive antennas of each SN are then investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition for one SN’s contribution to be positive, based on which a SN selection algorithm is proposed. To reduce the communication workload, we propose a distributed model-driven deep-learning algorithm that utilizes partially-sampled data for CE. Simulation results demonstrate the benefits of networked sensing and validate the higher efficiency of the proposed CE algorithm than existing methods.

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without re-training. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs’ computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

The emerging Ultra-Reliable and Low-Latency Communication (URLLC) is expected to meet a hard or probabilistic delay constraint that plays a key role in Time Sensitive Networking (TSN) and Deterministic Networking (DetNet). In this paper, we are interested in simple bounds for delay-constrained wireless communications with deterministic or even random packet arrivals. More specifically, we present a sufficient condition and derive a legitimate data arrival rate, with which the bounded delay can be guaranteed deterministically with an average power constraint. Our derived results will show that the delay-bounded data rate increases with the average power and the tolerated latency normalized to the coherent time. Furthermore, we derive the upper and lower bounds of the delay-violation probability (DVP) when the bounded delay condition cannot be satisfied. It is revealed that in the log coordinate, the DVP as a function of the normalized tolerated latency may enjoy a non-linear decay, with a decay rate that increases with the latency bound. This is in contrast to the large deviation based performance analysis that focuses on the low-latency transmission policies achieving linearly decayed DVP only. Our result demonstrates that the joint channel-aware and queue-aware scheduling may significantly reduce the DVP, compared to the single-layer approaches.

Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server’s access to many clients’ data and the edge servers’ closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.

Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network architectures inherited from applications such as computer vision. They often yield poor performance in large scale networks (i.e., poor scalability) and unseen network settings (i.e., poor generalization). To resolve these issues, graph neural networks (GNNs) have been recently adopted, as they can effectively exploit the domain knowledge, i.e., the graph topology in wireless communications problems. GNN-based methods can achieve near-optimal performance in large-scale networks and generalize well under different system settings, but the theoretical underpinnings and design guidelines remain elusive, which may hinder their practical implementations. This paper endeavors to fill both the theoretical and practical gaps. For theoretical guarantees, we prove that GNNs achieve near-optimal performance in wireless networks with much fewer training samples than traditional neural architectures. Specifically, to solve an optimization problem on an$n$-node graph (where the nodes may represent users, base stations, or antennas), GNNs’ generalization error and required number of training samples are$\mathcal {O}(n)$and$\mathcal {O}(n^{2})$times lower than the unstructured multi-layer perceptrons. For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement. Extensive simulations, which cover a variety of important problems and network settings, verify our theory and the effectiveness of the proposed design framework.

The emerging deterministic networking (DetNet) has attracted considerable recent attention due to its potential in supporting services with ultra-low latency, low delay variation, and extremely low loss. In the development of the DetN et, hard delay constraints and extremely low loss are highly desired to be guaranteed. However, the bursty data traffic demands and random channel qualities bring uncertainties for transmission, incurring bit dropping due to the possible delay violation. How to support reliable transmission services by reducing bit dropping rates becomes a critical issue in the envisioned sixth-generation (6G) network. To efficiently reduce the bit dropping rate, a joint channel and buffer aware scheduling is highly desired, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean-field approximation to simplify the design and analysis of the joint channel and buffer aware scheduling under a huge number of users. A threshold-based scheduling policy is proposed, which has low computational complexity. Numerical results will demonstrate the effectiveness of the mean-field approximation based scheduling.

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to preprocess its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.

Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its large array aperture and small wavelength, the near-field region of THz UM-MIMO is greatly enlarged. The high-dimensional channel of such systems thus consists of a stochastic mixture of far and near fields, which renders channel estimation extremely challenging. Previous works based on uni-field assumptions cannot capture the hybrid far- and near-field features, thus suffering significant performance loss. This motivates us to consider hybrid-field channel estimation. We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee. Built upon classic orthogonal approximate message passing, we transform each iteration into a contractive mapping, comprising a closed-form linear estimator and a neural network based non-linear estimator. A major algorithmic innovation involves applying fixed point iteration to compute the channel estimate while modeling neural networks with arbitrary depth and adapting to the hybrid-field channel conditions. Simulation results verify our theoretical analysis and show significant performance gains over state-of-the-art approaches in the estimation accuracy and convergence rate.

This paper investigates the cache-assisted power allocation in time domain for high-speed railway communications (HSRC), where train users are divided into real-time users (RUs) and non-real-time users (NRUs) from the time-sensitive perspective. RU's real-time data rate demand is ensured by power allocation, and NRU's data amount requirement is guaranteed by releasing cached content. In order to maximize the mobile service amount (MSA) of HSRC, an optimization problem is formulated to find the optimal cache switching time and power distribution under the constraints of total available energy, maximum power, caching and releasing causality, RU's minimal data rate requirement and NRU's minimal data amount requirement. Since the formulated problem is non-convex, a two-stage algorithm is proposed. In the first stage, we fix the cache switching time and transform the problem to be convex, and then use Karush-Kuhn-Tucker (KKT) condition to determine the optimal power distribution. In the second stage, one-dimensional search is employed to find the optimal cache switching time. Simulation results show that our proposed method is able to guarantee the RU's data rate threshold all the time by sacrificing some MSA. Moreover, the increase of data rate threshold and speed lead to a decrease of MSA, while the cache usage rate has relatively weak influence on MSA. © 2022 IEEE.

Queue length violation probability, i.e., the tail distribution of the queue length, is a widely used statistical quality-of-service (QoS) metric in wireless communications. Characterizing and optimizing the queue length violation probability have great significance in time sensitive networking (TSN) and ultra reliable and low-latency communications (URLLC). However, it still remains an open problem. In this paper, we put our focus on the analysis of the tail distribution of the queue length from the perspective of cross-layer design in wireless link transmission. We find that, under the finite average power consumption constraint, the queue length violation probability can achieve zero with diversity gains, while it can have a linear-decay-rate exponent according to large deviation theory (LDT) with limited receiver sensitivity. Besides, we find that the queue-length tail distribution with an arbitrary-decay-rate exponent under the finite average power constraint exists in the Rayleigh fading channel. Then, we generalize the sufficient conditions for the communication system belonging to these three scenarios, respectively. Moreover, we apply the above results to analyze the wireless link transmission in the Nakagami-m fading channel. Numerical results with approximation validate our analysis.

Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning from distributed data. Most previous works are based on federated average (FedAvg), which, however, faces several critical issues, including a high communication overhead and the difficulty in dealing with heterogeneous model architectures. Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients. However, so far there is no unified algorithmic framework or theoretical analysis for FD-based methods. In this paper, we first present a generic meta-algorithm for FD and investigate the influence of key parameters through empirical experiments. Then, we verify the empirical observations theoretically. Based on the empirical results and theory, we propose a communication-efficient FD algorithm with active data sampling to improve the model performance and reduce the communication overhead. Empirical simulations on benchmark datasets will demonstrate that our proposed algorithm effectively and significantly reduces the communication overhead while achieving a satisfactory performance.

In recent years, there has been a surge in applying deep learning to various challenging design problems in communication networks. The early attempts adopt neural architectures inherited from applications such as computer vision, which suffer from poor generalization, scalability, and lack of interpretability. To tackle these issues, domain knowledge has been integrated into the neural architecture design, which achieves near-optimal performance in large-scale networks and generalizes well under different system settings. This paper endeavors to theoretically validate the importance and effects of neural architectures when applying deep learning to communication network design. We prove that by exploiting permutation invariance, a common property in communication networks, graph neural networks (GNNs) converge faster and generalize better than fully connected multi-layer perceptrons (MLPs), especially when the number of nodes (e.g., users, base stations, or antennas) is large. Specifically, we prove that under common assumptions, for a communication network with n nodes, GNNs converge O(n log n) times faster and their generalization error is O(n) times lower, compared with MLPs.

This paper studies the cooperative base station switching-off for multiple mobile network operators (MNOs) in multiple-input single-output (MISO) networks. To save the energy consumption of the system and also guarantee MNOs’ profit, we formulate a power minimization problem by jointly optimizing the operation modes of BSs, the connection states between users and BSs, and the beamforming vectors of multi-antenna BSs. To tackle the formulated non-convex problem, a roaming-cost-based BS switching-off scheme is designed to first search the feasible BSs that can be switched off and then optimize the beamforming vectors. Simulation results show that the proposed scheme not only reduces network power consumption but also avoids the profit loss at each MNO. It is also observed that there exists a minimum power consumption and a maximum average profit gain in terms of the rate price. Besides, the proposed scheme has notable capability in improving the profit at the low rate price region.

Federated learning (FL) has recently emerged as a transformative paradigm that jointly train a model with distributed devices while avoiding the need for central data collection. Due to the limited observation range, the devices only contain local information, which limits the quality of trained models. In this case, combining the global information into FL may be helpful. However, in horizontal FL, the central agency only acts as a model aggregator without utilizing its global observation. Meanwhile, the global data may not be directly transmitted to agents for data security. Then how to utilize the global observation residing in the central agency while protecting its safety thus rises up as an important problem in FL. In this paper, we develop a vertical-horizontal federated learning (VHFL) scheme, where the global feature is shared with the agents in a procedure similar to that of vertical FL. It is shown by experiments that the proposed VHFL could enhance the accuracy compared with horizontal FL while protecting the central data from being announced.

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.

Inspired by diversity technology, we rethink the model enhancement from the view of wireless communication and propose a space-time framework for ensemble learning, called diversity learning. Such framework provides a new perspective that links the multi-model learning with the multi-channel commu-nication. In this paper, 2×1 diversity learning is mainly studied whose efficiency is guaranteed theoretically. We also evaluate the proposed scheme on two popular image classification tasks, MNIST and CIFAR-10. The results elucidate that the diversity learning reaps superiority on model enhancement, convergence, complexity and robustness compared to single models as well as weighting ensemble approach. Furthermore, the diversity schemes can be deployed in several emerging distributed learning systems, especially the mobile scenarios such as edge computing and cooperative learning where the resources for computation and communication are restricted.

In multiservice systems, multiple different Age of Information (AoI) penalty functions and corresponding algorithms are required to be deployed, which may result in high deployment complexity. Motivated by this, we propose a universal function$f(t)=\beta e^{\alpha t} -\beta $called$\alpha $-$\beta $AoI penaltyfunction to characterize different nonlinear forms of AoI penalty. With the presented$\alpha $-$\beta $AoI penalty function, we analyze the performance of wireless-powered communication networks (WPCNs), where a sensor first harvests energy from a wireless power station (WPS) and then transmits the generated update to its data collector. The sensor is equipped with a battery of limited energy capacity. When the battery of the sensor node is fully charged, the sensor generates a status update and uses all available energy to transmit it. A closed-form expression of the system average$\alpha $-$\beta $AoI penalty is derived by using some limit methods. In order to minimize the average$\alpha $-$\beta $AoI penalty of the system, an optimization problem is formulated to optimize the battery capacity. Simulation results demonstrate the correctness of our theoretical analysis results and show that there is a unique optimal battery capacity that optimizes the system AoI performance. Moreover, when the system is with the exponential-shape AoI penalty function ($\beta >0$and$\alpha >0$), with the increment of$\alpha $and$\beta $increase, the average$\alpha $-$\beta $AoI penalty also increases. Differently, when the system is with the logarithmic-shape AoI penalty function ($\beta < 0$and$\alpha < 0$), with the increment of$\alpha $and$\beta $, the average$\alpha $-$\beta $AoI penalty decreases.

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep${Q}$network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs’ average AoI are also discussed, which are expected to provide some useful insights.

In this article, we consider the timeliness of information transmissions in a three-node industrial wireless sensor network (IWSN) in terms of Age of Information (AoI). In this network, a sensor monitors the ambient environment and transmits the sensed information to a remote monitor directly or through a relay node. In particular, we are interested in how the timeliness of the system is changed by decomposing the long-distance transmission with a relay and by enabling parallel transmissions over the two hops with a packet buffer. To this end, we derive the average AoIs of the transmissions over the direct-link, the relay-links with and without a buffer in a closed form. The obtained results show that the relay-link with a buffer outperforms the other two links, while the relay-link without a buffer outperforms the direct-link only if the relay is properly placed and the sensor–monitor distance is relatively large. On the condition that the average transmission times over the direct-link and the relay-link without a buffer are equal, we further evaluate how fast the average AoI can be reduced by using a relay or a packet buffer, as the packet rate approaches the maximum feasible rate over the links. It is shown that, although the sensor–monitor distance dominates the average AoIs of the links, the gains of using the relay and the buffer do not change much with the distance and are approximately constant.

As an emerging technique, mobile-edge computing (MEC) introduces a new scheme for various distributed communication-computing systems, such as industrial Internet of Things (IoT), vehicular communication, smart city, etc. In this work, we mainly focus on the timeliness of the MEC systems where the freshness of the data and computation tasks is significant. First, we formulate a kind of age-sensitive MEC models and define the average Age-of-Information (AoI) minimization problems of interests. Then, a novel mixed-policy-based multimodal deep reinforcement learning (RL) framework, called heterogeneous multiagent actor–critic (H-MAAC), is proposed as a paradigm for joint collaboration in the investigated MEC systems, where edge devices and center controller learn the interactive strategies through their own observations. To improve the system performance, we develop the corresponding online algorithm by introducing the edge federated learning mode into the multiagent cooperation whose advantages on learning convergence can be guaranteed theoretically. To the best of our knowledge, it is the first joint MEC collaboration algorithm that combines the edge federated mode with the multiagent actor–critic RL. Furthermore, we evaluate the proposed approach and compare it with popular RL-based methods. As a result, the proposed algorithm not only outperforms the baselines on average system age, but also promotes the stability of training process. Besides, the simulation outcomes provide several insights for collaboration designs over MEC systems.

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from “connected things” to “connected intelligence”. However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

The next generation massive access has attracted considerable recent attention due to its potential in smart meters, industrial internet of things (IIoT), and smart traffics, etc. However, how to achieve the minimum queuing delay in massive access still remains open, thereby making quality-of-service (QoS) assurance a challenging issue in practice. In this paper, we aim at minimizing the average queuing delay by applying cross-layer scheduling with joint channel and buffer awareness, the complexity of which increases exponentially with the number of users. Fortunately, with massive users or devices, mean-field approximation can be adopted to substantially simplify the design and analysis of the delay-optimal scheduling. More specifically, we present a cocktail filling policy and a queue-aware multiuser diversity protocol, in which all backlogged packets of a user will be served by either NOMA or TDMA mode respectively, if the user’s channel gain is beyond a certain threshold. The average queuing delay and queue-length-violation probability are derived based on a Markov model. Numerical results will also demonstrate that the mean-field approximation based joint physical and network layer scheduling is capable of improving the QoS in massive access.

While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We propose a Transformer based framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively. In this case, DeepSC-IR is trained to optimize the distance in embedding space between images and DeepSC-MT is trained to minimize the semantic errors by recovering the semantic meaning of sentences. For the multimodal multi-user system, we develop a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting text-image information at the transmitters and fusing it at the receiver. In particular, a novel layer-wise Transformer is designed to help fuse multimodal data by adding connection between each of the encoder and decoder layers. Numerical results show that the proposed models are superior to traditional communications in terms of the robustness to channels, computational complexity, transmission delay, and the task-execution performance at various task-specific metrics.

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically.

Memory caches are being used aggressively in today's data analytics systems such as Spark, Tez, and Piccolo. The significant performance impact of caches and their limited sizes call for efficient cache management in data analytics clusters. However, prevalent data analytics systems employ rather simple cache management policies—notably Least Recently Used (LRU) and Least Frequently Used (LFU)—that areobliviousto the application semantics of data dependency, expressed as directed acyclic graphs (DAGs). Without this knowledge, cache management can, at best, be performed by “guessing” the future data access patterns based on history, which frequently results in inefficient, erroneous caching with a low hit rate and a long response time. Worse still, the lack of data dependency knowledge makes it impossible to retain theall-or-nothingcache property of cluster applications, in that a compute task cannot be sped up unless all the dependent data has been kept in the main memory. In this paper, we propose a novel cache replacement policy, named Least Reference Count (LRC), which exploits the application's data dependency information to optimize the cache management. LRC keeps track of thereference countof each data block, defined as the number of dependent child blocks that have not been computed yet, and always evicts the block with the smallest reference count. Furthermore, we incorporate the all-or-nothing requirement into LRC by coordinately managing the reference counts of all the input data blocks for the same computation. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, the proposed policies well address the all-or-nothing requirement and significantly improve the cache performance. Compared with LRU and a recently proposed caching policy called MEMTUNE, LRC improves the caching performance of typical workloads in production clusters by 22 and 284 percent, respectively.

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate for the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Moreover, the proposed algorithms are feasible to any finite number of antennas, and thus can be applicable in both ordinary MIMO and massive MIMO settings. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS-assisted massive MIMO systems with antenna selection.

Unmanned aerial vehicles (UAVs) with huge-capacity batteries could be employed to wirelessly charge the ground sensor users (GSUs) and enhance the coverage of aerial wireless networks in outdoor Internet of Things (IoT). This paper investigates the information and energy coverage of UAV-enabled simultaneous wireless information and power transfer (SWIPT) networks. Both power splitting (PS) and time switching (TS) receiver architectures are considered. By using stochastic geometry approach, the general and explicit expressions of the information coverage probability (ICP), the energy coverage probability (ECP) and the joint information and energy coverage probability (JIECP) are derived under the nonlinear and linear energy harvesting (EH) models, respectively. Particularly, the Laplace transform and the probability generating functional (PGFL) are used to derive the ICP. And, Campbell’s theorem and the maximum function are applied to obtain the ECP and the JIECP, respectively. To achieve the optimal UAVs’ deployment density, the maximization optimization problems are formulated for the PS-based and TS-based systems, respectively. By using the series expansion of$Q(x)$($Q$-function) with large$x$, the closed-form approximating optimal solutions to the formulated problems are obtained. Monte Carlo simulations validate the correction of our obtained theoretical results, and numerical results show that the performance of the PS-based system is superior to that of the TS-based one. Moreover, when the energy requirement of GSUs or the transmit power of UAVs is relatively large, or when the information requirement of GSUs or the UAV deployment density is relatively small, compared with the nonlinear EH model, the analysis bias caused by traditional linear EH model is relatively large and in these cases, traditional linear EH model cannot be used to replace the nonlinear EH one for the system performance analysis or optimal system design.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple-access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we present the convergence analysis of our proposed IRS-assisted AirComp-based FL system, based on which we propose to maximize the number of scheduled devices of each communication round under certain mean-squared error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex programming for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results demonstrate that our proposed algorithm and the deployment of an IRS can achieve a higher FL prediction accuracy than the baseline schemes.

Integrated sensing and communication (ISAC) creates a platform to exploit the synergy between two powerful functionalities that have been developing separately. However, the interference management and resource allocation between sensing and communication have not been fully studied. In this paper, we consider the design of perceptive mobile networks (PMNs) by adding sensing capability to current cellular networks. To avoid full-duplex operation, we propose the PMN with distributed target monitoring terminals (TMTs) where passive TMTs are deployed over wireless networks to locate the sensing target (ST). To manage the interference between sensing and communication, we jointly optimize the transmit and receive beamformers towards the communication user equipment (UEs) and the ST by alternating-optimization (AO) and prove its convergence. To reduce computation complexity and obtain physical insights, we further investigate the use of linear transceivers, including zero forcing and beam synthesis (B-syn). Our analysis revealed interesting physical insights: 1) instead of forming dedicated sensing signals, it is more efficient to redesign the communication signals for both communication and sensing purposes and “leak” communication energy for sensing; 2) the amount of energy leakage from one UE to the ST depends on their relative locations.

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical successes remain elusive. In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing. By identifying the critical role of smoothness, a key concept in graph signal processing, we develop a unified graph convolution-based framework for CF. We prove that many existing CF methods are special cases of this framework, including the neighborhood-based methods, low-rank matrix factorization, linear auto-encoders, and LightGCN, corresponding to different low-pass filters. Based on our framework, we then present a simple and computationally efficient CF baseline, which we shall refer to as Graph Filter based Collaborative Filtering (GF-CF). Given an implicit feedback matrix, GF-CF can be obtained in a closed form instead of expensive training with back-propagation. Experiments will show that GF-CF achieves competitive or better performance against deep learning-based methods on three well-known datasets, notably with a 70% performance gain over LightGCN on the Amazon-book dataset.

In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO. The detector is composed of two modules, a nonlinear module at the central processing unit (CPU) and a linear module at the access point (AP). The turbo principle in iterative decoding is utilized to compute and pass the extrinsic information between modules. An analytical framework is then provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Simulation results will show that the proposed method outperforms the distributed detectors in terms of bit-error rate.

Ultra-reliable and low latency communications (URLLC) has recently attracted much attention because it holds the promise of supporting mission-critical applications in task-driven radio access networks. In URLLC, finite-blocklength coding plays an important role. In this paper, we are interested in the performance limit of finite-blocklength coded wireless URLLC with burst traffics, which may induce severe delay in practice. A large deviation technique is exploited to derive the delay violation probability given a hard delay constraint. Specifi-cally, an approximate QoS exponent is conceived to characterize the reliability-latency tradeoff, i.e., tradeoff between the error probability and delay violation probability. Further, we present closed-form upper and lower bounds of the QoS exponent, which are shown to become tight in the high signal-to-noise ratio (SNR) regime. Our theoretical analysis is also validated by numerical results.

Short packet transmission techniques have recently attracted substantial attention due to their potential of achieving low latency in emerging Industrial Internet of Things (IIoT). To this end, finite-blocklength coding is expected to play a central role in short packet communications. With the random arrival of short packets, there exists a fundamental tradeoff between reliability and latency in finite-blocklength coding based transmission systems. In this paper, we consider the reliability-latency tradeoff of short packet transmission over AWGN channels. More specifically, an effective bandwidth approach is adopted to obtain the delay violation probability given a hard delay constraint. We present an approximate but closed-form Quality-of-Service (QoS) exponent that bridges the delay violation probability of short packets and the error probability of finite-blocklength coding. Numerical results shall validate our theoretical analysis, which characterizes a performance limit of ultra-reliable and low latency communications (URLLC) over AWGN channels.

The emerging Industrial Internet of Things (IIoT) is driving an ever increasing demand for providing low latency services to massive devices over wireless channels. As a result, how to assure the quality-of-service (QoS) for a large amount of mobile users is becoming a challenging issue in the envisioned sixth-generation (6G) network. In such networks, the delay-optimal wireless access will require a joint channel and queue aware scheduling, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean field approximation to conceive a buffer-aware multi-user diversity or opportunistic access protocol, which serves all backlogged packets of a user if its channel gain is beyond a threshold. A theoretical analysis and numerical results will demonstrate that not only the cross-layer scheduling policy is of low complexity but is also asymptotically optimal for a huge number of devices.

Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have been treated as two stand-alone components, which makes it difficult to achieve a global system optimality. In this paper, we propose a deep learning-based approach that directly optimizes the beamformers at the base station according to the received uplink pilots, thereby, bypassing the explicit channel estimation. Different from the existing fully data-driven approach where all the modules are replaced by deep neural networks (DNNs), a neural calibration method is proposed to improve the scalability of the end-to-end design. In particular, the backbone of conventional time-efficient algorithms, i.e., the least-squares (LS) channel estimator and the zero-forcing (ZF) beamformer, is preserved and DNNs are leveraged to calibrate their inputs for better performance. The permutation equivariance property of the formulated resource allocation problem is then identified to design a low-complexity neural network architecture. Simulation results will show the superiority of the proposed neural calibration method over benchmark schemes in terms of both the spectral efficiency and scalability in large-scale wireless networks.

Recently, federated learning (FL) has been treated as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their data sets. Different from centralized training on some collected data sets, FL training suffers a lot of constraints from limited resources in the network. Therein, the bandwidth and package loss restrict interactions in training. Meanwhile, the highly distributed data sets and limited computation could also affect its convergence. To figure out the specific impact, we analyze the convergence rate of FL training considering both communication and training. Further taking in training costs in terms of time and power, the closed-form optimal settings for communication networks are proposed with principles to assist the parameter selection. The results build a bridge between AI and communication, giving us an intuitive knowledge of how the background system could influence the distributed training process.

We consider an Internet-of-Things (IoT) system in which a sensor observes a phenomenon of interest with exponentially distributed intervals and delivers updates to a monitor with random service times. At the monitor, the received updates are used to make decisions with deterministic or random intervals. For this system, we investigate the freshness of the received updates at decision epochs using the age upon decisions (AuDs) metric. With the first-come-first-served (FCFS) policy, theoretical results show that: 1) when the decisions are made with exponentially distributed intervals, the average AuD of the system is smaller when the service time (e.g., transmission time) is uniformly distributed than when it is exponentially distributed and would be the smallest if it is deterministic; 2) when the decisions are made periodically, the average AuD of the system is larger than, and decreases with decision rate to, the average AuD of the corresponding system with Poisson decision intervals; and 3) the probability of missing to use a received update for any decisions is decreasing with the decision rate and is the smallest if the service time is deterministic. When the last-come-first-served (LCFS) policy with preemption is used, we observe that systems with Poisson service processes perform the best while systems with periodic service processes perform the worst. For IoT-based monitoring systems, therefore, it is suggested to use deterministic monitoring schemes, deterministic transmitting schemes, and Poisson decision schemes so that the received updates are as fresh as possible at the time they are used to make decisions.

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV's trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV's trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

This article investigates a multifog server (FS)-assisted nonorthogonal multiple access (NOMA)-based wireless powered network, where an energy-limited wireless device (WD) first harvests energy from a power transmitter (PT) and multiple helping FSs and then uses the harvested energy to partially offload its computing task to the FSs with NOMA for computing. To explore the WD's performance limit in terms of achievable computation rate, an optimization problem is formulated by jointly optimizing the time assignment, the power allocation, and the computation frequency under multiple system constraints. Since the problem is nonconvex with no known solution, an efficient solution approach is designed to achieve the ε-optimal solution, in which the transmit power vector and the computation frequency are jointly optimized with fixed-time assignment, and then, a golden section search (GSS)-based algorithm is designed to find the optimal time assignment. For the case when the FS is with sufficiently strong computation capability, some semiclosed-form results are derived. Numerous results show that our proposed design achieves much higher computation rate than benchmark schemes. Moreover, with the increment of the helping FSs, the achievable computation rate increases while the increasing rate is decreased. Besides, by employing NOMA, the WD's computation rate is also improved compared with orthogonal multiple access (OMA)-based scheme. Additionally, in such a system, with nonlinear energy harvesting (EH) model adopted, the more the helping FSs are deployed, the more the performance loss caused by the traditional linear EH model can be reduced.

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients’ private data. Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels. Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL. How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL’s delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training. In this paper, we present a unified framework to calculate the approximate delay distributions of FL over arbitrary fading channels. Specifically, saddle point approximation, extreme value theory (EVT), and large deviation theory (LDT) are jointly exploited to find the approximate delay distribution along with its tail distribution, which characterizes the quality-of-service of a wireless FL system. Simulation results will demonstrate that our approximation method achieves a small approximation error, which vanishes with the increase of training accuracy.

A UAV-aided wireless power transfer and data collection network is studied, where it is assumed that when the harvested energy at the sensor node (SN) cannot surpass its circuit activation threshold or the received data rate at UAV falls below a minimal required rate threshold, the information outage occurs. The closed-form expressions of energy outage probability and rate outage probability are derived at first, and then the overall outage probability and coverage performance of the system are analyzed. Based on which, an optimization problem is formulated to minimize the overall outage probability by optimizing UAV's elevation angle and the time splitting (TS) factor. Since the problem is non-convex and has no known solution, an alternating optimization (AO)-based algorithm with Golden-section (GS) based linear search method is designed to find the global optimal solution. In order to explore the maximum coverage area of the UAV for a given tolerable outage probability, another optimization problem is also formulated to maximize the coverage range by optimizing UAV's elevation angle. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution of the optimal elevation angle for maximizing the coverage area is derived. Monte Carlo simulations verify the accuracy of the derived closed-form expression of the overall outage probability and the semi-closed-form expressions of the optimum UAV's elevation angle and TS factor. It shows that there exist a unique optimum elevation angle and the TS factor to achieve the minimum overall outage probability, and significant performance gain can be obtained by using our proposed optimization scheme. The developed theoretical results can be useful to the design of UAV-aided wireless communication systems with wireless power transfer.

Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis. Specifically, we first demonstrate that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property. We then identify a family of neural networks, namedmessage passing graph neural networks(MPGNNs). It is demonstrated that they not only satisfy the permutation equivariance property, but also can generalize to large-scale problems, while enjoying a high computational efficiency. For interpretablity and theoretical guarantees, we prove the equivalence between MPGNNs and a family of distributed optimization algorithms, which is then used to analyze the performance and generalization of MPGNN-based methods. Extensive simulations, with power control and beamforming as two examples, demonstrate that the proposed method, trained in an unsupervised manner with unlabeled samples, matches or even outperforms classic optimization-based algorithms without domain-specific knowledge. Remarkably, the proposed method is highly scalable and can solve the beamforming problem in an interference channel with 1000 transceiver pairs within 6 milliseconds on a single GPU.

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. As FL does not collect and store the data centrally, it requires frequent model exchange through the wireless network. However, since the aggregation in FL can be partially participated with synchronized frequency, its communication pattern is different from the conventional network. Therein, limited bandwidth and package loss restrict interactions in training. Thus, the network scheduling could largely affect the FL convergence. To figure out the specific effects, we analyze the convergence rate of FL regarding the joint impact of communication and training. Combining it with the network model, we formulate the optimal scheduling problem for FL implementation. The theoretical results could guide the hyper-parameter design in the network and explain the principle of how the wireless communication could influence the FL training process.

This paper investigates a multi-node wireless powered communication network (WPCN), where a hybrid access point (HAP) first charges an Internet of Thing (IoT) device wirelessly with the assistance of multiple selfish wireless nodes (WNs), and then the IoT device uses the harvested energy to transmit real-time status updates to the HAP. Two incentive schemes, i.e., the energy-incentive scheme and the price-incentive scheme, are designed to overcome the selfishness of the WNs and enhance the per-packet AoI performance. For the energy-incentive scheme, an AoI-energy utility function is defined and an optimization problem is formulated to maximize the AoI-energy utility value of the HAP-IoT device pair. By using equality constraint elimination and Lagrangian method, the problem is solved and some closed-form solutions are derived to obtain the optimal solution. For the price-incentive scheme, an AoI-price utility function is defined and a Stackelberg game is established to maximize the utility of the HAP-IoT device pair. By using function transformation and Lagrange method, some semi-closed-form solutions are derived to maximize their own profits of the HAP and WNs in a distributed way. Numerical results show that our proposed two incentive mechanisms are able to achieve higher network utility values than the benchmark scheme. The more the number of WNs, the lower the AoI of each status update packet and the higher utility value of the HAP. It also shows that by positioning WNs closer to the IoT device, the better per-packet AoI performance can be achieved by both incentive mechanisms. Additionally, for the energy-incentive mechanism, its achieved AoI gain and energy gain decrease with the increment of the distance between the HAP and the IoT device. But for the price-incentive mechanism, the opposite phenomenon is observed.

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp is bottlenecked by the worst channel condition among all links between the IoT devices and the access point. In this paper, a reconfigurable intelligent surface (RIS) assisted AirComp system is proposed to boost the received signal power and thus mitigate the performance bottleneck by reconfiguring the propagation channels. With an objective to minimize the AirComp distortion, we propose a joint design of AirComp transceivers and RIS phase-shifts, which however turns out to be a highly intractable non-convex programming problem. To this end, we develop a novel alternating minimization framework in conjunction with the successive convex approximation technique, which is proved to converge monotonically. To reduce the computational complexity, we transform the subproblem in each alternation as a smooth convex-concave saddle point problem, which is then tackled by proposing a Mirror-Prox method that only involves a sequence of closed-form updates. Simulations show that the computation time of the proposed algorithm can be two orders of magnitude smaller than that of the state-of-the-art algorithms, while achieving a similar distortion performance.

Power-domain non-orthogonal multiple access (NOMA) has become a promising technology to exploit the new dimension of the power domain to enhance the spectral efficiency of wireless networks. However, most existing NOMA schemes rely on the strong assumption that users’ channel gains are quite different, which may be invalid in practice. To unleash the potential of power-domain NOMA, we propose a reconfigurable intelligent surface (RIS)-empowered NOMA scheme to introduce desirable channel gain differences among the users by adjusting the phase shifts at the RIS. Our goal is to minimize the total transmit power by jointly optimizing the beamforming vectors at the base station, the phase-shift matrix at the RIS, and user ordering. To address challenge due to the highly coupled optimization variables, we present an alternating optimization framework to decompose the non-convex bi-quadratically constrained quadratic problem under a specific user ordering into two rank-one constrained matrices optimization problems via matrix lifting. To accurately detect the feasibility of the non-convex rank-one constraints and improve performance by avoiding early stopping in the alternating optimization procedure, we equivalently represent the rank-one constraint as the difference between nuclear norm and spectral norm. A difference-of-convex (DC) algorithm is further developed to solve the resulting DC programs via successive convex relaxation, followed by establishing the convergence of the proposed DC-based alternating optimization method. We further propose an efficient user ordering scheme with closed-form expressions, considering both the channel conditions and users’ target data rates. Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.

This paper investigates the relay-assisted wireless information and power transfer enabled hybrid visible light communication (VLC)-radio frequency (RF) network, where a light emitting diode (LED) access point (AP) serves multiple information users (IUs) and multiple energy harvesting users (EHUs). IUs are allowed to receive information from the LED AP through time-division-multiple-access (TDMA) manner by either the single-hop VLC-ONLY mode or the relay-assisted dual-hop VLC-RF mode, while EHUs harvest energy via the VLC links. An optimization problem is formulated to maximize the achievable information rate of IUs by jointly optimizing the access mode selection, the direct current (DC) offset at the LED AP, the peak amplitude of the alternating current (AC) component at the LED AP, the electrical power allocated to the LED AP and the power allocation at relay, subject to the energy harvesting (EH) requirement constraints of EHUs. To tackle the non-convex problem with binary variables, we first decompose it into two subproblems in terms of the two access modes. Then, the subproblems are equivalently transformed and solved by the proposed successive convex approximation (SCA)-based algorithms. Simulation results show that significant performance gain can be achieved by optimizing the DC offset. It is also observed that the area where the VLC-ONLY mode is superior to the VLC-RF mode is enlarged with the decrease of the minimal EH requirement. Besides, the achievable information rate of IUs by the VLC-RF mode first increases and then decreases with the increment of the distance between the relay and the LED AP.

Proactive caching has emerged as a promising solution to reduce the content access latency in radio access networks (RANs), thereby attracting considerable attention in the era of 6G research. It allows base stations to push popular content items to mobile users’ devices proactively. Therefore, a cached-enabled RAN may serve a user by either on-demand transmission or proactive content placement, which share a common radio spectrum. How to efficiently schedule proactive caching and on-demand transmission then becomes a challenging issue that remains open. In this paper, we present a unified framework for joint scheduling of caching and on-demand transmission. In particular, we formulate a Markovian queueing model to analyze the average delay and power consumption of the proposed scheduling policy, which are then jointly minimized via linear programming (LP). Furthermore, a low-complexity heuristic scheduling policy is conceived to strike a sub-optimal tradeoff between delay and power based on greedy algorithms. Simulation results shall demonstrate that the overall service latency of a RAN can be substantially reduced by judiciously designing joint scheduling of caching and on-demand transmission.

Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel$\ell _{3}$-norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can be made arbitrarily close to the transmitted data up to a phase-permutation ambiguity. We then propose an efficient parameter-free algorithm to solve the$\ell _{3}$-norm problem and resolve the phase-permutation ambiguity. We also derive the convergence rate in terms of key system parameters such as the number of transmitters and receivers, the channel noise power, and the channel sparsity level. Numerical experiments will show that the proposed scheme has superior performance with low computational complexity.

Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients’ private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

In this article, we consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as age upon decisions (AuD), which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, and controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision-making process. The theoretical results show that: 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and will be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrival process are periodic, the average AuD is larger, but decreases with decision rate to, the average AuD of the corresponding system with the Poisson decisions (i.e., random); and 3) when both the decision process and the arrival process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making the periodical updates and decisions with properly controlled offset is also a promising solution, if the timing information of the two processes can be accessed by the monitor.

We consider an Internet of Things (IoT)-based sensing system, in which an energy harvesting powered sensor observes the phenomenon of interest and transmits its observations to a remote monitor through a Gaussian channel. Based on the received signals, the monitor makes estimations of source signals with some distortion requirement. We measure the timeliness of the recovered signals using the Age of Information (AoI), which could be reduced by transmitting observations more frequently (i.e., with shorter intervals). We evaluate the recovery distortion with the mean-squared-error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor is quite limited, however, the frequency and power of transmissions cannot be increased at the same time. Thus, we shall investigate the timeliness-distortion tradeoff of the system by minimizing the average weighted sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we investigate the offline optimization of the system and propose a backward water-filling-based power allocation scheme, as well as a genetic-based joint transmission scheduling and power control algorithm. Third, we formulate the online power control as a Markov decision process (MDP) and solve the problem with an iterative algorithm, which closely approaches the tradeoff limit of the system. We show that the optimal transmit power is a monotonic and bivalued function of current AoI and distortion. Finally, we present our results via simulations and extend the results on the save-and-transmit policy to fading sensing systems.

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV's trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV's CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV's computing energy can also be greatly reduced.

Heavy data load and wide cover range have always been crucial problems for big data processing in Internet of Things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this article, we propose a three-layer online data processing network based on the MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this article, we propose an online edge processing scheduling algorithm based on Lyapunov optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of a high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in a path planning issue. In this article, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as an input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications that will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend on communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and cloud computing will not be sufficient due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. EIS will provide not only low-latency content delivery and computation services but also localized data acquisition, aggregation, and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies, and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open-research problems are identified.

Data-intensive clusters increasingly rely on in-memory storages to improve I/O performance. However, the routinely observed file popularity skew and load imbalance create hot spots, which significantly degrade the benefits of in- memory caching. Common approaches to tame load imbalance include copying multiple replicas of hot files and creating parity chunks using storage codes. Yet, these techniques either suffer from high memory overhead due to cache redundancy or incur non-trivial encoding/decoding complexity. In this paper, we propose an effective approach to achieve load balancing without cache redundancy or encoding/decoding overhead. Our solution, termed SP-Cache,selectively partitionsfiles based on the loads they contribute and evenly caches those partitions across the cluster. We develop an efficient algorithm to determine the optimal number of partitions for a hot file—too few partitions are incapable of mitigating hot spots, while too many are susceptible to stragglers. We have implemented SP-Cache atop Alluxio, a popular in-memory distributed storage system, and evaluated its performance through EC2 deployment and trace-driven simulations. SP-Cache can quickly react to the changing load by dynamically re-balancing cache servers. Compared to the state-of-the-art solution, SP-Cache reduces the file access latency by up to 40 percent in both the mean and the tail, using 40 percent less memory.

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice.

This paper studies the secrecy energy efficiency (SEE) for MISO power-splitting (PS) SWIPT networks in the presence of multiple passive eavesdroppers (Eves), where the non-linear energy harvesting (EH) model and the dual-layer PS receiver architecture are employed. With only channel distribution information (CDI) of Eves known and the artificial noise (AN) embedded into the transmit signals at the transmitter, a SEE maximization problem is formulated under constraints of the minimal rate and EH requirements of legitimate receivers and the power budget at the transmitter. To tackle the difficulty caused by the fractional objective function and the probability constraints in solving the considered problem, the second-layer PS ratios are firstly optimized by bisection and sum-of-ratios maximization methods, and then the transmit beamforming vectors, the AN covariance matrix and the first-layer PS ratios are jointly optimized by using successive convex approximation (SCA) and Dinkelbach's methods. The proposed solution approach is theoretically proved to converge to a stationary point of the SDR form of the considered problem, which is further shown to be the optimal one. Numerical results show that our proposed design achieves the highest SEE over traditional power minimization and secrecy rate maximization designs. Moreover, when the rate requirement is larger than a threshold or the available power is less than a threshold, traditional power minimization design or secrecy rate maximization design is able to achieve a similar SEE to our proposed design. Besides, the dual-layer PS receiver architecture is able to improve the EH efficiency and system SEE.

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as thetask mismatchproblem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely,imitation learning. To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, namedLORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additionalunlabeledtraining samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, edge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed, where distributed raw data are collected and processed by edge servers. The edge servers are supposed to split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of the limited edge computation capability, part of the raw data may be directly transmitted to the cloud. To manage limited resources in an online manner, we propose an algorithm based on Lyapunov optimization, which jointly optimizes the policy involving edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay while saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.

This paper investigates the efficient cooperation in wireless-powered communication networks, where an access point (AP) and multiple helpers first together charge up a sensor via radio-frequency (RF)-based wireless power transfer (WPT), and then the sensor uses the harvested energy to transmit real-time status updates to the AP. Due to the selfishness of the helpers, payment is provided as an incentive to them by the sensor-AP communication pair. For such a system, a Stackelberg game approach is designed to establish efficient cooperation between the helpers and the sensor-AP communication pair. An Age of Information (AoI)-based utility and a profit-based utility are defined for the sensor-AP pair and the helpers, respectively. Optimization problems are formulated to maximize their utilities. An explicit expression of the optimal transmit power of the helper is derived, with which a Dinkelbach's Programming (DP)-based algorithm is designed to jointly find the optimal payment price and transmit power at the AP, and at the same time, the Stackelberg equilibrium (SE) is achieved. Moreover, a closed-form expression of the minimum AoI of the system is also presented. Numerical results show that the more helpers there exist, the higher payment price the AP should fix, and meanwhile, the lower AoI of the sensor-AP communication pair can be achieved. Besides, it is shown that by increasing the transmit power at the AP, the optimal average AoI could not be reduced evidently.

Effective resource allocation plays a pivotal role in wireless networks. Unfortunately, typical resource allocation problems are mixed-integer nonlinear programming (MINLP) problems, which are NP-hard. Machine learning based methods recently emerge as a disruptive way to obtain near-optimal performance for MINLP problems with affordable computational complexity. However, they suffer from severe performance deterioration when the network parameters change, which commonly happens in practice and can be characterized as the task mismatch issue. In this paper, we propose a transfer learning method via self-imitation, to address this issue for effective resource allocation in wireless networks. It is based on a general “learning to optimize” framework for solving MINLP problems. A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems. Numerical experiments demonstrate that the proposed method, with much less training time, achieves comparable performance with the model trained from scratch based on sufficient labeled samples. To the best of our knowledge, this is the first work that applies transfer learning for resource allocation in wireless networks.

Cluster caching has been increasingly deployed in front of cloud storage to improve I/O performance. In shared, multi-tenant environments such as cloud datacenters, cluster caches are constantly contended by many users. Enforcing performance isolation between users hence becomes imperative to cluster caching. A user's caching performance critically depends on two factors: (1) the amount of cache allocation and (2) the load of servers in which its files are cached. However, existing cache sharing policies only provide guarantees on the amount of cache allocation, while remaining agnostic to the load of cache servers. Consequently, "mice" users having files co-located with "elephants" contributing heavy data accesses may experience extremely long latency, hence receiving no isolation. In this paper, we propose a Load-Aware Cache Sharing scheme (LACS) to enforce isolation between users. LACS keeps track of the load contributed by each user and reins back the congestions caused by elephant users by throttling their cache usage and network bandwidth. We have implemented LACS atop Alluxio, a popular cluster caching system. EC2 deployment shows that LACS achieves performance isolation in the presence of elephants, while improving the mean read latency by up to 80.4% (25.3% on average) over the state-of-the-art load balancing technique.

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with the increment of time between two received updates. Due to EH, a waiting time is required at the sensor before transmitting the status update. An optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. A two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and the inner loop presents a semi-closed-form expression of the optimal waiting time policy based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results show that our proposed optimal transmission policy outperforms the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are in slight load. It also shows that the system U-AoI first decreases and then keeps unchanged with the increments of EH circuit's saturation level.

Cellular networks are promising to support effective wireless communications for unmanned aerial vehicles (UAVs), which will help to enable various long-range UAV applications. However, these networks are optimized for terrestrial users, and thus do not guarantee seamless aerial coverage. In this paper, we propose to overcome this difficulty by exploiting controllable mobility of UAVs, and investigate connectivity-aware UAV path planning. To explicitly impose communication requirements on UAV path planning, we introduce two new metrics to quantify the cellular connectivity quality of a UAV path. Moreover, aerial coverage maps are used to provide accurate locations of scattered coverage holes in the complicated propagation environment. We formulate the UAV path planning problem as finding the shortest path subject to connectivity constraints. Based on graph search methods, a novel connectivity-aware path planning algorithm with low complexity is proposed. The effectiveness and superiority of our proposed algorithm are demonstrated using the aerial coverage map of an urban section in Virginia, which is built by ray tracing. Simulation results also illustrate a tradeoff between the path length and connectivity quality of UAVs.

Massive device connectivity is a crucial communication challenge for Internet of Things (IoT) networks, which consist of a large number of devices with sporadic traffic. In each coherence block, the serving base station needs to identify the active devices and estimate their channel state information for effective communication. By exploiting the sparsity pattern of data transmission, we develop a structured group sparsity estimation method to simultaneously detect the active devices and estimate the corresponding channels. This method significantly reduces the signature sequence length while supporting massive IoT access. To determine the optimal signature sequence length, we study the phase transition behavior of the group sparsity estimation problem. Specifically, user activity can be successfully estimated with a high probability when the signature sequence length exceeds a threshold; otherwise, it fails with a high probability. The location and width of the phase transition region are characterized via the theory of conic integral geometry. We further develop a smoothing method to solve the high-dimensional structured estimation problem with a given limited time budget. This is achieved by sharply characterizing the convergence rate in terms of the smoothing parameter, signature sequence length and estimation accuracy, yielding a tradeoff between the estimation accuracy and computational cost. Numerical results are provided to illustrate the accuracy of our theoretical results and the benefits of smoothing techniques.

This paper investigates a fog computing-assisted multiuser simultaneous wireless information and power transfer network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multiuser scheduling, the time assignment, the sensors' transmit powers, and the PS ratios. Since, the problem is a mixed integer programming problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions, and theoretical analysis. For a scheduled sensor, the closed-form and semi-closed-form solutions to achieve its minimal required energy are derived, and then an efficient multiuser scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server, the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

This paper considers an MISO simultaneous wireless information and power transfer (SWIPT) system, where one transmitter serves multiple authorized receivers in the presence of several potential eavesdroppers (idle receivers). To prevent the information interception by eavesdroppers, artificial noise (AN) is embedded into the transmit signals. The non-linear energy harvesting (EH) model is adopted and a novel power-splitting (PS) EH receiver architecture is proposed. Stochastic uncertainty channel model (SUM) is considered for the idle receivers due to outdated channel feedback. A global energy efficiency (GEE) maximization problem is formulated by jointly optimizing the transmit beamforming vectors, the AN covariance matrix, and the PS ratios, under the minimal rate and secure transmission constraints of authorized receivers, the EH requirement constraints of idle receivers, and the total available power constraint at the transmitter. Since the problem is non-convex with no known solution, it is solved based on the following solution framework. Firstly, the PS ratios are optimized by using the bisection method and successive convex approximation (SCA), and then, the transmit beamforming vectors and the AN covariance matrix are jointly optimized by using a Dinkelbach's Algorithm based method, where SCA is applied to solve its inner subproblem. It is theoretically proved that by involving AN, the system GEE can be improved. Numerous results show that system GEE first increases and then keeps unchanged with the increment of the total available power, but it first keeps unchanged and then decreases with the increment of the minimal rate requirement. It is also observed that compared with traditional EH receiver architecture and linear EH model, our proposed PS EH receiver architecture is able to achieve higher GEE and avoid false output power at idle receivers.

Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming.

To exploit the potential of content caching and device-to-device (D2D) communication, we propose a user-centric joint device caching and channel assignment (DCA) policy to facilitate content exchanges between user equipments (UEs). The objective is to minimize the average content delivery delay by effectively leveraging D2D communications using as few channels as possible, subject to the UEs' cache capacities and availability of D2D links. This joint design problem is formulated as a nonlinear combinatorial optimization problem which is NP-hard. We first analyze the optimal DCA policy in two special cases. Then, a low-complexity heuristic algorithm is proposed for general cases which alternatively performs greedy device caching and graphcoloring based channel allocating. Simulation results show that the proposed DCA policy can reduce the average content delivery delay by more than half, in contrast to baseline schemes with locally popular caching.

We study the fair cache allocation problem in shared cloud environments, where many users and applications contend for the main memory to cache shared datasets or files. Unlike other resources such as CPUs and networks, in-memory caches can be non-exclusively shared across many users, e.g., a cached columnar dataset queried by many Spark SQL jobs. This results in a unique challenge of the "free-riding" problem, where a user lies about its caching preferences to trick other users to cache files for it, using their allocated cache space. We show that existing cache allocation policies either suffer from such manipulations or result in poor efficiency. To address this problem, we propose a new cache allocation algorithm, termed OpuS, or Opportunistic Sharing for high efficiency. We show that OpuS provides performance isolation between users and is strategy-proof against "free-riding" manipulations. We have implemented OpuS as a pluggable cache manager in Alluxio, a popular memory-centric filesystem. Cluster deployment and trace-driven simulations demonstrate that OpuS allocates each user a fair share of caches while achieving near-optimal efficiency in cache utilization.

This paper investigates the artificial noise (AN)-aided multi-cell coordinated beamforming (MCBF) for secure simultaneous wireless information and power transfer in both centralized and distributed manners. The proposed transmit design is formulated into a power-minimization problem to guarantee the authorized users' information and energy harvesting (EH) requirements while avoiding the information interception by unauthorized users. Power splitting receiver architecture and the non-linear EH model are employed. Both perfect and imperfect channel state information (CSI) cases are considered. For the perfect CSI case, the non-robust design is presented by applying semi-definition relaxation (SDR). When no user harvests energy, the global optimum is guaranteed, and when some users harvest energy, approximate global optimum is achieved. For the imperfect CSI case, the worst-case robust design under the deterministic uncertainty channel model is studied, where a solving approach based on SDR and S-procedure is proposed, and the statistically robust design under the stochastic uncertainty channel model is also studied, where an upper bound to the global optimum is obtained by using SDR and Bernstein-type inequality. We further propose a distributed AN-aided MCBF design framework by using an alternating direction method of multipliers for the non-robust, worst-case robust, and statistically robust designs, with which each BS is able to optimize its own transmit design with the local CSI. Simulation results demonstrate our theoretical analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It also shows that employing the non-linear EH model is able to avoid false output power and save power consumption at the BSs.

The storage and the transmission of messages in big data are discussed in this paper, where message importance is taken into account. To this end, we propose to use non-parametric message importance measure (NMIM) as a measure of message importance, which can characterize the uncertainty of random events like Shannon entropy and Rényi entropy. We prove that NMIM sufficiently describes the two key characters of big data, i.e., the rare events finding and the large diversities of events. Based on NMIM, we then propose an effective compressed encoding mode for data storage, and discuss the transmission of messages over some typical channel models with limited message importance loss. Our numerical results show that the proposed strategy occupies less storage space without losing too much important information, and the maximum received entropy rate increases with the increasing of message importance loss until it reaches saturation, which contributes to designing of better practical communication system.

Dense cloud radio access networks (cloud-RANs) provide a promising way to enable scalable connectivity and handle diversified service requirements for massive mobile devices. To fully exploit the performance gains of dense cloud-RANs, channel state information of both the signal link and interference links is required. However, with limited radio resources for training, the channel estimation problem in dense cloud-RANs becomes a high-dimensional estimation problem, i.e., the number of measurements will be typically smaller than the dimension of the channel. In this paper, we shall develop a generic high-dimensional structured channel estimation framework for dense cloud-RANs, which is based on a convex structured regularizing formulation. Observing that the wireless channel possesses ample exploitable statistical characteristics, we propose to convert the available spatial and temporal prior information into appropriate convex regularizers. Simulation results demonstrate that exploiting the spatial and temporal dynamics can achieve good estimation performance even with limited training resources. The alternating direction method of multipliers algorithm is further adopted to solve the resultant large-scale high-dimensional channel estimation problems. The proposed framework thus enjoys modeling flexibility, low training overhead, and computation cost scalability.

This paper considers the placement of unmanned aerial vehicle base stations (UAV-BSs) with criterion of minimum UAV-recall-frequency (UAV-RF), indicating the energy efficiency of mobile UAVs networks. Several different power consumptions, including signal transmit power, on-board circuit power and the power for UAVs mobility, and the ground user density are taken into account. Instead of conventional empirical stochastic models, this paper utilizes a pattern formation system to track the instable and non-ergodic time-varying nature of user density. We show that for a single time-slot, the optimal placement is achieved when the transmit power of UAV-BSs equals their on-board circuit power. Then, for multiple time-slot duration, we prove that the optimal placement updating problem is an integer nonlinear programming coupled with an inherent integer linear programming. Since the original problem is NP-hard and cannot be solved with conventional recursive methods, we propose a sequential-Markov-greedy-decision strategy to achieve near minimal UAV-RF in polynomial time. Furthermore, we prove that the increment of UAV-RF caused by inaccurate predicted user density is proportional to the generalization error of learned patterns. Here, in regions with large area, high-rise buildings, or low user density, large sample sets are required for effective pattern formation.

This paper investigates the secure transmit design for simultaneous wireless information and power transfer system under the non-linear energy harvesting (EH) model, where a transmitter sends confidential information and transfers energy to multiple information receivers (IRs) and EH receivers (ERs) with the existence of multiple eavesdroppers (Eves). To prevent confidential information leakage, multiple artificial redundant signals (MARSs) are embedded in the transmit signals. The goal is to minimize the total transmit power by jointly optimizing transmit beamforming vectors and the covariance matrixes of MARSs, such that the minimal information rate and EH requirements at IRs and ERs are guaranteed while making the received signal-to-Interference ratio at ERs and Eves lower than their information decoding thresholds. Both the non-robust and the robust designs are studied. For the non-robust design, the optimal solution is derived. For the robust design, an approximate optimal solution is obtained by using Gaussian randomization procedure. Simulation results show that compared with traditional non-MARS-aided beamforming design, our proposed design is superior in terms of the total required transmit power. It also shows that employing the non-linear EH model can avoid false output power at the ERs and/or save power at the transmitter.

Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks, which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed ℓp-minimization approach with the iterative reweighted-ℓ2algorithm to return a Karush-Kuhn- Tucker (KKT) point solution, as well as enhance the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closedform solutions at each iteration to reduce the computational complexity. The well-structured solutions provide opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information, which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed ℓp-minimization approach, as well as the effectiveness of the large system analysis-based framework for computing the RRH ordering criterion.

Caching popular content at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technology for effective mobile content delivery. User mobility is an important factor when investigating such networks, which unfortunately was largely ignored in most previous works. Preliminary studies have been carried out but the effect of mobility on the caching performance has not been fully understood. In this paper, by explicitly considering users’ contact and inter-contact durations via an alternating renewal process, we first investigate the effect of mobility with a given cache placement. A tractable expression of the data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is derived, which is proved to be increasing with the user moving speed. The analytical results are then used to develop an effective mobility-aware caching strategy to maximize the data offloading ratio. Simulation results are provided to confirm the accuracy of the analytical results and also validate the effect of user mobility. Performance gains of the proposed mobility-aware caching strategy are demonstrated with both stochastic models and real-life data sets. It is observed that the information of the contact durations is critical to design cache placement, especially when they are relatively short or comparable to the inter-contact durations.

Densifying networks and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, the complicated distributions of the signal power and the accumulated interference power, largely induced by various space-time processing techniques, make it highly challenging to quantitatively characterize the performance of multi-antenna networks. In this paper, using tools from stochastic geometry, a unified framework is developed for the analysis of such networks. The major results are two innovative representations of the coverage probability, which make the analysis of multi-antenna networks almost as tractable as the single-antenna case. One is expressed as an ℓ1-induced norm of a Toeplitz matrix, and the other is given in a finite sum form. With a compact representation, the former incorporates many existing analytical results on single- and multi-antenna networks as special cases and leads to tractable expressions for evaluating the coverage probability in both ad hoc and cellular networks. While the latter is more complicated for numerical evaluation, it helps analytically gain key design insights. In particular, it helps prove that the coverage probability of ad hoc networks is a monotonically decreasing convex function of the transmitter density and that there exists a peak value of the coverage improvement when increasing the number of transmit antennas. On the other hand, in multi-antenna cellular networks, it is shown that the coverage probability is independent of the transmitter density and that the outage probability decreases exponentially as the number of transmit antennas increases.

Single-carrier frequency-domain equalization (SC-FDE) with multi-input multi-output (MIMO) has been recognized as an alternative technology to orthogonal frequency-division multiplexing with MIMO for broadband wireless communication systems because of its single carrier transmission advantages. Conventional SC-FDE MIMO schemes are designed under the assumption of perfect channel estimation. In this paper, we investigate the robust SC-FDE MIMO design for systems with imperfect channel estimation. Based on a statistical model for channel estimation, the optimal equalization coefficients for the robust SC-FDE MIMO schemes with both parallel interference cancellation and successive interference cancellation (SIC) are derived with the objective of minimizing the sum of the multiple data streams' mean square errors (sum-MSE). We propose an optimal ordering algorithm in the sense of minimum sum-MSE for the SC-FDE MIMO scheme with SIC, and further propose a low complexity sub-optimal ordering algorithm. The bit-error-rate (BER) performance in the uncoded case is analyzed and a tight BER approximation is derived. Numerical results show that the proposed robust SC-FDE MIMO schemes achieve 1.5dB~4dB gains in Eb/Noover the conventional non-robust schemes.

Memory caches are being aggressively used in today's data- parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency- or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy.

Hybrid precoding has been recently proposed as a cost- effective transceiver solution for millimeter wave (mm- wave) systems. The analog component in such precoders, which is composed of a phase shifter network, is the key differentiating element in contrast to conventional fully digital precoders. While a large number of phase shifters with unquantized phases are commonly assumed in existing works, in practice the phase shifters should be discretized with a coarse quantization, and their number should be reduced to a minimum due to cost and power consideration. In this paper, we propose a new hybrid precoder implementation using a small number of phase shifters with quantized and fixed phases, i.e., a fixed phase shifter (FPS) implementation, which significantly reduces the cost and hardware complexity. In addition, a dynamic switch network is proposed to enhance the spectral efficiency. Based on the proposed FPS implementation, an effective alternating minimization (AltMin) algorithm is developed with closed-form solutions in each iteration. Simulation results show that the proposed algorithm with the FPS implementation outperforms existing ones. More importantly, it needs much fewer phase shifters than existing hybrid precoder proposals, e.g., around 10 fixed phase shifters are sufficient for practically relevant system settings.

In this paper, we shall develop a generic channel estimation framework based on the convex formulation for dense cloud radio access networks (Cloud-RAN). Due to the training resource constraint and the large number of transmit antennas, the pilot length is smaller than the antenna number, and thus channel estimation becomes an ill-posed inverse problem. By observing that the wireless channel possesses ample exploitable statistical characteristics, we propose to convert the available spatial and temporal prior information into appropriate convex regularizing functions, yielding convex optimization formulations for the underdetermined channel estimation problem. Simulation results demonstrate that exploiting the prior information of large-scale fading and temporal correlation can achieve good estimation performance even with limited training resources. The alternating direction method of multipliers (ADMM) algorithm is further adopted to solve the resultant large-scale channel estimation problems. The proposed framework is, therefore, scalable to the overhead of prior information and the computation cost for large network sizes.

Caching at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technique to accommodate the exponentially increasing mobile data traffic. While most previous works ignored user mobility, there are some recent works taking it into account. However, the duration of user contact times has been ignored, making it difficult to explicitly characterize the effect of mobility. In this paper, we adopt the alternating renewal process to model the duration of both the contact and inter-contact times, and investigate how the caching performance is affected by mobility. The data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is taken as the performance metric. We first approximate the distribution of the communication time for a given user by beta distribution through moment matching. With this approximation, an accurate expression of the data offloading ratio is derived. For the homogeneous case where the average contact and intercontact times of different user pairs are identical, we prove that the data offloading ratio increases with the user moving speed, assuming that the transmission rate remains the same. Simulation results are provided to show the accuracy of the approximate result, and also validate the effect of user mobility.

Densifying the network and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, due to the complicated distributions of random signal and interference channel gains, largely induced by various space-time processing techniques, it is highly challenging to quantitatively characterize the performance of dense multi-antenna networks. In this paper, using tools from stochastic geometry, a tractable framework is proposed for the analytical evaluation of such networks. The major result is an innovative representation of the coverage probability, as an induced ℓ1-norm of a Toeplitz matrix. This compact representation incorporates lots of existing analytical results on single-and multi-antenna networks as special cases, and its evaluation is almost as simple as the single-antenna case with Rayleigh fading. To illustrate its effectiveness, we apply the proposed framework to investigate two kinds of prevalent dense wireless networks, i.e., physical layer security aware networks and millimeter-wave networks. In both examples, in addition to tractable analytical results of relevant performance metrics, insightful design guidelines are also analytically obtained.

Memory caches are being aggressively used in today's data-parallel systems such as Spark, Tez, and Piccolo. However, prevalent systems employ rather simple cache management policies — notably the Least Recently Used (LRU) policy — that are oblivious to the application semantics of data dependency, expressed as a directed acyclic graph (DAG). Without this knowledge, memory caching can at best be performed by “guessing” the future data access patterns based on historical information (e.g., the access recency and/or frequency), which frequently results in inefficient, erroneous caching with low hit ratio and a long response time. In this paper, we propose a novel cache replacement policy, Least Reference Count (LRC), which exploits the application-specific DAG information to optimize the cache management. LRC evicts the cached data blocks whose reference count is the smallest. The reference count is defined, for each data block, as the number of dependent child blocks that have not been computed yet. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, compared with LRU, LRC speeds up typical applications by 60%.

To enhance the computation capability of mobile devices by offloading computation-demanding tasks to the nearby edge servers. In order to minimize the task latency and the device energy consumption, in this paper, we investigate the multi-objective resource allocation for multi-user MEC systems by adopting the system utility as the performance metric, which is a normalized weighted combination of the time and energy saving achieved by computation offloading. To provide an efficient solution, a low-complexity ranking-based algorithm is proposed based on the modified Newton method and the concept of computation offloading priority. Simulation results show that our proposed algorithm achieves a near-optimal performance and greatly outperforms a baseline algorithm with random spectrum allocation. In addition, it is demonstrated that jointly optimizing the spectrum and computational resource management policy is more critical when the number of MEC users is large.

Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task off loading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.

Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of the defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation from the performance with actual antenna patterns. In this paper, using tools from stochastic geometry, we carry out a comprehensive investigation on the impact of directional antenna arrays in mm-wave networks. We first present a general and tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns. It is then applied to mm-wave ad hoc and cellular networks, where two sophisticated antenna patterns with desirable accuracy and analytical tractability are proposed to approximate the actual antenna pattern. Compared with previous works, the proposed approximate antenna patterns help to obtain more insights on the role of directional antenna arrays in mm-wave networks. In particular, it is shown that the coverage probabilities of both types of networks increase as a non-decreasing concave function with the antenna array size. The analytical results are verified to be effective and reliable through simulations, and numerical results also show that large-scale antenna arrays are required for satisfactory coverage in mm-wave networks.

The exponential growth of mobile data traffic is driving the deployment of dense wireless networks, which will not only impose heavy backhaul burdens, but also generate considerable power consumption. Introducing caches to the wireless network edge is a potential and cost-effective solution to address these challenges. In this paper, we will investigate the problem of minimizing the network power consumption of cache-enabled wireless networks, consisting of the base station (BS) and backhaul power consumption. The objective is to develop efficient algorithms that unify adaptive BS selection, backhaul content assignment, and multicast beamforming, while taking account of user QoS requirements and backhaul capacity limitations. To address the NP-hardness of the network power minimization problem, we first propose a generalized layered group sparse beamforming (LGSBF) modeling framework, which helps to reveal the layered sparsity structure in the beamformers. By adopting the reweighted ℓ1/ℓ2-norm technique, we further develop a convex approximation procedure for the LGSBF problem, followed by a three-stage iterative LGSBF framework to induce the desired sparsity structure in the beamformers. Simulation results validate the effectiveness of the proposed algorithm in reducing the network power consumption, and demonstrate that caching plays a more significant role in networks with higher user densities and less power-efficient backhaul links.

This paper investigates the optimal resource allocation in wireless powered communication network with user cooperation, where two single-antenna users first harvest energy from the signals transmitted by a multi-antenna hybrid access point (H-AP) and then cooperatively send information to the H-AP using their harvested energy. To explore the system information transmission performance limit, an optimization problem is formulated to maximize the weighted sum-rate (WSR) by jointly optimizing energy beamforming vector, time assignment, and power allocation. Besides, another optimization problem is also formulated to minimize the total transmission time for given amount of data required to be transmitted at the two sources. Because both problems are non-convex, we first transform them to be convex by using proper variable substitutions and then apply semi-definite relaxation to solve them. We theoretically prove that our proposed methods guarantee the global optimum of both problems. Simulation results show that system WSR and transmission time can be significantly enhanced by using energy beamforming and user cooperation. It is observed that when the total amount of information of two users is fixed, with the increase of the information amount of the user relatively farther away from the H-AP, the transmission time of the user cooperation scheme decreases while that of the direct transmission increases. Besides, the effects of user position on the system performances are also discussed, which provides some useful insights.

To deal with the rapid growth of high-speed and/or ultra-low latency data traffic for massive mobile users, fog radio access networks (Fog-RANs) have emerged as a promising architecture for next-generation wireless networks. In Fog-RANs, the edge nodes and user terminals possess storage, computation and communication functionalities to various degrees, which provide high flexibility for network operation, i.e., from fully centralized to fully distributed operation. In this paper, we study the cache placement problem in Fog-RANs, by taking into account flexible physical-layer transmission schemes and diverse content preferences of different users. We develop both centralized and distributed transmission aware cache placement strategies to minimize users' average download delay subject to the storage capacity constraints. In the centralized mode, the cache placement problem is transformed into a matroid constrained submodular maximization problem, and an approximation algorithm is proposed to find a solution within a constant factor to the optimum. In the distributed mode, a belief propagation-based distributed algorithm is proposed to provide a suboptimal solution, with iterative updates at each BS based on locally collected information. Simulation results show that by exploiting caching and cooperation gains, the proposed transmission aware caching algorithms can greatly reduce the users' average download delay.

In this paper, we study the delay optimal scheduling policy for a multi-state wireless fading channel, by taking bursty packet arrivals and automatic repeat request-based packet transmission into account. In our system, the average delay each packet experiences includes the time it waits in the queue and the time it may take to retransmit due to packet delivery failure. To reduce the average delay, we propose a joint channel aware and queue-aware stochastic scheduling policy to determine whether and with which probability the source should transmit based on channel and buffer states, subject to an average power constraint at the transmitter. To find the optimal scheduling probabilities, we formulate a non-linear power-constrained delay minimization problem with the aid of controlled Markov decision processes. The optimization problem is then converted into an equivalent linear programming problem by introducing new variables from the steady-state probabilities of the underlying Markov chain and transmission probabilities. By analyzing its property, we derive the structure of the optimal solution, and exploit it to obtain the optimal probabilities analytically. It is found that the optimal scheduling policy has a double threshold structure, and can significantly reduce the average delay.

Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an [O (1/V) , O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.

Caching at mobile devices can facilitate device-to-device (D2D) communications, which may significantly improve spectrum efficiency and alleviate the heavy burden on backhaul links. However, most previous works ignored user mobility, thus having limited practical applications. In this paper, we take advantage of the user mobility pattern by the inter-contact times between different users, and propose a mobility-aware caching placement strategy to maximize thedata offloading ratio, which is defined as the percentage of the requested data that can be delivered via D2D links rather than through base stations. Given the NP-hard caching placement problem, we first propose an optimal dynamic programming algorithm to obtain a performance benchmark with much lower complexity than exhaustive search. We then prove that the problem falls in the category of monotone submodular maximization over a matroid constraint, and propose a time-efficient greedy algorithm, which achieves an approximation ratio as$\frac {1}{2}$. Simulation results with real-life data sets will validate the effectiveness of our proposed mobility-aware caching placement strategy. We observe that users moving at either a very low or very high speed should cache the most popular files, while users moving at a medium speed should cache less popular files to avoid duplication.

This paper considers a wireless powered communication network (WPCN) with group cooperation, where two communication groups cooperate with each other via wireless power transfer and time sharing to fulfill their expected information delivering and achieve “win-win” collaboration. To explore the system performance limits, we formulate optimization problems to maximize the weighted sum-rate (WSR) and minimize the total consumed power. The time assignment, beamforming vector and power allocation are jointly optimized under available power and quality of service requirement constraints of both the groups. For the WSR-maximization, both fixed and flexible power scenarios are investigated. As all problems are non-convex and have no known solution methods, we solve them by using proper variable substitutions and the semi-definite relaxation. We theoretically prove that our proposed solution method guarantees the global optimum for each problem. Numerical results are presented to show the system performance behaviors, which provide some useful insights for future WPCN design. It shows that in such a group cooperation-aware WPCN, optimal time assignment has the greatest effect on the system performance than other factors.

Highly mobile wireless communication attracts much more attention currently due to the rapid development of high speed railways (HSRs) all over the world. Although the single train scenario has been well studied by now, two trains encountering scenario over the general two-way railways is also an important problem deserving to investigate. To this end, this paper concentrates on the uplink information transmission of HSRs in the two trains encountering scenario, which is modeled as a time- varying partial multiple access channel. In order to evaluate the transmission performance, the achievable rate region is utilized as a metric to characterize the tradeoff between the rates that each train can obtain under limited channel source constraint. With the help of superposition modulation and sequential interference cancelling, an optimal adaptive power allocation scheme aided by real-time position information is proposed to achieve the maximal boundary of the achievable rate region, namely alleviating the effect of encountering on information transmission to the largest extent. According to the numerical results, great improvement can be obtained by new proposed adaptive power allocation along time.

Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Specifically, at each time slot, the optimal frequencies of the local CPUs are obtained in closed forms, while the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method. Performance analysis is conducted for the proposed algorithm, which indicates that the power consumption and execution delay obeys an [0(1/V), 0(V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters to the system performance.

In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet, so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource - as most existing works did - is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving.

As a promising technique to meet the drastically growing demand for both high throughput and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input multiple-output (MIMO) systems have attracted significant attention in recent years. However, in massive MIMO systems, as the density of mobile users (MUs) increases, conventional uplink training methods will incur prohibitively high training overhead, which is proportional to the number of MUs. In this paper, we propose a selective uplink training method for massive MIMO systems, where in each channel block only part of the MUs will send uplink pilots for channel training, and the channel states of the remaining MUs are predicted from the estimates in previous blocks, taking advantage of the channels' temporal correlation. We propose an efficient algorithm to dynamically select the MUs to be trained within each block and determine the optimal uplink training length. Simulation results show that the proposed training method provides significant throughput gains compared to the existing methods, while much lower estimation complexity is achieved. It is observed that the throughput gain becomes higher as the MU density increases.

Caching popular contents at the edge of wireless networks has recently emerged as a promising technology to improve the quality of service for mobile users, while balancing the peak-to-average transmissions over backhaul links. In contrast to existing works, where a central coordinator is required to design the cache placement strategy, we consider a distributed caching problem which is highly relevant in dense network settings. In the considered scenario, each Base Station (BS) has a cache storage of finite capacity, and each user will be served by one or multiple BSs depending on the employed transmission scheme. A belief propagation based distributed algorithm is proposed to solve the cache placement problem, where the parallel computations are performed by individual BSs based on limited local information and very few messages passed between neighboring BSs. Thus, no central coordinator is required to collect the information of the whole network, which significantly saves signaling overhead. Simulation results show that the proposed low-complexity distributed algorithm can greatly reduce the average download delay by collaborative caching and transmissions.

Caching popular content at base stations is a powerful supplement to existing limited backhaul links for accommodating the exponentially increasing mobile data traffic. Given the limited cache budget, we investigate the cache size allocation problem in cellular networks to maximize the user success probability (USP), taking wireless channel statistics, backhaul capacities and file popularity distributions into consideration. The USP is defined as the probability that one user can successfully download its requested file either from the local cache or via the backhaul link. We first consider a single-cell scenario and derive a closed-form expression for the USP, which helps reveal the impacts of various parameters, such as the file popularity distribution. More specifically, for a highly concentrated file popularity distribution, the required cache size is independent of the total number of files, while for a less concentrated file popularity distribution, the required cache size is in linear relation to the total number of files. Furthermore, we study the multi-cell scenario, and provide a bisection search algorithm to find the optimal cache size allocation. The optimal cache size allocation is verified by simulations, and it is shown to play a more significant role when the file popularity distribution is less concentrated.

Underlaying device-to-device (D2D) communications to a cellular network is considered as a key technique to improve spectral efficiency in 5G networks. For such D2D systems, mode selection and resource allocation have been widely utilized for managing interference. However, previous works allowed at most one D2D link to access the same channel, while mode selection and resource allocation are typically separately designed. In this paper, we jointly optimize the mode selection and channel assignment in a cellular network with underlaying D2D communications, where multiple D2D links may share the same channel. Meanwhile, the QoS requirements for both cellular and D2D links are guaranteed, in terms of Signal-to-Interference-plus-Noise Ratio (SINR). We first propose an optimal dynamic programming (DP) algorithm, which provides a much lower computation complexity compared to exhaustive search and serves as the performance bench mark. A bipartite graph based greedy algorithm is then proposed to achieve a polynomial time complexity. Simulation results will demonstrate the advantage of allowing each channel to be accessed by multiple D2D links in dense D2D networks, as well as, the effectiveness of the proposed algorithms.

Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.

In this paper, we develop a statistical group sparse beamforming framework to minimize the network power consumption for green cloud radio access networks (Cloud-RANs). It will promote group sparsity structures in the beamforming vectors, which will provide a good indicator for remote radio head (RRH) ordering to enable adaptive RRH selection for power saving. In contrast to the previous works that depend heavily on instantaneous channel state information (CSI), the proposed algorithm only depends on the long-term channel state attenuation for RRH ordering, which does not require frequent update, thereby significantly reducing the computation overhead. This is achieved by developing a smoothed ℓp-minimization approach to induce group sparsity in beamforming vectors, followed by an iterative reweighted-ℓ2algorithm via the principles of the majorization-minimization (MM) algorithm and the Lagrangian duality theory. With the well-structured closed-form solutions at each iteration, we further leverage the large-dimensional random matrix theory to derive deterministic approximations for the squared ℓ2-norm of the induced group sparse beamforming vectors in the large system regimes. The deterministic approximation results only depend on statistical CSI and will guide the RRH ordering. Simulation results demonstrate the near-optimal performance of the proposed algorithm, even in finite systems.

Automatic repeat request (ARQ) is widely used in modern communication systems to improve transmission reliability. In conventional ARQ protocols developed for systems with energy-unconstrained receivers, an acknowledgement/negative-acknowledgement (ACK/NACK) message is fed back when decoding succeeds/fails. Such kind of non-adaptive feedback consumes significant amount of energy, and thus will limit the performance of systems with energy harvesting (EH) receivers. In order to overcome this limitation and to utilize the harvested energy more efficiently, we propose a novel ARQ protocol for EH receivers, where the ACK feedback can be adapted based upon the receiver's EH state. Two conventional ARQ protocols are also considered. By adopting the packet drop probability (PDP) as the performance metric, we formulate the throughput constrained PDP minimization problem for a communication link with a non-EH transmitter and an EH receiver. Optimal reception policies including the sampling, decoding and feedback strategies, are developed for different ARQ protocols. Simulation results will show that the proposed ARQ protocol not only outperforms the conventional ARQs in terms of PDP, but can also achieve a higher throughput.

Millimeter wave (mmWave) communications has been considered as a promising technology for 5G cellular networks. Exploiting directional beamforming using antenna arrays to combat path loss is one of the defining features in mmWave cellular networks. However, previous works on mmWave network analysis usually adopt simplified antenna patterns for tractability. In this paper, we show that there are huge discrepancies between the simplified and actual antenna patterns when investigating the coverage probability of mmWave networks. Analytical expressions for the coverage probabilities are derived using tools from stochastic geometry, by considering the actual antenna pattern with the uniform linear array. Moreover, the impact of the array size is investigated, which cannot be revealed from existing results with simplified antenna patterns. Numerical results will show that large-scale antenna arrays are required for satisfactory coverage in mmWave cellular networks. Furthermore, dense mmWave cellular networks are shown to achieve much higher rate coverage than conventional sub-6 GHz cellular systems.

In this study, the caching problem is investigated. Assuming that the users are only equipped with buffer of small sizes, the peak rate of caching problem is investigated in this study. In contrast to recent results in the literature, this study shows that under some specific condition, i.e. if the number of users is no less than the amount of files in the server, a lower peak rate of caching is achievable. Furthermore, this new presented peak rate of caching is demonstrated to coincide with the well‐known cut‐set bound.

For a two-way relay network (TWRN) with three nodes, we discuss the performance optimization of digital network coding (DNC) and physical network coding (PNC) schemes under the energy harvesting (EH) constraints and peak power constraints. We also consider the energy transfer between nodes, which is referred to as energy co-operation. To find the maximal achievable performance, we first consider the case of offline scheduling, formulate the corresponding optimization problems, find the optimal solutions, as well as present some useful theoretical properties on optimality. Then we move to the online scheduling, and propose both dynamic programming and some intuitive policies to approach the performance of its offline counterpart. Numerical results show that PNC outperforms DNC due to the higher spectrum efficiency and the intrinsic coding gains, under the same conditions. Furthermore, it is observed that if the relay harvests much more energy and shares it with the two source nodes, DNC with energy co-operation scheme has the potential to perform comparable to or even better than PNC without energy co-operation scheme, which validates the importance of energy co-operation in contemporary communication systems.

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

The cloud radio access network (Cloud-RAN) has recently been proposed as one of the cost-effective and energy-efficient techniques for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems, which call for adaptive remote radio head (RRH) selection and user admission. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the ℓ0-norm-based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but nonsmooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed ℓ1/ℓ2-norm relaxation, we adopt the nonconvex but smoothed ℓp-minimization (02algorithm is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the relaxed smoothed ℓp-minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast cloud-RAN.

This paper investigates the energy efficiency (EE) in multiple relay-aided OFDM systems, where decode-and-forward (DF) relay beamforming is employed to help the information transmission. In order to explore the system performance behavior with user fairness for such a system, an optimization problem is formulated to maximize the EE by jointly considering multiple factors, i.e., the transmission mode selection (DF relay beamforming or direct-link transmission), the helping relay set selection, the subcarrier assignment and the power allocation at the source and relays on subcarriers, under nonlinear proportional rate fairness constraints, where both transmit power consumption and linearly rate-dependent circuit power consumption are taken into account. To solve the nonconvex optimization problem, we propose a low-complexity scheme to approximate it. Simulation results demonstrate its effectiveness. The effects of the circuit power consumption on system performance is also studied and it is observed that with either the constant or the linearly rate-dependent circuit power consumption, system EE grows with the increment of system average channel-to-noise ratio (CNR), but the growth rates show different behaviors. For the constant circuit power consumption, system EE increasing rate is an increasing function of the average CNR, while for the linearly rate-dependent one, system EE increasing rate is a decreasing function of the average CNR. This observation is very important, which indicates that by deducing the circuit dynamic power consumption per unit data rate, system EE can be greatly enhanced. Besides, we also discuss the effects of the number of users and subcarriers on the system EE performance.

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF-CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF-CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF-AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF-AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC.

We derive a general and closed-form result for the success probability in downlink multiple-antenna (MIMO) heterogeneous cellular networks (HetNets), utilizing a novel Toeplitz matrix representation. This main result, which is equivalently the signal-to-interference ratio (SIR) distribution, includes multiuser MIMO, single-user MIMO and per-tier biasing for K different tiers of randomly placed base stations (BSs), assuming zero-forcing precoding and perfect channel state information. The large SIR limit of this result admits a simple closed form that is accurate at moderate SIRs, e.g., above 5 dB. These results reveal that the SIR-invariance property of SISO HetNets does not hold for MIMO HetNets; instead the success probability may decrease as the network density increases. We prove that the maximum success probability is achieved by activating only one tier of BSs, while the maximum area spectral efficiency (ASE) is achieved by activating all the BSs. This reveals a unique tradeoff between the ASE and link reliability in multiuser MIMO HetNets. To achieve the maximum ASE while guaranteeing a certain link reliability, we develop efficient algorithms to find the optimal BS densities. It is shown that as the link reliability requirement increases, more BSs and more tiers should be deactivated.

Energy harvesting (EH) has recently emerged as a key technology for green communications as it can power wireless networks with renewable energy sources. However, directly replacing the conventional non-EH transmitters by EH nodes will be a challenge. In this paper, we propose to deploy extra EH nodes as relays over an existing non-EH network. Specifically, the considered non-EH network consists of multiple source-destination (S-D) pairs. The deployed EH relays will take turns to assist each S-D pair, and energy diversity can be achieved to combat the low-EH rate of each EH relay. To make the best of these EH relays, with the source transmit power minimization as the design objective, we formulate a joint power assignment and relay selection problem, which, however, is NP-hard. We thus propose a general framework to develop efficient suboptimal algorithms, which is mainly based on a sufficient condition for the feasibility of the optimization problem. This condition yields useful design insights and also reveals an energy hardening effect, which provides the possibility to exempt the requirement of noncausal EH information. Simulation results will show that the proposed co-operation strategy can achieve near-optimal performance and provide significant power savings. Compared to the greedy co-operation method that only optimizes the performance of the current transmission block, the proposed strategy can achieve the same performance with much fewer relays, and the performance gap increases with the number of S-D pairs.

Device-to-device (D2D) communication has great potential to improve spectrum efficiency and offload traffic for cellular networks. In this paper, we focus on a multiflow two-way D2D network with decode-and-forward (DF) relays, coexisting with OFDMA cellular network. The spectrum sharing and relay selection are considered to minimize the outage probability of the device in D2D networks. The induced problem is a complicated probabilistic integral programming. A novel weighted random bipartite graph (WRBG)-based minimum weight maximum matching (MWMM) approach will be proposed in this paper. To offload not only the traffic but also the signaling and computation overhead, the improved min-sum algorithm will be applied to find the MWMM in the distributed manner with only polynomial complexity. The proposed approach enjoys an advantage that the close-form approximation formulas for optimal outage probability and diversity-multiplexing tradeoff can be derived by analyzing the properties of MWMM on WRBG. Both the theoretical derivations and simulation results will illustrate that the proposed approach for multiflow two-way D2D networks achieves the same performance as single-flow two-way D2D systems. Therefore, the distributed WRBG matching approach yields not only a practical distributed algorithm, but also a simple and elegant theoretical framework for multiflow two-way D2D networks.

To meet the surging demand for throughput, 5G cellular networks need to be more heterogeneous and much denser, by deploying more and more small cells. In particular, the number of users in each small cell can change dramatically due to users' mobility, resulting in random and time varying uplink interference. This paper considers the uplink interference in a 5G heterogeneous network, which is jointly covered by one macro cell and several small cells. Based on the Lévy flight moving model, a mobility-aware interference model is proposed to characterize the uplink interference from macro cell users to small cell users. In this model, the total uplink interference is characterized by its moment generating function, for both closed subscriber group (CSG) and open subscriber group (CSG) femto cells. In addition, the proposed interference model is a function of basic step length, which is a key velocity parameter of Lévy flights. It is shown by both theoretical analysis and simulation results that the proposed interference model provides a flexible way of evaluating the system performance in terms of success probability and average rate.

An important requirement on 5G mobile systems is to accommodate massive numbers of wireless devices and users. Heterogeneous networks are expected to play a crucial role in meeting this requirement. In this vein, small cells are expected to become an integral part of these heterogeneous networks. However, their success would not last longer unless they offer services at a quality similar to that currently ensured by the macro cellular networks. Mitigating congestion of the backhaul links to small cell networks is a crucial factor. With this regard, this paper proposes an admission control that makes decisions to redirect IP flows, fully or partially, to the macro or small cell networks, or to reject the incoming flows. The decision mechanism is based on predictions of users' Quality of Experience (QoE). It is modeled as a Markov decision process (MDP), whereby the aim is to derive the optimal policy (i.e. reject or accept flows in the macro or the small cell) that maximizes users' QoE. Through computer simulations, we evaluate the performance of the proposed admission control and compare it against a random policy decision. We also numerically illustrate its optimal policies in different scenarios under different traffic load conditions.

Wireless energy transfer, namely, radio frequency (RF)-based energy harvesting, is a potential way to prolong the lifetime of energy-constrained devices, especially in wireless sensor networks. However, due to huge propagation attenuation, its energy efficiency is regarded as the biggest bottleneck to wide applications. It is critical to find appropriate transmission policies to improve the global energy efficiency in this kind of system. To this end, this paper focuses on the sensor networks scenario, where a mobile control center powers the sensors by RF signal and also collects information from them. Two related schemes, called harvest-and-use scheme and harvest-store-use scheme, are investigated. In the harvest-and-use scheme, as a benchmark, both constant and adaptive transmission modes from sensors are discussed. In the harvest-store-use scheme, we propose a new concept, the best opportunity for wireless energy transfer, and use it to derive an explicit closed-form expression of optimal transmission policy. It is shown by simulation that a considerable improvement in terms of energy efficiency can be obtained with the help of the transmission policies developed in this paper. Furthermore, the transmission policies are also discussed under the constraint of fixed information rate. The minimal required power, the performance loss from the new constraint, and the effect of fading are then presented.

Hybrid energy supply (HES) wireless networks have recently emerged as a new paradigm to enable green networks, which are powered by both the electric grid and harvested renewable energy. In this paper, we will investigate two critical but conflicting design objectives of HES networks, i.e., the grid energy consumption and quality of service (QoS). Minimizing grid energy consumption by utilizing the harvested energy will make the network environmentally friendly, but the achievable QoS may be degraded due to the intermittent nature of energy harvesting. To investigate the tradeoff between these two aspects, we introduce the total service cost as the performance metric, which is the weighted sum of the grid energy cost and the QoS degradation cost. Base station assignment and power control is adopted as the main strategy to minimize the total service cost, while both cases with non-causal and causal side information are considered. With non-causal side information, a Greedy Assignment algorithm with low complexity and near-optimal performance is proposed. With causal side information, the design problem is formulated as a discrete Markov decision problem. Interesting solution structures are derived, which shall help to develop an efficient monotone backward induction algorithm. To further reduce complexity, a Look-Ahead policy and a Threshold-based Heuristic policy are also proposed. Simulation results shall validate the effectiveness of the proposed algorithms and demonstrate the unique grid energy consumption and QoS tradeoff in HES networks.

Massive multiple-input-multiple-output (MIMO) is a promising technique for providing unprecedented spectral efficiency. However, it has been well recognized that the excessive training overhead required for obtaining the channel side information is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting the sparsity structures of massive MIMO channels. So far, however, there has been little discussion about how to exploit the partial support information of these channels to achieve further overhead reductions. Such information, which is a set of indices of the significant elements of a channel vector, can be acquired in advance and hence is an important option to explore. In this paper, we examine the impact on the required training overhead when this information is applied within a weighted ℓ1minimization framework, and analytically show that a sharp estimate of the reduced overhead size can be successfully obtained. Furthermore, we examine how the accuracy of the partial support information impacts the achievable overhead reduction. Numerical results for a wide range of sparsity and partial support information reliability levels are presented to quantify our findings and main conclusions.

In this paper, we present a flexible low-rank matrix completion (LRMC) approach for topological interference management (TIM) in the partially connected $K$-user interference channel. No channel state information (CSI) is required at the transmitters except the network topology information. The previous attempt on the TIM problem is mainly based on its equivalence to the index coding problem, but so far only a few index coding problems have been solved. In contrast, in this paper, we present an algorithmic approach to investigate the achievable degrees-of-freedom (DoFs) by recasting the TIM problem as an LRMC problem. Unfortunately, the resulting LRMC problem is known to be NP-hard, and the main contribution of this paper is to propose a Riemannian pursuit (RP) framework to detect the rank of the matrix to be recovered by iteratively increasing the rank. This algorithm solves a sequence of fixed-rank matrix completion problems. To address the convergence issues in the existing fixed-rank optimization methods, the quotient manifold geometry of the search space of fixed-rank matrices is exploited via Riemannian optimization. By further exploiting the structure of the low-rank matrix varieties, i.e., the closure of the set of fixed-rank matrices, we develop an efficient rank increasing strategy to find good initial points in the procedure of rank pursuit. Simulation results demonstrate that the proposed RP algorithm achieves a faster convergence rate and higher achievable DoFs for the TIM problem compared with the state-of-the-art methods.

In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire “partial” instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to the existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming, which enjoys a much lower complexity compared with exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.

The 5th generation (5G) wireless networks are conceived in the form of heterogeneous networks (HetNets), where small cells are deployed over the conventional macrocell networks to improve the spectral efficiency. In HetNets, the interference between different tiers is the main bottleneck for achieving high spectral efficiency. Many spectrum access schemes have been proposed to manage the cross-tier interference. Unfortunately, the optimal spectrum access scheme remains unknown. In this paper, we propose an F-ALOHA based cognitive spectrum access scheme for macro-femto HetNets, where the femtocells can access the idle macro-tier spectrum with a certain probability. Therefore, besides the degrees of freedom from the conventional spectrum deployment and co-tier spectrum access, the proposed scheme obtains a new degree of freedom from cross-tier spectrum access for interference management and spectral efficiency optimization. Simulation results will show that the proposed scheme outperforms existing F-ALOHA based spectrum access schemes in terms of the area spectral efficiency (ASE). More importantly, it is observed that the maximum ASE is achieved when the number of active links per unit area, which governs the interference level, reaches a certain value. The advantage of the proposed scheme comes from its ability to offload the traffic between two tiers through the cross-tier spectrum access probability, which flexibly manages the cross-tier interference.

Abstract Multiple‐Input, Multiple‐Output (MIMO)‐orthogonal frequency division multiplexing (OFDM) is a promising technique in 5G wireless communications. In high‐mobility scenarios, the transmission environments are time‐varying and/or the relative moving velocity between the transmitter and receiver is also time‐varying. In the literature, most of previous works mainly focused on fixed subcarrier group size and precoded the MIMO signals with unitary channel state information. In this way, the subcarrier grouping may naturally lead to big loss of channel capacity in high‐mobility scenarios because of the channel state information difference on the subcarriers in each group. To employ the MIMO‐OFDM technique, adaptive subcarrier grouping scheme may be an efficient way. In this paper, we first consider MIMO‐OFDM systems over double‐selective i.i.d. Rayleigh channels and investigate the quantitative relation between subcarrier group size and capacity loss theoretically. With developed theoretical results, we also propose an adaptive subcarrier grouping scheme to satisfy the preset capacity loss threshold by adjusting grouping size with the sensed environmental information and mobile velocity. Theoretical analysis and simulation results show that to achieve a better system capacity, a sparse scattering, lower signal‐to‐noise ratio, and lower velocity as well as properly large antenna number are matched with larger subcarrier group size. One important observation is that if the antenna number is too large and higher than a threshold, which will not bring any additional gain to the subcarrier grouping. That is, the system capacity loss will converge to a lower bound expeditiously with respect to antenna number, which is given in theory also. Copyright © 2016 John Wiley & Sons, Ltd.

As the capacity demand of mobile applications keeps increasing, the backhaul network is becoming a bottleneck to support high quality of experience (QoE) in next-generation wireless networks. Content caching at base stations (BSs) is a promising approach to alleviate the backhaul burden and reduce user-perceived latency. In this paper, we consider a wireless caching network where all the BSs are connected to a central controller via backhaul links. In such a network, users can obtain the required data from candidate BSs if the data are pre-cached. Otherwise, the user data need to be first retrieved from the central controller to local BSs, which introduces extra delay over the backhaul. In order to reduce the download delay, the caching placement strategy needs to be optimized. We formulate such a design problem as the minimization of the average download delay over user requests, subject to the caching capacity constraint of each BS. Different from existing works, our model takes BS cooperation in the radio access into consideration and is fully aware of the propagation delay on the backhaul links. The design problem is a mixed integer programming problem and is highly complicated, and thus we relax the problem and propose a low-complexity algorithm. Simulation results will show that the proposed algorithm can effectively determine the near-optimal caching placement and provide significant performance gains over conventional caching placement strategies.

Underlaying device-to-device (D2D) communication links to a cellular network is a promising way to improve spectrum efficiency, for which the cross- link interference should be carefully controlled. Resource allocation has been widely utilized for managing interference in D2D networks. However, most previous works made simple assumptions by either ignoring the reliability requirement of D2D links or not allowing multiple D2D links to share the same channel. In this paper, we propose effective channel assignment algorithms to maximize the weighted sum-rate in a cellular network with underlaying D2D communications, where multiple D2D links are allowed to share the same channel. Meanwhile, the minimum Signal-to- Interference-plus-Noise Ratio (SINR) requirements for both cellular and D2D links are guaranteed. We first provide an optimal algorithm based on dynamic programming (DP) to serve as the performance benchmark, which enjoys much lower complexity compared to exhaustive search. To further reduce complexity, we then propose a cluster-based near-optimal channel assignment algorithm. Simulation results will demonstrate the advantage of allowing multiple D2D links to share the same channel in dense D2D networks, as well as verifying the effectiveness of the proposed algorithms.

Millimeter wave (mmWave) communications holds a promise to offer an unprecedented capacity boost for 5G cellular networks. Due to the small wavelength of mmWave signals, mmWave multiple-input-multiple-output (MIMO) systems can leverage large-scale antennas to combat the path loss and rain attenuation via precoding. Different from conventional MIMO systems, mmWave MIMO cannot realize precoding entirely at baseband using digital precoders, as a result of potentially high power consumed by signal mixers and analog-to-digital converters (ADCs). As a cost- effective alternative, a hybrid precoding transceiver architecture for mmWave MIMO systems has received considerable attention. However, the optimal design of such hybrid precoding has not been fully understood. In this paper, an alternating minimization algorithm based on manifold optimization is proposed to design the hybrid precoder, thereby making it comparable in performance to the digital precoder. Numerical results show that our proposed algorithm can significantly outperform existing ones in terms of spectral efficiency and, more importantly, it can achieve the optimal performance in certain cases. Finally, the alternating minimization approach is shown to be generally applicable to precoding design with different hybrid structures, and the corresponding comparison will show interesting design insights for hybrid precoding.

This paper investigates the simultaneous wireless power and information transfer (SWPIT) for network-coded two-way relay transmission from an information theoretical viewpoint, where two sources exchange information via an energy harvesting relay. By considering the time switching (TS) relay receiver architecture, we present the TS-based two-way relaying (TS-TWR) protocol. In order to explore the system throughput limit with data rate fairness, we formulate an optimization problem under total power constraint. To solve the problem, we first derive some explicit results and then design an efficient algorithm. Numerical results show that with the same total available power, TS-TWR has a certain performance loss compared with conventional non-EH two-way relaying due to the path loss effect on energy transfer, where in relatively low and relatively high SNR regimes, the performance losses are relatively small.

The Cloud radio access network (Cloud-RAN) has great potentials to improve energy efficiency and increase capacity of wireless networks. In this paper, we investigate multicast beamforming design for network power minimization of Cloud-RAN, which is shown to be a highly intractable non-convex mixed integer non-linear programming problem. To provide an efficient solution to this highly complicated problem, we propose a three-stage algorithm based on the group-sparsity inducing norm, which minimizes network power by coordinated multicast beamforming and adaptively selecting active remote radio heads (RRHs). In particular, a novel quadratic variational weighted ℓ1=ℓ2-norm aided alternating algorithm is proposed to exploit the group-sparsity structure of the beamforming vector, thereby guiding the active RRH set selection. Given the selected RRH set, multicast beamforming is performed to minimize the network power consumption. Furthermore, to enhance the computation efficiency upon utilizing the shared computing resources in the cloud center, we employ the alternating direction method of multipliers (ADMM) algorithm to solve the resulting semidefinite programming problems in parallel. Extensive simulation results will demonstrate the effectiveness of the proposed multicast group sparse beamforming algorithm.

Wireless energy transfer, namely RF-based energy harvesting, is a potential way to prolong the lifetime of energy-constrained devices, such as wireless sensor networks. However, due to the huge propagation attenuation, energy efficiency is widely regarded as the biggest bottleneck for large-scale applications. Thus, it is significantly essential to explore appropriate transmission policies for the system to improve the global energy efficiency. To this end, this paper focuses on the optimum transmission policies in sensor networks scenario, where a mobile control center powers the sensor node by RF signal and then collects information from sensor node. Based on whether there is an energy storage at the sensor node, two related schemes, called as harvest-and-use scheme and harvest-store-use scheme, are investigated, respectively. In harvest-and-use scheme, as a baseline, both constant and optimal adaptive transmission strategies are discussed. In harvest-store-use scheme, an explicit closed-form expression of optimal transmission policy is derived in terms of throughput maximization, which can greatly enhance the system performance by opportunistic energy transfer with the help of energy storage. According to the simulation results, a considerably large improvement can be observed by employing the optimum transmission policy developed in this paper.

Heterogeneous networks (HetNets) provide an effective way to meet the explosive growth of mobile data traffic. Previous studies have revealed that the successful transmission probability, i.e., the link reliability, of a SISO HetNet is invariant to the base station (BS) density. This indicates that the area spectral efficiency (ASE) can be increased by densifying the network, without sacrificing the link performance. However, in this paper, we shall show that the above invariance property no longer holds in multi-antenna HetNets. More specifically, changing the BS density will affect both the link reliability and the ASE, and there exists a tradeoff between these two important performance metrics. By adopting the Poisson point process to model the BS positions, we develop an exact expression of the successful transmission probability for general multiuser MIMO HetNets. We then use this result to evaluate the tradeoff between the ASE and the link reliability. It is analytically shown that the maximum successful transmission probability of the network is achieved by activating only the tier of BSs with the largest number of antennas per BS, while the maximum ASE is achieved by activating all the BSs. By adjusting the density of each tier of the HetNet, a tradeoff between the link reliability and ASE can be achieved.

Massive MIMO is a promising technique to provide unprecedented spectral efficiency. However, it has been well recognized that huge training overhead for obtaining channel side information (CSI) is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting sparse structures of massive MIMO channels. So far, however, there has been little discussion about how to utilize partial support information of sparse channels to achieve further overhead reduction. This support information, which is a set of indexes of significant elements of a channel vector, actually can be acquired in advance. In this paper, we examine the required training overhead when partial support information is applied within a weighted ℓ1minimization framework and analytically show that a sharp estimate of this overhead size can be successfully obtained. Furthermore, we demonstrate that the accuracy of partial support information plays an important role in determining how much reduction can be achieved. Numerical results shall verify the main conclusions.

In 5G networking, it is promising to integrate cellular system and wireless local area networks (WLAN) to enhance the throughput. The access point of the WLAN can be authenticated as a relay receiver in cellular system to assist the communication between the base station and the user equipment. In this paper, we model the integrated system as a Receiver Frequency Division Gaussian Relay Channel (RFD-GRC) and study how to improve the achievable transmission rate by adopting Decode-and-Forward (DF) and Compress-and-Forward (CF) schemes in the system.Specifically, making use of the orthogonality between the source frequency band (SFB, the cellular system frequency band) and the relay frequency band (RFB, the WLAN frequency band), we independently divide the available SFB and RFB into two subbands and adopt DF and CF in the two subbands, respectively. Joint bandwidth and power allocation of this hybrid DF-CF scheme is optimized, resulting in a cooperation gain larger than that achieved by DF and CF schemes individually.A sub-optimal setting for the hybrid DF-CF scheme is also given, simplifying the implementation and approaching the optimal rate performance. Numerical analysis confirms the effectiveness of the new scheme.

This paper considers the topological interference management problem in a partially connected K-user interference channel, where no channel state information at transmitters (CSIT) is available beyond the network topology knowledge. Due to the practical CSI assumption, this problem has recently received enough attention. In particular, it has been established that the topological interference management problem, in terms of degrees of freedom (DoF), is equivalent to the index coding problem with linear schemes. However, so far only a few index coding problems have been solved, and thus there is a lack of a systematic way to characterize optimal DoF of an arbitrary network topology. In this paper, we present a low-rank matrix completion (LRMC) approach to find linear solutions to maximize the achievable symmetric DoF for any given network topology. To decode the desired messages at each receiver, we also propose an LRMC based channel acquisition scheme, which can obtain interference-free measurements of the desired channel at each receiver while minimizing the pilot training length. To address the NP-hardness of the non-convex rank objective function in the resulting LRMC problem, we further present a Riemannian pursuit (RP) algorithm to solve it efficiently. This algorithm alternatively performs fixed-rank optimization using Riemannian optimization and rank increase by exploiting the manifold structure of the fixed-rank matrices. The LRMC approach aided by the RP algorithms not only recovers the existing optimal DoF results but also provides insights for general network topologies.

In high speed railways (HSRs) communication system, the wireless channel between the train and base station varies strenuously due to high mobility, which makes it very essential to implement appropriate adaptive algorithms to guarantee the quality-of-service (QoS). What's more, how to evaluate the performance limits in this new scenario must also be considered. To this end, this paper investigates the performance limits of wireless communication in HSRs scenario. Since the information transmitted between train and base station usually has diverse QoS requirements, a QoS-distinguished achievable rate region is utilized to characterize the transmission performance in this paper, which can be regarded as a generalized case of traditional ergodic capacity and outage capacity with unique QoS requirement. The specific adaptive algorithm that can achieve the maximal boundary of achievable rate region is also derived. Compared with conventional strategies, the advantages of the proposed strategy are validated in terms of green communication, namely minimizing average transmit power.

This paper addresses the joint base station (BS) assignment and power control problem in a hybrid energy supply wireless network, where an energy harvesting BS and a grid-powered BS coordinate to serve a mobile user. In order to minimize the grid energy consumption while maximizing the number of transmitted data packets, we introduce the total service cost over an N-block frame as the performance metric, which is the weighted sum of the grid energy cost and the packet drop cost. With non-causal side information (SI) available at the BSs, including energy SI and channel SI, a Greedy Assignment algorithm with low complexity and near optimal performance is proposed. For the causal SI setting, the design problem is formulated as a discrete Markov decision problem. Interesting solution structures are derived, which help develop an efficient monotone backward induction algorithm. To further reduce the complexity, a heuristic online policy is also proposed. Simulation results shall validate the effectiveness of the proposed policies and demonstrate a unique tradeoff in such networks, i.e., the tradeoff between the grid energy consumption and the provided quality of service.

Powering cellular networks with renewable energy sources via energy harvesting (EH) have recently been proposed as a promising solution for green networking. However, with intermittent and random energy arrivals, it is challenging to provide satisfactory quality of service (QoS) in EH networks. To enjoy the greenness brought by EH while overcoming the instability of the renewable energy sources, hybrid energy supply (HES) networks that are powered by both EH and the electric grid have emerged as a new paradigm for green communications. In this paper, we will propose new design methodologies for HES green cellular networks with the help of Lyapunov optimization techniques. The network service cost, which addresses both the grid energy consumption and achievable QoS, is adopted as the performance metric, and it is optimized via base station assignment and power control (BAPC). Our main contribution is a low-complexity online algorithm to minimize the long-term average network service cost, namely, the Lyapunov optimization-based BAPC (LBAPC) algorithm. One main advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of channels and EH processes. To determine the network operation, we only need to solve a deterministic per-time slot problem, for which an efficient inner-outer optimization algorithm is proposed. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Finally, sample simulation results are presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

This paper investigates the simultaneous wireless information and energy transfer for the non-regenerative multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relaying system. By considering two practical receiver architectures, we present two protocols, time switching-based relaying (TSR) and power splitting-based relaying (PSR). To explore the system performance limits, we formulate two optimization problems to maximize the end-to-end achievable information rate with the full channel state information (CSI) assumption. Since both problems are non-convex and have no known solution method, we firstly derive some explicit results by theoretical analysis and then design effective algorithms for them. Numerical results show that the performances of both protocols are greatly affected by the relay position. Specifically, PSR and TSR show very different behaviors to the variation of relay position. The achievable information rate of PSR monotonically decreases when the relay moves from the source towards the destination, but for TSR, the performance is relatively worse when the relay is placed in the middle of the source and the destination. This is the first time such a phenomenon has been observed. In addition, it is also shown that PSR always outperforms TSR in such a MIMO-OFDM relaying system. Moreover, the effects of the number of antennas and the number of subcarriers are also discussed.

Small cell networks are regarded as a promising candidate to meet the exponential growth of mobile data traffic in cellular networks. With a dense deployment of access points, spatial reuse will be improved, and uniform coverage can be provided. However, such performance gains cannot be achieved without effective intercell interference management. In this paper, a novel interference coordination strategy, called user-centric intercell interference nulling, is proposed for small cell networks. A main merit of the proposed strategy is its ability to effectively identify and mitigate the dominant interference for each user. Different from existing works, each user selects the coordinating base stations (BSs) based on the relative distance between the home BS and the interfering BSs, called the interference nulling (IN) range, and thus interference nulling adapts to each user's own interference situation. By adopting a random spatial network model, we derive an approximate expression of the successful transmission probability to the typical user, which is then used to determine the optimal IN range. Simulation results shall confirm the tightness of the approximation, and demonstrate significant performance gains (about 35-40%) of the proposed coordination strategy, compared with the non-coordination case. Moreover, it is shown that the proposed strategy outperforms other interference nulling methods. Finally, the effect of imperfect channel state information (CSI) is investigated, where CSI is assumed to be obtained via limited feedback. It is shown that the proposed coordination strategy still provides significant performance gains even with a moderate number of feedback bits.

A relay channel (RC), consisting of a source, a relay, and a destination, is a basic transmission unit of cooperative communication networks. The capacity of an RC is not known in general. In this paper, an SNR decomposition (SD) strategy is presented to implement time sharing, which provides a new tractable and achievable rate for a full-duplex Gaussian RC. More specifically, we first expand the SNR of a relay destination channel (SNR-RD) into two terms under the relay power constraint and divide the system into two subbands. Then, we assign the obtained SNR-RD for each subband and employ decode-forward (DF) or compress-forward (CF) according to the assigned SNR-RD. It is shown that the achievable rate of the SD strategy is competitive with that of superposing CF on DF. As the superposition structure requires a sophisticated codeword design, the SD strategy provides another practical combination structure of DF and CF strategies. Approximations for the SNR-RD and bandwidth allocation for subbands are also given. Based on the obtained results, two application scenarios, i.e., mobile relay and quasi-static fading RCs, are also considered. Finally, various numerical results are shown to support our developed theoretical results.

In this paper, two cooperative communications schemes with inter-relay interference (IRI) management are proposed for wireless multi-user decode-and-forward (DF) relay networks. The schemes are based on a DF half-duplex (HD) relaying protocol and a relay selection method which maximizes the signal-to-noise ratio (SNR) of the second hop. To minimize the IRI, the first scheme [constellation real part (CRP)] uses a new transmission scheme based on the constellation real parts of the modulated signals, and the second scheme [previous message buffering (PMB)] uses a buffering technique at the relays. To assess the performance, we derive the expressions of the average bit error rate (BER) for the proposed schemes. Numerical results are given to confirm the analytical expressions and the advantage of the proposed schemes in enhancing interference management for wireless cooperative networks.

Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive MIMO, i.e., the maximum number of users whose SINR targets can be achieved, has not been addressed. This paper provides an explicit expression of the Massive MIMO user capacity in the pilot-contaminated regime where the number of users is larger than the pilot sequence length. This capacity expression characterizes a region within which a set of SINR requirements can be jointly satisfied. The size of this region is fundamentally limited by the pilot sequence length. Furthermore, the scheme for achieving the user capacity, i.e., the uplink pilot training sequences and downlink power allocation, has been identified. Specifically, the generalized Welch bound equality sequences are exploited and it is shown that the power allocated to each user should be proportional to its SINR target. With this capacity-achieving scheme, the SINR requirement of each user can be satisfied and energy-efficient transmission is achieved in the large-antenna-size (LAS) regime. The comparison with two non-capacity-achieving schemes highlights the superiority of our proposed scheme in terms of achieving higher user capacity. Furthermore, for the practical scenario with a finite number of antennas, the actual antenna size required to achieve a significant percentage of the asymptotic performance has been analytically quantified.

In full-duplex Gaussian relay channels, neither Decode-and-Forward (DF) nor Compress-and-Forward (CF) can achieve a larger rate than the other for all the channel gain combinations. Combining DF and CF strategies, we show that a new achievable rate, which is the concave envelop of the maximal rate achieved by DF and CF with respect to the source power, is achievable. To this end, we actively adjust the transmission power of the source for different time and switch the transmission strategy between DF and CF according to the source power. It is proved that when the signal to noise ratio (SNR) of the source-destination link falls into a certain range, the new achievable rate is strictly larger than that achieved by pure DF and pure CF. The optimal power allocation and corresponding time proportions are also obtained. Numerical results show that the new achievable rate is also competitive with the rate achieved by superposing CF on DF. As strategy switching avoids complex codeword constructions, it is more practical than superposition structures to be implemented in relay systems.

Harvesting energy from ambient environment is a new promising solution to free electronic devices from electric wire or limited-lifetime battery, which is significant in sensor networks and body-area networks. This paper investigates the fundamental limits of information transmission in data acquisition system with RF-based energy harvesting sensor node, in which the host node acts not only as an information source but also as an energy source for the sensor node while only information is transmitted back from sensor to host node. From a view of system level, achievable capacity-rate region and capacity-rate function are proposed as metrics to measure the transmission performance. The tradeoff relationship of two way information rates between sensor and host node in a time division duplex system is obtained and the corresponding optimal transmission policy is also given. At last, a typical application in terms of minimizing required transmit power, namely green system, is introduced to validate the results developed in this paper.

Energy harvesting (EH) has recently been attracting significant attention because of its ability to scavenge environmentally friendly energy. In this paper, we investigate the use of EH relay nodes to improve the quality of service (QoS) for relaying networks. To simplify the hardware design, we adopt a half-duplex selective decode-and-forward (SDF) relay. We propose a joint link selection and relay power allocation strategy to minimize the average outage probability. Both offline and online policies, i.e., with non-causal or causal side information about the energy state and the decoding result at the relay, are investigated by utilizing deterministic and stochastic dynamic programming (DP) algorithms, respectively. Furthermore, to reduce the complexity of the optimal online solution, we propose two low-complexity suboptimal online policies. Simulation results will show that the proposed suboptimal policies outperform the existing policies and achieve near optimal performance.

Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive MIMO, i.e., the maximum number of admissible users, has not been addressed. This paper provides an explicit expression of the Massive MIMO user capacity in the pilot-contaminated regime where the number of users is larger than the pilot sequence length. Furthermore, the scheme for achieving the user capacity, i.e., the uplink pilot training sequence and downlink power allocation, has been identified. By using this capacity-achieving scheme, the SINR requirement of each user can be satisfied and energy-efficient transmission is feasible in the large-antenna-size (LAS) regime. Comparison with two non-capacity-achieving schemes highlights the superiority of our proposed scheme in terms of achieving higher user capacity.

To meet the ever growing demand for both high throughput and uniform coverage in future wireless networks, dense network deployment will be ubiquitous, for which cooperation among the access points is critical. Considering the computational complexity of designing coordinated beamformers for dense networks, low-complexity and suboptimal precoding strategies are often adopted. However, it is not clear how much performance loss will be caused. To enable optimal coordinated beamforming, in this paper, we propose a framework to design a scalable beamforming algorithm based on the alternative direction method of multipliers (ADMM). Specifically, we first propose to apply the matrix stuffing technique to transform the original optimization problem to an equivalent ADMM-compliant problem, which is much more efficient than the widely-used modeling framework CVX. We will then propose to use the ADMM algorithm, a.k.a. the operator splitting method, to solve the transformed ADMM-compliant problem efficiently. In particular, the subproblems of the ADMM algorithm at each iteration can be solved with closed-forms and in parallel. Simulation results show that the proposed techniques can result in significant computational efficiency compared to the state-of-the-art interior-point solvers. Furthermore, the simulation results demonstrate that the optimal coordinated beamforming can significantly improve the system performance compared to sub-optimal zero forcing beamforming.

Deploying femtocells over the conventional macrocell network is a promising way to increase network capacity, whereas the main bottleneck is the interference between the femtocell and macrocell tiers. Recent research has proposed many effective methods for cross-tier interference mitigation, but unfortunately, the optimal spectrum access scheme remains unknown. In this paper, a cognitive spectrum access scheme is proposed, where each femtocell can dynamically explore and access the idle macro-tier spectrum besides its dedicated femto-tier spectrum. The optimum probabilities for each femtocell to access the femto-tier and idle macro-tier spectrum which maximize the area spectral efficiency (ASE) are investigated. With perfect spectrum sensing, the ratio between the optimum probabilities to access the femto-tier and macro-tier spectrum is equal to the amounts of available subchannels in the femto-tier and macro-tier spectrum for most cases. With non-perfect spectrum sensing, a lower bound of the optimum probability to access the femto-tier spectrum is determined, which is a piecewise function of the femtocell intensity. Simulation results validate the accuracy of our analytical results and reveal the advantages of the proposed scheme over existing schemes.

The green communications, which focus on improving the energy efficiency, have attracted much attention from both academia and industry recently. In this paper, the tradeoff between outage probability and energy efficiency will be addressed for multi-flow cooperative communication systems with taking energy budget and devices energy consumption into consideration. The proposed approach will first formulate the multi-flow cooperative communication system as a weighted random bipartite graph (WRBG) model. The minimum weighted maximum matching (MWMM) method will then be proposed to select a relay for each source-destination (s-d) pair in order to minimize the outage probability and maximize the average energy efficiency simultaneously. By analyzing the properties of every sample of the WRBG model, the closed-form formulas for the outage probability and average energy efficiency will then be obtained. Therefore, based on the derived tradeoff, the outage probability and average energy efficiency can be balanced by adjusting the energy budgets and transmission rate according to the system requirement. Moreover, the proposed MWMM method also enjoys an advantage of the log-polynomial computation complexity for parallel implementations.

This work considers the achievable rate region of full-duplex Gaussian relay channel. Under Gaussian signaling, it was found that neither Decode-Forward (DF) nor Compress-Forward (CF) can achieve better performance than the other for all channel gains. Recently, it was verified that superposing CF on DF in one band has a better performance than both DF and CF for Gaussian signaling. In this work, we consider another combining structure of CF and DF by making use of Subband Division (SD). It will show that our new developed strategy will achieve a larger rate for Gaussian signaling by comparing with DF lower bound, CF lower bound and the result of superposing CF on DF. In addition, a closed form solution of the achievable rate is found, in which the subband division factors are also given. Numerical results confirm our developed theoretical results.

Small cell networks provide an effective way to meet the explosive growth of mobile data traffic, which, however, complicates the network structure and makes intercell interference management more challenging. One existing interference management approach is to divide the whole network into disjoint clusters, with base stations (BSs) within each cluster doing interference coordination, but the performance will then be limited by the cluster edge users. In this paper, a novel intercell interference coordination method is proposed from the user's point of view. Each mobile user will request some neighboring BSs for interference avoidance, which is based on the relative distance between the home BS and the interfering BSs, called as the interference coordination (IC) range. In this way, the most critical interfering sources for each user can be suppressed, and thus there will be no edge user. We derive an accurate approximation for the successful transmission probability of a typical user with the proposed interference coordination method, based on which the optimal IC range can be obtained. Simulation results demonstrate a significant performance gain for the proposed method, and also show that it outperforms the existing BS clustering method.

Cloud radio access network (Cloud-RAN) is a promising network architecture to meet the explosive growth of the mobile data traffic. In this architecture, as all the baseband signal processing is shifted to a single baseband unit (BBU) pool, interference management can be efficiently achieved through coordinated beamforming, which, however, often requires full channel state information (CSI). In practice, the overhead incurred to obtain full CSI will dominate the available radio resource. In this paper, we propose a unified framework for the CSI overhead reduction and downlink coordinated beamforming. Motivated by the channel heterogeneity phenomena in large-scale wireless networks, we first propose a novel CSI acquisition scheme, called compressive CSI acquisition, which will obtain instantaneous CSI of only a subset of all the channel links and statistical CSI for the others, thus forming the mixed CSI at the BBU pool. This subset is determined by the statistical CSI. Then we propose a new stochastic beamforming framework to minimize the total transmit power while guaranteeing quality-of-service (QoS) requirements with the mixed CSI. Simulation results show that the proposed CSI acquisition scheme with stochastic beamforming can significantly reduce the CSI overhead while providing performance close to that with full CSI.

The deployment of femtocells in a conventional cellular network is a promising way to increase network capacity, whereas the main bottleneck is the interference between and within tiers. Previous work proposed channel splitting and F-ALOHA to manage the cross-tier and co-tier interference, respectively. However, such spectrum allocation scheme is not efficient given the often scenarios where part of the macro-tier spectrum is vacant but the femto-tier spectrum is overused. In this paper, a cognitive spectrum access scheme is proposed, where femtocells can access both femto-tier and macro-tier spectrum with certain probabilities, to increase the area spectral efficiency (ASE). The closed-form expressions of the optimum spectrum access probabilities in maximizing the ASE are derived for two scenarios where macrocell base stations (MBSs) are modeled as Poisson point process (PPP) and periodic grid. Analytical results reveal that for most cases, the ratio between the optimum probabilities for femtocells to access the femto-tier and macro-tier spectrum is equal to the ratio between the number of subchannels in the femto-tier and idle macro-tier spectrum. Simulation results show that with both models, the proposed scheme outperforms previous work in terms of the ASE.

Two user Gaussian interference channel (GIC) consists of two source-destination pairs which transmit independent messages and interfere with each other. The best achievable rate region, referred to HK sum rate bound, requires the sources to split the information into public messages and private messages. As Gaussian signaling holds the potential of approaching the capacity, finding the HK sum rate achieved by Gaussian signaling is of great importance. However, The optimal power allocation over messages for Gaussian signaling are not known yet This work clearly describes the optimal power allocation and corresponding sum rate achieved by Gaussian signaling without time sharing (TS) in closed form. It lays a foundation for finding the TS strategy achieving the optimal sum rate. The obtained power allocation indicates that without TS, message splitting may not be always necessary. Besides, the conditions for using and not using message splitting are also characterized in detail.

Caching at wireless access points is a promising approach to alleviate the backhaul burden in wireless networks. In this paper, we consider a cooperative wireless caching network where all the base stations (BSs) are connected to a central controller via backhaul links. In such a network, users can get the required data locally if they are cached at the BSs. Otherwise, the user data need to be assigned from the central controller to BSs via backhaul. In order to reduce the network cost, i.e., the back-haul cost and the transmit power cost, the data assignment for different BSs and the coordinated beamforming to serve different users need to be jointly designed. We formulate such a design problem as the minimization of the network cost, subject to the quality of service (QoS) constraint of each user and the transmit power constraint of each BS. This problem involves mixed-integer programming and is highly complicated. In order to provide an efficient solution, the connection between the data assignment and the sparsity-introducing norm is established. Low-complexity algorithms are then proposed to solve the joint optimization problem, which essentially decouple the data assignment and the transmit power minimization beamforming. Simulation results show that the proposed algorithms can effectively minimize the network cost and provide near optimal performance.

Random spatial network models have been recently utilized in the performance analysis and system design for multi-cell networks. Such an approach has been mainly adopted to investigate the outage based system performance, such as the outage probability and outage throughput. However, these performance metrics are defined with a fixed-rate transmission, and cannot characterize the performance of data traffic, which normally adopts rate adaptation. In this paper, we will evaluate the average throughput of a space division multiple access (SDMA) based cellular network by considering stochastically distributed base stations (BSs) and mobile terminals (MTs). The major difficulty for the performance analysis is the complicated distribution of the interference links. We shall provide an analytical framework for evaluating the average throughput by using the Moment Generating Function (MGF) based method. Simulations will show that the proposed method is very accurate. In particular, the analytical result can be utilized to determine the optimum number of MTs to be served in SDMA networks that can maximize the network throughput.

Cognitive radio can improve the spectrum efficiency by allowing multiple secondary users to access the idle licensed spectrum, for which efficient and fair spectrum sharing is one of the key challenges. In this paper, we investigate the multiuser multi-channel spectrum allocation problem in cognitive radio networks with the objective of maximizing the minimum throughput among all the cognitive pairs. In the proposed approach, all the secondary users will get the opportunity to access the channel and thus a good fairness can be achieved. We first introduce a weighted bipartite graph model for this design problem. A novel semi-matching based framework is then proposed to provide an efficient suboptimal solution, which only requires statistical channel state information. Simulation results will show that by applying this approach, max-min fairness can be improved for the secondary users.

In this paper, we propose blind interference alignment (BIA) schemes using reconfigurable antenna technology to achieve high degree of freedom (DoF) or diversity gain in K-user interference channels. First, two DoF-oriented BIA schemes are proposed that fully and partially align the inter-user interference, respectively. We compare the achievable DoF of the two schemes and show that the partial BIA scheme has higher DoF than the full BIA scheme under some conditions, and vice versa. Then, three diversity-oriented BIA schemes with space-time coding are proposed to achieve both spatial diversity gain and reconfigurable antenna pattern diversity gain. With different tradeoffs among the diversity gain, the rate and the decoding complexity, the BIA with threaded algebraic space-time (TAST) codes, the BIA with orthogonal space-time block codes (OSTBCs), and the BIA with multiplexing Alamouti codes are proposed, respectively. The BIA with TAST codes can achieve the full diversity and high rate with high decoding complexity. The BIA with OSTBCs can achieve the full diversity and the linear decoding complexity but has low rate. The BIA with multiplexing Alamouti codes can achieve high rate with low decoding complexity at the expense of full diversity loss.

This paper considers a conditional decode-and-forward (DF) based cooperative system where a source (S) with multiple (M) antennas transmits information to a single-antenna destination (D) with the help of multiple (L ≤ M - 1) single-antenna relays ({Ri}). The optimal transmit weighting vector at the source is not available in the literature due to the non-linear conditional DF operation, which renders the problem non-convex. To solve this problem, we first show that the optimal transmit vector for the single-relay system can be determined by comparing the S-D beamformer (maximum-ratio-transmit beamforming vector for the S-D link) with the one that utilizes “just sufficient” energy to activate the relay-link. However, it is difficult to directly apply the above idea to the multi-relay system, due to the fact that the S-R and S-D links are normally not orthogonal. To tackle this issue, we propose to utilize basis functions that are orthogonal to the S-D and S-R links, respectively, which enables the activating of one S-R link without considering the S-D link and the other S-R links. We then apply the new basis functions to the multi-relay system and propose a selective relay-activation algorithm, where the optimal solution is obtained by comparing the S-D beamformer with schemes that selectively activate different combinations of the relay-links. The selective relay-activation algorithm is different from the conventional water-filling in the sense that the energy is filled to discrete levels to activate the S-R links, a unique feature arising from the conditional DF operation.

With interference alignment in the spatial domain, the achievable degrees of freedom (DoF) of a single-hop multiple-input multiple-output (MIMO) interference channel (IC) are limited by the number of antennas at the sources and destinations. The use of relays introduces additional freedoms to manage the interference and can enhance the DoF performance. However, the characterization of the DoF regions with relays is much more complicated and is not available in the literature. In this paper, we shall investigate the DoF of the dual-hop MIMO IC via interference alignment. Based on the solvability of the alignment conditions, the upper bound for the maximum achievable DoF tuple is obtained. To evaluate the tightness of the derived bound, we further propose an iterative algorithm to determine the processing matrices at the sources, relays, and destinations for a given feasible DoF tuple. It is shown that the proposed algorithm can achieve the upper bound for the sum DoF in the low and high DoF regions, where the achievability indicates that the upper bound indeed gives the maximum sum DoF. It is also found that despite the DoF loss caused by the half-duplexity assumption, the dual-hop IC with sufficient number of relays can still outperform the conventional single-hop IC under most circumstances.

Small cell networks have recently been proposed as an important evolution path for the next-generation cellular networks. However, with more and more irregularly deployed base stations (BSs), it is becoming increasingly difficult to quantify the achievable network throughput or energy efficiency. In this paper, we develop an analytical framework for downlink performance evaluation of small cell networks, based on a random spatial network model, where BSs and users are modeled as two independent spatial Poisson point processes. A new simple expression of the outage probability is derived, which is analytically tractable and is especially useful with multi-antenna transmissions. This new result is then applied to evaluate the network throughput and energy efficiency. It is analytically shown that deploying more BSs can always increase the network throughput, but the throughput will scale with the BS density first linearly, then logarithmically, and finally converge to a constant. On the other hand, increasing the number of BS antennas can decrease the outage probability exponentially, thus can always increase the network throughput. However, increasing the BS density or the number of transmit antennas will first increase and then decrease the energy efficiency if the non-transmission power or the circuit power consumption is less than certain thresholds, and the optimal BS density and the optimal number of BS antennas can be found. Otherwise, the energy efficiency will always decrease. Simulation results shall demonstrate that our conclusions based on the random network model are general and also hold in a regular grid-based model.

A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near-optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted ℓ1/ℓ2-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.

Space time network coding (STNC) is a recently proposed time-division multiple-access (TDMA)-based cooperative relaying scheme for multi-relay wireless systems, which can achieve full diversity order with low transmission delay by taking advantage of the concepts of network coding and distributed space time coding. However, STNC does not fully exploit the benefit of the broadcast nature of wireless channels, since it only allows relays to receive signals from the sources. To explore the potential capacity of STNC-based systems, in this paper, we propose a new cooperative relaying scheme, termed space-time network coding with overhearing relays (STNC-OR), by allowing each relay to collect the signals transmitted from not only the sources but also its previous relays. Then, we derive some explicit expressions for the outage probability and symbol error rate (SER) for STNC-OR with decode-and-forward relaying over independent non-identically distributed (i.n.i.d) Rayleigh fading channels. For comparison, we also derive the explicit expression of the outage probability for STNC. To further improve the performance of STNC-OR, we investigate the effect of relay ordering on the performance of STNC-OR and then present the optimal relay ordering algorithm. Further, a suboptimal relay ordering is also designed to reduce the complexity. Extensive simulation and numerical results are presented finally to validate our theoretical analysis. It is shown that the proposed STNC-OR achieves much lower outage probability and SER than STNC and traditional pure TDMA relaying schemes.

ABSTRACT In this paper, we study the channel service process of a multiple‐input multiple‐output (MIMO) system over an independent and identically distributed (i.i.d.) fading channel. One key problem of communication over fading MIMO channels is what kind of service the MIMO channel can provide. In this paper, this problem is investigated in terms of channel service process. Assuming that the channel state information is available at the receiver, the channel service process S(t) is defined as the integral of the instantaneous channel capacity over a time interval of length t, which specifies the service provided by the channel during the period. Using the characteristic function approach and the infinitely divisible law, it is proved that the channel service process S(t) is a deterministic linear function of time t, other than any curve form or a stochastic process. Specifically, , where is a constant equal to the corresponding ergodic capacity. This result has two implications: (i) i.i.d. fading MIMO channels can support a constant rate traffic stream of rate without higher layer transmission delay; (ii) the ergodic capacity is the actual transmission capacity of the fading channel, other than only a statistical average value. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Signal‐time coding (STC) is a newly proposed transmission scheme for half‐duplex relay networks, which is able to achieve higher information flow rate by combining the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in the time domain. However, most of the results for STC are only obtained under the ideal assumptions that the signal detections at physical layer are perfect and there are still a lot of fundamental problems to be explored. This paper considers the implementing issues of STC at physical layer in additive white Gaussian noise relay networks. Firstly, a performance evaluation criterion, the reliable information per symbol (RIPS), is proposed to characterize the performance of STC in noisy wireless networks. Secondly, a new construction scheme based on route ID for the codeword of STC is presented, and some structural properties of the codeword of STC are investigated. Thirdly, the error probabilities of STC in both the signal domain and the time domain are discussed. Furthermore, two implementing schemes, that is, the energy detection based STC (ED‐STC) and the symbol detection based STC (SD‐STC), are proposed, and their performance bounds in terms of RIPS are discussed. Numerical analyses show that both ED‐STC and SD‐STC outperform traditional transmission methods in terms of effective information rate even under some practical conditions. Copyright © 2011 John Wiley & Sons, Ltd.

Multi-antenna transmission, or MIMO, is a major enabling technique for broadband cellular networks. The current implementation, however, is mainly for the point-to-point link, and its potential for Space-Division Multiple Access (SDMA) has not been fully exploited. In this paper, we will analytically evaluate the performance of SDMA in multicell networks based on a spatial random network model, where both the base stations (BSs) and users are modeled as two independent Poisson point processes. The main difficulty is the evaluation of the interference distribution, for which we propose a novel BS grouping approach that leads to a closed-form expression for the network area spectral efficiency. We find that the number of active users (U) served with SDMA is critical, as it affects the spatial multiplexing gain, the aggregated interference, and the diversity gain for each user. The optimal value of U can be selected based on our analytical result, with which SDMA is shown to outperform both the single-user beamforming and full-SDMA for which U is the same as the number of BS antennas. In particular, it is shown that the performance gain of SDMA is higher when the BS density is relatively small compared to the user density, but the optimal value of U is almost the same for different scenarios, which is close to half of the BS antenna number.

Energy harvesting (EH) has recently emerged as a promising technique for green communications, as it can power communication systems with renewable energy. In this paper, we investigate how to adopt cooperative relay selection to improve the short-term performance of EH communication systems. The main focus is on how to efficiently utilize the available side information (SI), including channel side information (CSI) and energy side information (ESI). We formulate relay selection problems with either non-causal or causal SI, with an emphasis on the more practical causal case. For this causal SI case, we propose a low-complexity relay selection strategy based on the relative throughput, that is, in each block, the relay with enough energy and with the highest instantaneous throughput compared with the average throughput is selected. This relay selection rule captures the key characteristic of EH systems, namely, each relay should have some chance to be selected so that the harvested energy can be efficiently utilized, and it should be selected only if its throughput is near its own peak. Simulation results will show that the proposed relay selection method provides significant throughput gain over the conventional one which is only based on the current side information.

This paper analyzes the outage probability of space-time network coding (STNC) with amplify-and-forward (AF) relays in a cooperative relaying system, where multiple sources transmit their information to a common destination with the help of multiple AF relays in time-division multiple-access (TDMA) mode. We derive an approximate closed-form expression of the outage probability for STNC with an arbitrary number of AF relays for independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. Numerical results validated our analysis. Moreover, with the developed result, we also discuss the impact of the transmit signal-to-noise ratio (SNR), the outage threshold, the number of relays and the nonorthogonal codes on the system performance.

The channel state information at the transmitter side (CSIT) is playing a more and more important role in the design of OFDM/OFDMA communication systems. Unfortunately, it is not trivial to conduct a performance analysis of OFDM systems with partial CSIT. Using the saddle-point approximation method, this paper will develop an analytical design and performance analysis framework for OFDM channels with 1 bit CSIT, which we shall refer to as the conditional outage exponent. The proposed framework will then allow us to present the fundamental relationship among the outage probability, transmission rate, SNR, outage capacity, delay-limited capacity, ergodic capacity, diversity-multiplexing tradeoff (DMT), finite-SNR DMT, and the number of diversity branches. It is surprising that the outage performance with 1 bit CSIT (conditional outage exponent) is worse than the corresponding one without CSIT (non-conditional outage exponent) in most cases. This counter-intuitive phenomenon occurs because the observation of the channel at the transmitter side will result in a state space collapse of the channel gains, i.e., from the prior probability space to the posterior probability space. As a result, the conditional outage exponent based framework can be easily used to design and evaluate the performance of existing and upcoming OFDM/OFDMA multichannel systems with 1 bit CSIT.

Small cell networks have recently been proposed as an important evolution path for the next-generation cellular networks. While such approach has the potential of meeting the growing network throughput requirement, the energy efficiency of small cell networks is of great concern as the base station (BS) density will be significantly increased. The objective of this paper is to analyze the energy efficiency in small cell networks. To do so, we adopt a random spatial network model, where BSs and users are modeled as two independent spatial Poisson point processes (PPPs). We shall derive analytical results for the network energy efficiency, which show that the BS power consumption model plays a critical role. In particular, it will be shown that increasing the BS density can actually improve the energy efficiency if the BS power consumption that is not related to signal transmission is less than a certain threshold. By comparing the cases between single-antenna and multi-antenna BSs, we find that single-antenna BSs provide a higher energy efficiency if the circuit power is larger than a threshold. Simulation results will demonstrate that our conclusions which are based on the random network model also hold in a regular grid-based model.

Energy harvesting (EH) has recently emerged as a promising technique for green communications. To realize its potential, communication protocols need to be redesigned to combat the randomness of the harvested energy. In this paper, we investigate how to apply relaying to improve the short-term performance of EH communication systems. With an EH source and a non-EH half-duplex relay, we consider the problem of maximizing the achievable rate for a given time duration. The half-duplex constraint at the relay renders the design problem quite challenging, as the source and relay transmission periods should be carefully scheduled. Moreover, the adaptive power allocation is needed at the source to combat the random energy arrivals. A key finding is that the optimal power allocation algorithm, called directional water-filling (DWF), for the single-hop EH system can serve as guideline for the design of a two-hop communication system, as it not only provides an achievable performance upper bound, but also forms the basis to derive the optimal solution for our design problem. Based on a modified energy profile according to the DWF power allocation, we derive key properties of the optimal solution and thereafter propose an efficient algorithm to maximize the throughput. Simulation results show that both scheduling and power allocation optimizations are necessary in two-hop EH communication systems.

This paper investigates the resource allocation for two-way relay networks with symmetric data rates from an information theoretic perspective, where a round of information exchange between two sources requiring equal end-to-end transmission rates is considered to be completed by a muti-access (MAC) phase and a broadcast (BC) phase. Decode-and forward (DF) protocol is employed. In this case, we formulate an optimization problem to maximize the sum rate of the system under total available energy. Our goal is to seek the jointly optimized time assignment between the MAC and BC phases and the power allocation among the source and relay nodes. Since the problem is difficult to solve in general, we firstly discuss it in two extreme cases by considering very low and very high system available energy. By doing so, we find an interesting result that in the very high energy case, the optimal ratio of the time assigned for the MAC phase to that assigned for the BC phase is a constant, i.e., 2 : 1. Further, we adopt such constant time assignment to general cases, and derive a closed-form power allocation for two-way relay transmissions. Extensive numerical results vitiated the proposed joint resource allocation and show that the maximum system sum-rate can be approached by our scheme, which obviously excels traditional equal time assignment and equal power distribution schemes.

It has been shown that, with the instantaneous power constraint and joint power allocation, two-way relaying (TWR) outperforms one-way relaying (OWR) in terms of system throughput. However, the conclusion is not clear for the case with the average power constraint and the case with the instantaneous power constraint but separate power allocation. In this paper, we shall first investigate the optimality of TWR with the average power constraint where we consider both of the joint power allocation among three nodes and the separate power allocation between the source and relay nodes. It will be shown that, with the average power constraint, TWR is not always better than OWR. The conditions with which one scheme outperforms the other are derived and utilized to indicate the operating regions for the two schemes. It is observed that the operating region for TWR, where TWR outperforms OWR, increases as the transmit SNR increases. A similar conclusion is obtained for the case with the instantaneous power constraint and separate power allocation.

The optimal DMT (diversity and multiplexing tradeoff) of interference channel is still unknown. In this paper, we investigate the maximum diversity gain of interference channel and try to answer two questions. Firstly, it is known that no two links can achieve their maximum DoF (degree of freedom) simultaneously in an interference channel. The question is whether two links can achieve their maximum diversity gains simultaneously over MIMO or diagonal interference channel? Secondly, it has been shown in the literature that, with IA (interference alignment) and single-beam transmission, only one-side diversity gain (transmit or receive) is achievable in MIMO interference channel. The question is whether IA is optimal in achieving diversity gain and whether both of the transmit and receive diversity gains of MIMO interference channel can be obtained simultaneously? The major contribution of this paper is to show that, although two links in MIMO interference channel can not obtain their maximum diversity gains at the same time, both the transmit and receive diversity gains are achievable. On the other hand, two links in diagonal interference channel can obtain their maximum channel freedoms at the same time, which correspond to the maximum diversity gains.

The parallel fading channel, which consists of finite number of subchannels, is very important, because it can be used to formulate many practical communication systems. The outage probability, on the other hand, is widely used to analyze the relationship among the communication efficiency, reliability, signal-to-noise ratio (SNR), and channel fading. To the best of our knowledge, the previous works only studied the asymptotic outage performance of the parallel fading channels which are only valid for a large number of subchannels or high SNRs. In this paper, a unified performance metric, which we shall refer to as the outage exponent, will be proposed. Our approach is mainly based on the large deviations theory and Meijer'sG-function. It is shown that the proposed outage exponent is not only an accurate estimation of the outage probability for any number of subchannels, any SNR, and any target transmission rate, but also provides an easy way to compute the outage capacity, finite-SNR diversity-multiplexing tradeoff, and SNR gain. The asymptotic performance metrics, such as the delay-limited capacity, ergodic capacity, and diversity-multiplexing tradeoff can be directly obtained by letting the number of subchannels or SNR tend to infinity. Similar to Gallager's error exponent, a reliable function for parallel fading channels, which illustrates a fundamental relationship between the transmission reliability and efficiency, can also be defined from the outage exponent. Therefore, the proposed outage exponent provides a complete and comprehensive performance measure for parallel fading channels.

Energy harvesting (EH) has recently emerged as a promising technique for green communications. To realize its potential, communication protocols need to be redesigned to combat the randomness of the harvested energy. In this paper, we investigate how to apply relaying to improve the short-term performance of EH communication systems. With an EH source and a non-EH half-duplex relay, we consider two different design objectives: 1) short-term throughput maximization; and 2) transmission completion time minimization. Both problems are joint time scheduling and power allocation problems, rendered quite challenging by the half-duplex constraint at the relay. A key finding is that directional water-filling (DWF), which is the optimal power allocation algorithm for the single-hop EH system, can serve as guideline for the design of two-hop communication systems, as it not only determines the value of the optimal performance, but also forms the basis to derive optimal solutions for both design problems. Based on a relaxed energy profile along with the DWF algorithm, we derive key properties of the optimal solutions for both problems and thereafter propose efficient algorithms. Simulation results will show that both time scheduling and power allocation optimizations are necessary in two-hop EH communication systems.

In this paper, we investigate the benefits of relaying by comparing the transmission rates of both direct transmission (DT) and relaying. It is shown that relaying achieves SNR (signal-to-noise power ratio) gain over DT due to less pathloss, but with several relaying penalties, including a lower multiplexing gain (due to half-duplex), a lower transmit power and a higher outage requirement at each hop (due to multi-hop). We determine the conditions over which relaying outperforms DT, where the SNR gain is greater than the loss due to relaying penalties. The result is applied to the LTE-advanced networks (LTE-A) where the relay nodes (RNs) are implemented to relay information between the user equipment (UE) and the evolutional NodeB (eNB). The major difference between LTE-A and a general relay system lies in that the UE-RN hop consists of multiple frequency-division access links, while the RN-eNB hop is a point-to-point link. By investigating the effects of diversity gain on the transmission rate, we propose an outage-capacity based adaptive relaying (OCA-R) scheme to replace the conventional same-carrier relaying (SC-R). It is shown that the transmission rates of both SC-R and OCA-R are one half of the harmonic means between the outage-capacities for two hops, where the advantage of OCA-R over SC-R comes from a higher diversity gain in the RN-eNB link.

Relaying primary signals by cognitive base-stations (CBSs) can help the primary system and thus win CBSs a higher chance to transmit their own signals. For this purpose, conventional zero-forcing (CZF) beamforming is a straightforward solution where the primary and cognitive signals are transmitted from a multi-antenna CBS without causing interference to each other. However, with CZF, no priority is given to the primary user (PU), which is not consistent with the idea of cognitive radio. In this paper, we shall propose a prior ZF (PZF) scheme which gives priority to the PU by transmitting primary signals without considering their interference to the cognitive users (CUs), while cognitive signals are not allowed to generate interference to the PU. As a result, PZF provides a better channel for the CBS-PU link than CZF but the same channel gain for the CBS-CU links as CZF. We compare PZF and CZF by considering both the transmit power with given target rates and the outage performance with given transmit power, where closed-form conditions are derived to indicate their respective advantages. One of the important contributions of this paper is to prove that, with one CU, a target rate of 1 bit/s/Hz for the CU is the key point that differentiates PZF and CZF, which is independent of the number of CBS-antennas, the channel distributions, and the signal-to-noise power ratio (SNR).

Energy harvesting (EH) has recently emerged as an effective way to solve the lifetime challenge of wireless sensor networks, as it can continuously harvest energy from the environment. Unfortunately, it is challenging to guarantee a satisfactory short-term performance in EH communication systems because the harvested energy is sporadic. In this paper, we consider the channel training optimization problem in EH communication systems, i.e., how to obtain accurate channel state information to improve the communication performance. In contrast to conventional communication systems, the optimization of the training power and training period in EH communication systems is a coupled problem, which makes such optimization very challenging. We shall formulate the optimal training design problem for EH communication systems, and propose two solutions that adaptively adjust the training period and power based on either the instantaneous energy profile or the average energy harvesting rate. Numerical and simulation results will show that training optimization is important in EH communication systems. In particular, it will be shown that for short block lengths, training optimization is critical. In contrast, for long block lengths, the optimal training period is not too sensitive to the value of the block length nor to the energy profile. Therefore, a properly selected fixed training period value can be used.

Relaying is a promising technique to extend coverage and improve throughput in wireless networks, but its performance is degraded in the presence of co-channel interference. In this paper, we consider coordinated relay beamforming to suppress interference and improve the date rates of two-hop interference networks. We first propose optimal coordinated relay beamforming algorithms to characterize the achievable rate region and maximize the sum-rate. By imposing a constraint on the desired signals, a low-complexity iterative algorithm is then proposed to maximize the sum-rate. Through performance comparison, we show that the proposed relaying strategy provides a promising tradeoff between complexity and performance. To further reduce design complexity, we propose a new interference management scheme, interference neutralization, to cancel the interferences over the air at the second hop. We show that this scheme yields a closed-form solution for the beamforming design and provides good performance especially at high signal-to-noise ratio (SNR).

In this paper, we investigate the joint subcarrier-pairing and resource allocation scheme for multi-relay aided two-way relay OFDM networks, where power allocation, subcarriers assignment and relay selection are taken into account. It is assumed that amplify-and-forward relaying protocol is deployed on all relay nodes to assists the information exchange between two sources via orthogonal subchannels. In this case, we formulate an optimization problem to maximize the total end-to-end transmission rate of the system under individual power constraints at each node. The goal is to seek the jointly optimized subcarrier pairing, subcarrier-pair-to-relay selection and power allocation. To solve the problem, we derive an asymptotically optimal scheme by adopting the dual decomposition approach of mixed-integer programming problems. Finally, simulation results are presented to demonstrate the performance of the proposed scheme.

In this paper, a random combinatorial optimization approach, which we shall refer to as random bipartite graph (RBG) based maximum matching, will be proposed to investigate and solve the joint relay selection and subchannel allocation problem in cooperative networks. By studying the properties of the maximum matching on RBG, the outage probability and diversity-multiplexing tradeoff of the proposed RBG matching method will be obtained. It will then be demonstrated that the outage probability and diversity-multiplexing tradeoff of the RBG matching method for cooperative communication systems with multiple source-destination pairs is the same as that of relay systems with only one source and one destination, i.e., d(r) = N (K + 1)(1 - 2r), where N is the number of subchannels, and K is the number of relay nodes. In addition, it will be shown that the proposed algorithm for maximum matching enjoys a sublinear computation complexity O(N2/3). Simulation results will illustrate the potential of the proposed RBG matching method as well as verify the theoretical derivations.

Maximizing the sum rate of a wireless network with multiple interfering links is an important and challenging problem in communication systems. This difficult non-convex problem has been approached from both an algorithmic perspective to achieve global optimality (e.g., using d.c. programming) and a relaxation perspective to obtain approximate solutions (e.g., high signal-to-noise-ratio approximation, binary power control, network symmetry, game-theoretic reformulation, etc.). It is generally agreed that 1) the global algorithms suffer from scalability issues and are more appropriate for problems of small instances; and 2) the solutions obtained based on maximizing the instantaneous performance are most likely suboptimal in practical fading wireless networks. In this work, we demonstrate that the sum rate can be efficiently optimized using the tool of stochastic geometry. In particular, we show that the average network sum rate can be derived in closed-form, taking into account both the random spatial distribution of the transmitters and the random Nakagami channel fading. An optimal contention density is further derived, which indicates the optimal number of supportable concurrent transmissions that attains the maximal sum rate. We discuss several applications of the derived results in interference-limited wireless systems.

Interference alignment (IA) is a powerful technique to handle interference between links that share the same wireless channel. It has been shown that IA can asymptotically help each link to achieve half of the degrees of freedom (DoF). This result solves one endpoint of the optimal diversity and multiplexing tradeoff (DMT) for interference channels, namely, the maximum multiplexing gain. In this paper, we shall focus on the other endpoint to investigate the maximum diversity gain. For an interference channel with K links where each link has a channel freedom (independent channels) of L, zero-forcing algorithm can achieve a diversity gain of L-K +1 for each link and a sum diversity gain of K(L - K + 1) for the whole network. The question we want to answer is whether interference alignment can provide a higher diversity gain, which will help determine the optimal DMT of interference channels. It will be shown that IA can increase the sum diversity gain by K - 1, where at most K - 2 of them can be utilized by one link.

In this paper, we propose a relay assisted spectrum sharing (RASS) scheme based on the mixed sharing strategy in cognitive radio networks. Mixed sharing is a more general sharing strategy which provides a higher spectrum utilization efficiency than underlay sharing and interweave sharing. Compared to conventional mixed sharing, the proposed approach enhances the throughput of secondary users while not causing harmful interference to the primary receiver. The optimal time allocation which maximizes the achievable capacity of the secondary system is derived. At the same time, the existence of the optimal sensing time is proved and the optimal sharing time allocation between the two-hop relay links is presented. Numerical results show that there exists global optimal time allocation for sensing and sharing in the proposed RASS scheme.

This paper investigates the resource allocation problem to minimize the downlink transmission delay under power constraints for two-way relay transmission using network coding over OFDM channels, where two sources with unbalanced traffic exchange their information via a relay node with network coding deployed. Since the explicit solution to this optimization problem is hard to obtain and with extremely high computation complexity even for numerical solution, we propose low-complexity suboptimal algorithms for the problem, where subcarrier assignment is carried out by assuming an equal power distribution at first and then optimal power allocation is executed to minimize the transmission delay. By simulations, the proposed resource allocation scheme is shown to achieve less than 1.01 times the optimal delay and outperform the strategies without network coding in overwhelming majority cases.

Analysis of effective information transmission rate over fading channels has attracted much attentions in the last few years. Ergodic capacity and outage capacity, as two conventional indices, have been widely investigated in various scenarios. However, there exists a gap between them for any fixed average signal to noise ratio. Thus, one problem is raised naturally: How to fill this gap? To answer it, we shall propose a new concept, є-overflow rate, which is used to characterize the transmission capability of a fading channel when a finite size buffer is employed at the transmitter. With this buffer, the constant rate source data stream is matched with the time varying channel status so that the fading channel can support a higher rate source data stream. It will be proved that the є-overflow rate is larger than the є-outage capacity under the same outage constraint and can converge to the ergodic capacity in all signal to noise ratio region.

The degrees of freedom (DoF) has been recognized as a powerful metric to characterize the capacity of interference channels in the high signal-to-noise (SNR) region. In this paper, by utilizing linear interference alignment, we investigate the DoF of multiple-input and multiple-output (MIMO) interference relay channels without symbol extensions. An innovative algorithm is presented to align the interference, where the filter matrices at the sources, relays and destinations are determined in an iterative manner. Based on the assumption that improperness of the alignment condition implies its unsolvability, an upper bound for the achievable DoF tuple by linear interference alignment is derived, and then utilized to examine the performance of the proposed alignment algorithm. Simulation results show that the iterative algorithm can achieve the upper bound in medium to high DoF regions.

Given the growing concern over energy consumption and associated global warming, green communication is becoming more and more important. Lots of efforts have been put into investigating energy efficiency based design in wireless systems. Unfortunately, the maximum energy efficiency of a point-to-point link is normally achieved when the transmit power approaches zero, which, however, is not desirable in practical systems due to the low achievable data rate. In this paper, we consider the energy efficiency optimization with a practical power consumption model, where, besides a maximum transmit power constraint, we set a rate constraint (r0) to guarantee an average throughput requirement (Rth). Due to the possible outage in transmission, r0is in general different from Rth, and determining r0for a given Rthis not trivial. We shall derive a closed-form solution for the optimal transmit power that maximizes energy efficiency. We will also demonstrate that a carefully selected rate constraint r0can guarantee the required average throughput Rthand provide the freedom to achieve different tradeoffs between energy efficiency and average throughput.

Recent works have shown that cooperative diversity can be achieved by using relay selection (RS), distributed space-time coding (DSTC), and distributed beam-forming (DBF) in narrow-band decode-and-forward (DF) cooperative networks with one source-and-destination (s-d) pair. However, the joint resource allocation for broadband DF cooperative networks with multiple s-d flows has not received much attention yet. In this paper, a random hypergraph based unified matching framework is proposed, under which five feasible types of multi-flow DF cooperative networks will be considered. In each type, the maximum matching method will be applied to RS, DSTC, and DBF schemes so as to achieve the optimal channel allocation and relay selection with fairness assurance. By analyzing the properties of maximum matching, the outage probability of each s-d pair after resource allocation will be obtained. The results of diversity-multiplexing tradeoff will show that the proposed framework is capable of achieving the full frequency diversity and cooperative diversity for each s-d pair simultaneously, while the frequency multiplexing is equally shared. Based on the unified framework, the random rotation based parallel Hopcroft-Karp (R2PHK) algorithm will then be designed, which can work in each destination node independently, and shall enjoy a poly-logarithmic complexity O(log2loN), where N is the number of channels and lois a constant.

This paper investigates the linear precoder design for spectrum sharing in multi-antenna cognitive radio networks with finite-alphabet inputs. It formulates the precoding problem by maximizing the constellation-constrained mutual information between the secondary-user transmitter and secondary-user receiver while controlling the interference power to primary-user receivers. This formulation leads to a nonlinear and nonconvex problem, presenting a major barrier to obtain optimal solutions. This work proposes a global optimization algorithm, namely Branch-and-bound Aided Mutual Information Optimization (BAMIO), that solves the precoding problem with arbitrary prescribed tolerance. The BAMIO algorithm is designed based on two key observations: First, the precoding problem for spectrum sharing can be reformulated to a problem minimizing a function with bilinear terms over the intersection of multiple co-centered ellipsoids. Second, these bilinear terms can be relaxed by its convex and concave envelopes. In this way, a sequence of relaxed problems is solved over a shrinking feasible region until the tolerance is achieved. The BAMIO algorithm calculates the optimal precoder and the theoretical limit of the transmission rate for spectrum sharing scenarios. By tuning the prescribed tolerance of the solution, it provides a trade-off between desirable performance and computational complexity. Numerical examples show that the BAMIO algorithm offers near global optimal solution with only several iterations. They also verify that the large performance gain in mutual information achieved by the BAMIO algorithm also represents the large gain in the coded bit-error rate.

ABSTRACT Secure transmission of information over wireless channels in the presence of an eavesdropper has attracted much attention recently. Previous work assumes that a transmitter has perfect knowledge of the channel side information (CSI) of the legitimate channel and the eavesdropper's channel. To loosen this strong requirement, this paper considers the case where a transmitter has perfect knowledge of the CSI of the legitimate channel and only the average channel gain of the eavesdropper's channel. In this case, some information may be leaked to the eavesdropper because the eavesdropper's channel may have a higher gain than the legitimate channel. Hence, we propose, for the first time, to study leakage‐probability‐constrained secrecy capacity of a fading channel, that is, secrecy capacity under the constraint on leakage probability. We also propose an optimal power allocation strategy that maximizes leakage‐probability‐constrained secrecy capacity under average power constraint. Moreover, we study the impact of the bias of the average gain estimate of the eavesdropper's channel on leakage‐probability‐constrained secrecy capacity. Copyright © 2011 John Wiley & Sons, Ltd.

Large-scale propagation effect plays an important role in multiple-input multiple-output (MIMO) relay systems. In particular, the position of the relay (between the source and destination) determines the path-loss effects of adjacent hops, which will further affect the performance of each hop and thus the relay system. In this paper, by minimizing the outage probability of a decode-and-forward (DF) MIMO relay system with orthogonal space-time block coding, we show that relay position optimization improves the finite-SNR (signal-to-noise ratio) diversity gain of a relay system whose adjacent hops have different diversity orders (unbalanced system). Specifically, with relay position optimization, the diversity gain is no longer bounded by that of the weaker hop, i.e., the hop with a lower diversity order, but approaches the diversity order of the stronger hop. This diversity improvement provides a dramatic improvement for the end-to-end outage probability. It will also be shown that although power allocation has no effects on the achievable diversity order, it provides some SNR gains.

This paper investigates the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (DMT) of point-to-point wireless channels assisted by multiple relays. Results are derived for both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. For the AF protocol, we derive accurate approximations for the system outage probability when the relays are clustered between the source and destination. For the case where all the relays are clustered near the destination, an exact closed-form expression for the system outage probability is obtained. For the DF protocol, under general multiple relaying configurations, we derive an exact closed-form expression for the system outage probability. The outage results for AF and DF are used subsequently to yield expressions for the finite-SNR DMT. We also extract the conventional asymptotic DMTs as special cases of the finite-SNR results, and demonstrate that the asymptotic DMTs can significantly overestimate the level of diversity that is achievable for practical error rates and SNRs.

In a two-hop system with multiple amplify-and-forward (AF) relays, the instantaneous channel state information (CSI) is usually required at the relays to achieve a coherent combining at the destination. In this paper, we investigate the design of non-coherent relaying systems, where no CSI is available at the relays. It will be shown that non-coherent relaying can achieve the optimal diversity and multiplexing tradeoff (DMT) of coherent relaying systems with no penalty in the coding length, but incurs a loss of combining gain. To illustrate the practicality of non-coherent relaying, we propose a simple spreading-based non-coherent relaying scheme which achieves the optimal diversity gain. However, the spreading scheme demonstrates a unique non-coherent penalty, namely, the inclusion of one non-coherent relay may introduce "negative" contribution to the receive SNR. To handle this penalty, a distributed relay selection scheme, which can achieve a better outage performance with less energy, is proposed.

Network coding, which can combine various traffic flows or packets via algebraic operations, has the potential of achieving substantial throughput and power efficiency gains in wireless networks. As such, it is considered as a powerful solution to meet the stringent demands and requirements of next-generation wireless systems. However, because of the random and asynchronous packet arrivals, network coding may result in severe delay and packet loss because packets need to wait to be network-coded with each others. To overcome this and guarantee quality of service (QoS), we present a novel cross-layer approach, which we shall refer to as Buffer-Aware Network Coding, or BANC, which allows transmission of some packets without network coding to reduce the packet delay. We shall derive the average delay and power consumption of BANC by presenting a random mapping description of BANC and Markov models of buffer states. A cross-layer optimization problem that minimizes the average delay under a given power constraint is then proposed and analyzed. Its solution will not only demonstrate the fundamental performance limits of BANC in terms of the achievable delay region and delay-power tradeoff, but also obtains the delay-optimal BANC schemes. Simulation results will show that the proposed approach can strike the optimal tradeoff between power efficiency and QoS.

In the paper, we study the service process S(t) of an independent and identically distributed (i.i.d.) Nakagami-m fading channel, which is defined as the amount of service provided, i.e., the integral of the instantaneous channel capacity over time t. By using the Moment Generation Function (MGF) approach and the infinitely divisible law, it is proved that, other than certain generally recognized curve form or a stochastic process, the channel service process S(t) is a deterministic linear function of time t, namely, S(t)=c_m* \cdot t where c_m* is a constant determined by the fading parameter m. Furthermore, we extend it to general i.i.d. fading channels and present an explicit form of the constant service rate c_p*. The obtained work provides such a new insight on the system design of joint source/channel coding that there exists a coding scheme such that a receiver can decode with zero error probability and zero high layer queuing delay, if the transmitter maintains a constant data rate no more than c_p*. Finally, we verify our analysis through Monte Carlo simulations.

The problem of distributed power allocation for an interference relay network is considered, where multiple source-relay-destination (S-R-D) links communicate concurrently in the same frequency and interfere over two hops, referred to as a two-hop interference channel. The direct source-destination connections are not considered in this work. Power allocation in this scenario is challenging as it necessitates not only performance tradeoff among multiple links but also power distribution for each link along spatial dimensions. We approach the problem from a game-theoretic perspective and propose a novel implicit-based approach to prove the uniqueness of the Nash equilibrium (NE). This technique complements the existing literature on equilibria analysis by revealing the benefits of expressing the best responses in implicit forms. To benchmark the performance of the NE, we have investigated the non-convex sum-utility maximization problem, and have (1) identified the optimal solution structures; (2) proved that linear pricing is locally optimal in maximizing the sum utilities. A simple and distributed pricing algorithm is then proposed. Simulation results show that for a two-user network, the proposed scheme closely approaches the optimal sum rate, while, for a ten-user network, the improvement is up to 340% compared to the non-cooperative game model without pricing.

ABSTRACT In this paper, we consider information transmission over a block Rayleigh fading channel, where a finite size buffer is employed to match the source traffic with the channel service capability. Given the buffer size, the transmission capability of a block fading Rayleigh channel is characterized from two aspects: (i) the buffer behavior when the input traffic rate is constant; and (ii) the traffic rate that can be supported by the channel for a given overflow probability constraint. For the first problem, the stationary distribution of the queue length in the buffer is derived by discretizing the queue length using a uniform quantization strategy. It is also shown that the overflow probability of the finite size buffer decreases exponentially with buffer size. An explicit upper bound on the overflow probability is also given. For the second one, a new concept of ε‐overflow rate is proposed to measure the transmission capability of a block fading channel under overflow probability constraints. It will be shown that the ε‐overflow rate is larger than the ε‐outage capacity under the same outage constraint and will meet the great gap between outage capacity and ergodic capacity as the overflow probability constraint varies. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous work on PF only considers the single cell scenario. This paper focuses on the problem of achieving network‐wide PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using the dual method. By decomposing the dual objective function, we get a distributed pricing based algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still apply in the presence of time‐varying rates. The proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that the proposed distributed network‐wide PF scheduling algorithm achieves almost the same performance as the centralized one. Compared with traditional local PF (LPF) scheduling, the network‐wide PF scheduling achieves higher throughput, lower throughput oscillation, and greater fairness. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we investigate spatial intercell interference cancellation - an efficient technique to mitigate intercell interference in multicell networks. We consider a practical model for channel state information (CSI), where the transmit CSI is acquired through downlink training and uplink feedback. Due to the requirement of channel information from multiple base stations, the training and feedback design is quite different from conventional single-cell processing systems. We optimize training and feedback, where both analog and digital feedback is considered. For analog feedback, it is shown that the downlink training optimization provides a more significant performance gain than feedback optimization; while conversely for digital feedback over a finite-rate feedback channel, the feedback bit allocation is more important than the training optimization.

Analog network coding is a promising technique which can greatly improve the transmission efficiency of wireless communications. In two-way relay systems with multiple subchannels, multiple user pairs and multiple relays, however, the optimal joint relay selection and subchannel allocation problem has not been studied in a systematic way. In this paper, a random combinatorial optimization approach, referred to as the weighted random bipartite graph (WRBG) based minimum weighted matching (MWM) method, will be proposed to solve this problem. By analyzing the properties of the MWM on WRBG, we shall derive the outage probability and diversity-multiplexing tradeoff of each user after relay selection and channel allocation. Theoretical results will demonstrate that the outage probability, cooperative diversity, and frequency diversity of the proposed WRBG based MWM method for multi-user two-way relay systems is the same as that of two-way relay systems with only one user pair. The proposed algorithm for MWM also enjoys a low computation complexity of O(log2N) for parallel implementations, where N is the number of subchannels. Simulation results will illustrate the potential of the proposed method and also verify the theoretical derivations.

In this paper, we first introduce the new proposed measure on channel capacity, reliable throughput, which set up a coherent relationship among the error detection probability, signal transmission rate and channel capacity in a systematic way. Based on the new measure, we then discuss the efficient transmission rate for single input single output (SISO) Rician fading channels and further confirm such a finding for Nakagamim fading channels that both high transmission efficiency and low outage probability can be achieved simultaneously only for either very high signal to noise ratio case or very low signal to noise ratio case. Furthermore, we apply the developed reliable throughput to discuss Multiple Input Multiple output (MIMO) transmission over Rayleigh fading channels and get some important insights that (1) MIMO system will have much higher reliable date rate and much lower outage probability compared to the single input single output system. (2) For the same diversity order, the system with more receiver antennas will have higher reliable data rate as the signal to noise ratio is relatively low. In contrast, as the signal to noise ratio is relatively high, the system with the same numbers of antennas at the transmitter and receiver will have higher reliable data rate.

In cognitive radio networks (CRNs), dynamic spectrum access has been demonstrated as an effective way to improve the spectrum utilization. Spectrum holes can be exploited not only in certain time slots or frequency bands, but also at particular locations. In relay assisted CRNs, one relay at a certain location can help to identify and provide different spectrum holes over multiple channels. In this paper, a multi-dimensional combinatorial optimization problem is formulated for joint relay selection and channel allocation. We propose a weighted bipartite graph model and a minimum weighted assignment approach to efficiently get the optimal solution of the considered problem. Simulation results show that by applying this approach, spectrum efficiency, relay selection diversity and power efficiency can be improved simultaneously for the cognitive users. Besides, only the statistical channel state information is needed and the allocation results can be computed efficiently by using the proposed approach.

This paper considers the problem of resource allocation for a large-scale wireless network consisting of multiple amplify-and-forward cooperative systems. The traditional competitive design leads to a network collapse where every system obtains almost zero throughput. We resolve this issue by resorting to the novel economic model of coalition formation with externalities by first letting the systems (modeled as players) self-organize into mutually beneficial coalitions, followed by a sequential non-cooperative game with improved player profiles. The results on the two-player game has been generalized to the case of an arbitrary number of players, incorporating additional peak power constraints as well. The uniqueness of the Nash equilibrium is proved analytically. Simulation results show that the proposed method improves the sum rate over 200% relative to the non-cooperative case due to improved frequency reuse.

Relaying of the primary signals by cognitive base- stations (CBSs) can help the primary system and thus win CBSs a higher chance to transmit their own signals. For this purpose, conventional zero- forcing (CZF) beamforming is a straightforward solution where the primary and cognitive signals are transmitted from a multi-antenna CBS without causing interference to each other. However, with CZF, no priority is given to the primary signals, which is not consistent with the idea of cognitive radio. In this paper, we shall propose a prior ZF (PZF) method which gives priority to the primary users (PUs) by allowing the primary signals to be transmitted without considering their interference to the cognitive users (CUs), while the cognitive signals are not allowed to generate any interference to the PU. As a result, PZF provides a higher effective channel gain for the CBS-PU link and thus is preferred by the PU. The comparison of CZF and PZF with respect to the CU's performance is dictated by a tradeoff between a higher cognitive signal power (with PZF) and a lower primary interference (with CZF). It is shown that, with one CU, a target rate of 1 bit/s/Hz for the CU is the key point that differentiates the advantage of PZF and CZF, and this point is independent of the number of CBS- antennas, channel conditions, and transmit SNR.

Cooperative relaying and network coding have attracting more attention recently. In this paper, we combine these two techniques together and present a Network Coding-aware Cooperative Relaying (NC2R) scheme for downlink cellular networks, in which two relay nodes are used to assist base stations in transmitting signals to cell-edge users. Moreover, we analyze its SINR performance and its spectral efficiency. Extensive simulations show that NC2R greatly improves the downlink transmission performance for users located near celledge regions, and outperforms existing known relaying schemes in terms of blocking probability and spectral efficiency. In addition, the effects of relay position on the performance of NC2R are also discussed.

Relay selection has been shown to be a practical and effective way to achieve cooperative diversity. In wide-band OFDM cooperative communication systems with multiple source and destination nodes, however, the best relay selection and subchannel allocation has not been studied in a systematic way. In this paper, a random combinatorial optimization approach, referred to as the random bipartite graph based maximum matching (RBG matching), will be proposed to solve this problem. By applying the method of Euler beta function and generalized hypergeometric function, we will first derive a new closed-form outage probability for the best relay selection in the decode-and-forward (DF) scheme. Based on this result and the properties of the maximum matching on RBG, the outage probability and diversity-multiplexing tradeoff of the proposed RBG matching method will also be derived. As a result, we will demonstrate that the outage probability and the cooperative and frequency diversity-multiplexing tradeoff of the RBG matching method for cooperative communication systems with multiple source-destination pairs is the same as that of relay systems with only one source and one destination. Besides, the proposed algorithm for maximum matching also enjoys a sublinear computation complexity O(N2/3), where N is the number of subchannels. Simulation results will illustrate the potential of the proposed RBG matching method, and also verify the theoretical derivations.

Cooperative diversity is an emerging and powerful solution that can significantly improve the diversity order and link reliability over the harsh wireless fading channels. Although there has been a lot of work on achieving full diversity by using advanced space-time coding or signal processing schemes, how to increase the spectral efficiency or multiplexing gain with half-duplex relays has not received much attention so far. In this paper, we will present a spectral efficient cooperative diversity method, which adopts our recently proposed Network Interference CancEllation (NICE) to thoroughly mitigate the inter-relay interference in successive relaying, where a source and a relay may send different messages simultaneously. We shall also investigate the optimal diversity multiplexing tradeoff of the proposed method, and show that its diversity gain is strictly greater than that of the two-timeslot relaying protocols for large multiplexing gains. Moreover, the decoding algorithm for the relays and destination has a similar complexity as that of the decision feedback equalizer.

Cognitive radio (CR) has been recently proposed as a promising technology to improve the spectrum utilization. In this paper, we consider the spectrum sharing between CR users and a licensed primary user (PU) in order to enhance the spectrum efficiency. In the considered scenario, the PU is located at a far position from the primary base station (PBS) which makes it difficult to obtain a successful transmission. In this case, the cognitive base station (CBS) at the vicinity of the primary network, offers its help to transmit the primary data. As a reward, the CR system will be able to share the spectrum with the primary system. With the deployment of multiple antennas at the CBS, quality-of-service (QoS)-aware spectrum underlay CR network is considered. In particular, we formulate a "fair'' opportunistic spectrum sharing approach that determines the optimal beamforming weights in order to maximize the overall minimum throughput of the CRs while guaranteeing the QoS of the PU. The original optimization problem is non-convex and does not have any closed-form solution. However, using a convex optimization approach, we transform it to a convex form and find an approximate solution using standard numerical methods. Simulation results will demonstrate the effectiveness of the proposed approach.

In a multi-relay system, the instantaneous channel state information (CSI) is usually required at relays to achieve the space diversity. In this paper, we propose a delayed noncoherent relaying scheme to achieve space diversity without requiring knowledge of the instantaneous CSI at the relays. Specifically, each relay will non-coherently amplify and forward its received signals with different delays such that a virtual multipath channel is created between the source and destination. We will first determine the most efficient way to achieve the optimal diversity gain. We will then show that, with spreading or coding, the proposed non-coherent relaying can provide the same diversity order as its coherent counterpart but with a noncoherent penalty. With the simplest spreading scheme, the noncoherent penalty results in a loss of both multiplexing gain and finite-SNR diversity gain. To recover the loss due to this noncoherent penalty, a distributed relay selection algorithm, which can achieve a better outage performance with less energy, is proposed. On the other hand, with the Gaussian-coded noncoherent relaying, no multiplexing gain loss will be incurred but a loss of SNR gain will occur.

In the paper, we consider the end-to-end routing and link scheduling problem for multi-hop wireless sensor networks. The efficient link scheduler under our consideration is intended to assign time slots to different users so as to minimize channel usage subject to constraints on data rate, delay bound, and delay bound violation probability. We also present a coupled robust multi-path routing structure satisfying the restriction of flows over fading channels based on an SINR-based interference model. Here the effective capacity (EC) model is used and then the joint routing and link scheduling can be formulated as a mixed integer optimization problem. Moreover, because the mixed integer optimization problem is NP-complete, we propose a computationally feasible EC-based Column-Generation-Algorithm (EC-CGA) to search for a sub-optimal solution. Simulation results are given to evaluate the performance of our proposed scheme.

Signal-Time Coding (STC), a novel transmission mechanism, was proposed recently. It combines the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in the time domain and can achieve higher information flow rate in some cases for relay networks. However, there are still many fundamental problems to be investigated. This paper considers the implementing issue of STC in AWGN relay networks. Firstly, an energy detection based STC (ED-STC) scheme is proposed and the error probabilities of ED-STC in both the signal domain and the time domain are given. Secondly, a performance evaluation criterion, the reliable information per symbol (RIPS), is proposed to characterize the performance of STC in noisy wireless networks. Moreover, the performance bounds of the RIPS of ED-STC are derived. Numerical analysis show that ED-STC outperforms traditional transmission method in terms of effective information rate within some practical conditions.

Cooperative transmission has attracted much attention recently, but it mainly focused on one S-D pair or multiple source one destination case (uplink). This paper, however, investigates a multi-source-multi-destination transmission system with a single assisting relay. Under a simple scheduling strategy, outage probability and outage capacity are derived. Moreover, optimal system throughput is proposed and analyzed. Fair access strategy of each S-D pair is also given and demonstrated. Furthermore, relay selection strategy is presented and demonstrated.

In the paper, we study the channel service process of an independent and identically distributed (i.i.d.) Rayleigh channel. The channel service process S(t) is defined as the amount of service provided by the channel, i.e., integral of the instantaneous channel capacity over a time interval of length t. The channel side information (CSI) is assumed available at the receiver. Using the Moment Generation Function (MGF) approach and the infinitely divisible law, it is proved that the channel service process S(t) is a deterministic linear function of time t other than the generally recognized certain curve form, namely, S(t) = c* · t, where c* is a constant.

On behalf of the Technical Program Committee, we welcome all of you to the IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC 2011) in the beautiful campus of Bahcesehir University, Istanbul, Turkey! We are indeed delighted that this year's IEEE IWCMC accomplishes its goal under the conference theme “Making Wireless Communities,” and continues its tradition of providing the premier forum for presentation of research results and experience reporting on the cutting edge research in the general areas of wireless communications and mobile computing. This year, we received more than 1000 submissions from 51 countries worldwide. Each paper received at least three peer technical reviews, comprised of 49 Symposia Chairs/Co-Chairs and a total of more than 450 TPC members from academia, government laboratories, and industries. After carefully examining all the received review reports, the IEEE IWCMC 2011 TPC finally selected about 35% high-quality papers for presentation at the conference and publication in the IEEE IWCMC 2011 proceedings. The conference program starts on Monday July 4thwith a full day Tutorials that is free of charge to all our attendees. Then, each day starts with a keynote speaker chosen from renowned world-class leaders in the area-Dr. Rick Stevens, Dr. Mario Gerla, and Dr. Sajal Das, highlighting the latest research trends in the wireless communications, mobile computing, and networks. This year, the technical sessions reflect the continued and growing interests in a wide range of spectrum, including wireless communications and networks, cross-layer design and optimization, mobile computing, wireless sensor networks, network security, and use of wireless technologies in social emergency applications. We also added a special Workshop this year to address practical aspects of Wireless Communications and Mobile Computing, such as Multihop Wireless Network Testbeds and Experiments, Network and Communications for Advanced Society, and Federated Wireless Sensor Systems (FedSenS). There are five special sessions composed of invited papers from renowned experts from around the world. Outstanding papers will be selected for four Special Issues in well known international journals. Our objective in the future is to reduce the acceptance rate further to reach 30% and less. In addition, we would like to reduce the number of Symposia and Workshops as well to meet the conference theme.

Single carrier frequency division multiple access (SC-FDMA) has been receiving much attention as the uplink multiple access technology in the next generation communication systems due to its lower peak-to-average power ratio (PAPR) compared to OFDMA. However, it was shown that PAPR is still an issue for SCFDMA, especially with the localized subcarrier allocation (SC-LFDMA) scheme. The precoding method has been shown to be effective in reducing the peak power. However, the construction of the codewords is a nondeterministic polynomial-time hard (NP-hard) problem. In this paper, we first formulate the problem of PAPR reduction by precoding as a combinatorial problem, and then propose the semidefinite relaxation approach with which the problem can then be solved in polynomial time. It will be shown that the proposed scheme can efficiently reduce the peak power with much lower complexity. By taking the transmit power limit into consideration, we further demonstrate the existence of a tradeoff between the transmit power increase and the peak power reduction. Specifically, less stringent power constraint will lead to more significant PAPR reduction.

Due to the potential of enhancing traffic safety, protecting environment, and enabling new applications, vehicular communications, especially vehicle-to-vehicle (V2V) communications, has recently been receiving much attention. Because of both safety and non-safety real-time applications, V2V communications has QoS requirements on rate, latency, and reliability. How to appropriately design channel allocation is therefore a key MAC/PHY layer issue in vehicular communications. The QoS requirements of real-time V2V communications can be met by achieving a low outage probability and high outage capacity. In this paper, we first formulate the subchannel allocation in V2V communications into a maximum matching problem on random bipartite graphs. A distributed shuffling based Hopcroft-Karp (DSHK) algorithm will then be proposed to solve this problem with a sub-linear complexity of O(N^{2/3}), where N is the number of subchannels. By studying the maximum matching generated by the DSHK algorithm on random bipartite graphs, the outage probabilities are derived in the high (two near vehicles) and low (two far away vehicles) SNR regimes, respectively. It is then demonstrated that the proposed method has a similar outage performance as the scenario of two communicating vehicles occupying N subchannels. By solving high degree algebraic equations, the outage capacity can be obtained to determine the maximum traffic rate given an outage probability constraint. It is also shown that the proposed scheme can take an advantage of small signaling overhead with only one-bit channel state information broadcasting for each subchannel.

We investigate the capacity and beamforming optimality of multi-antenna relaying systems, given access to statistical channel information at the relay and source. Multi-antenna relay configurations are considered, for which the source as well as either the relay or destination have multiple antennas, and the relay operates with amplify-and-forward. We first compute the optimal transmission strategies at both the source and relay, and derive necessary and sufficient conditions for which beamforming achieves capacity. To gain more insight, we then employ tools from stochastic majorization theory to characterize the impact of the destination correlation and the relay gain on the capacity and beamforming optimality range. These results demonstrate some intriguing behavior, which arises due to the joint interplay between the transmit power, relay gain, and level of correlation at the destination. It is shown, among other things, that for certain transmit powers and relay gains, the beamforming optimality region becomes independent of the destination correlation. By relaxing the beamforming optimality condition, we also derive a simple explicit upper bound which gives further insights into the joint effect of the SNR and the transmit correlation on the beamforming optimality range.

A generalized multiuser cooperative concatenated coding framework is theoretically developed for not only cooperative diversity but also enhanced distance spectra. Novel schemes are proposed featuring flexible cooperation group, variable-size messages, and other practical concerns. In particular, users failing to detect their partners messages are allowed to cooperate without incurring error propagation.

Two-way wireless communication has regained significant attention recently over relay channels and network coding techniques have been adopted to improve the spectral efficiency. In the literature, most works assumed knowledge of the channel state information at the transmitter (CSIT) and focused mainly on the three-step network coding (NetC) and two-step superposition coding (SupC) schemes. This work instead targets systems without CSIT and explores in detail the features and limitations of SupC and NetC under both the two-step and three-step frameworks. Equal-rate bi-directional traffic is considered and a comparative study of the schemes is provided. The maximum goodput and robustness to channel knowledge discrepancies are evaluated. Key results include theoretically derived outage regions and performance, and the analysis of the complementary role of NetC and SupC. Special features of the three-step framework and a simple yet efficient protocol, adaptive relay-assisted and direct transmission (ARDT), which smartly exploits the direct link in the absence of CSIT, will also be presented. Optimal power allocation at the relay is also derived for the three-step SupC scheme by exploiting the channel reciprocity (no CSIT required). A significant advantage of ARDT is demonstrated over the recently developed two-step SupC scheme. Numerical results together with extensive discussions are provided.

It has been shown in the literature that, with zero-padding prefix (ZPP), the optimum diversity gain of frequency selective channels can be obtained by uncoded signals and zero forcing (ZF) equalizers. In this paper, we derive two important results about linear equalizers over frequency selective channels: 1) With cyclic prefix and any rate-1 unitary precoding, which includes the uncoded/coded-OFDM/SCFDMA systems as special cases, linear equalizers can only obtain order-1 diversity; and 2) Although linear equalizers can achieve the optimum diversity order with zero-padding prefix and uncoded signals, the required SNR (signal-to-noise ratio) is an increasing function of the symbol-length. The second result implies that we may not be able to take advantage of the diversity gain in practice due to the high SNR requirements. Special cases are also investigated to gain some physical insights about the effects of different prefixes on the performance of linear equalizers. The results in this paper can be utilized in the design of up/down-link LTE systems with linear equalizers.

The diamond relay network is an efficient cooperative networking configuration in which the source node cooperates with two selected neighbors. For such networks, we investigate the impact of the relay positioning on the performance. In particular, considering an opportunistic protocol based on the use of relaying buffers, we provide sets of constraints which, when satisfied, give sufficient conditions for simultaneously guaranteeing network stability and throughput improvement. These results are given for both symmetric and asymmetric relay locations. In contrast to prior work dealing with the diamond relay network, we also break the strong hypothesis of no interlink interference between the source and relay transmissions. Our results provide design guidelines for relay selection by establishing areas of feasible relay locations, whilst also identifying optimal positions which lead to maximum throughput gain.

OFDMA is a promising technique because it is capable of improving the transmission reliability and efficiency of multi-user wireless communications. However, previous works on the performance of OFDMA did not properly consider the fundamental relationship between multiplexing and diversity in OFDMA systems. As a comprehensive performance metric, the diversity-multiplexing tradeoff will be applied in this paper to evaluate the subcarrier allocation scheme. The OFDMA system will be formulated into a correlated random bipartite graph model, in which, whether the edges occur or not depends on the distribution of the channel fading. The \mathcal{H}-matching method, which is used to determine the maximum collection of vertex-disjoint copies of a fixed sub-graph \mathcal{H} contained in a given graph, will then be developed to address the optimal subcarrier allocation problem. Theoretical analysis will show that the proposed \mathcal{H}-matching method achieves the optimal outage performance at a given target multiplexing gain, which implies that the optimal diversity-multiplexing tradeoff can be achieved by only allocating subcarriers. Although the \mathcal{H}-matching problem is NP-complete, the proposed Random Rotation and Expansion based Hopcroft-Karp (R^2EHK) algorithm can still achieve the asymptotically optimal outage performance (i.e., optimal diversity-multiplexing tradeoff) with a sub-linear complexity. Furthermore, the channel state information needed is only one bit per subcarrier. Simulation results will verify the theoretical analysis and will show that the performance loss of the R^2EHK algorithm is negligible compared to the exhaustive search method. In addition, it is also shown that the R^2EHK algorithm has at least a 2 dB SNR gain compared to the interleaved subcarrier allocation with water-filling power allocation in IEEE 802.16 standards.

Carrier frequency offset (CFO) damages the orthogonality between sub-carriers and thus causes multiuser interference in uplink OFDMA/SC-FDMA systems. For a given CFO, such multiuser interference is mainly dictated by channel (sub-carrier) allocation, which also specifies the diversity gain of one user over multi-path channels. In particular, the positions of one user's sub-channels will determine its diversity gain, while the distances between sub-channels of the concerned user and those of others will govern the CFO interference. Two popular channel allocation methods are the localized and interleaved (distributed) schemes where the former has less CFO interference but the latter achieves more diversity gain. In this paper, we will consider the channel allocation scheme for uplink LTE systems by investigating the effects of channel allocation on both of the diversity gain and CFO interference. By combining these two effects, we will propose a semi-interleaved scheme, which achieves full diversity gain with minimum CFO interference.

We analyze the impact of imperfect channel state information at the transmitter and receiver (CSITR) on the achievable diversity gain in multi-input multi-output (MIMO) fading channels with joint rate and power control. With rate control only, we consider the system with and without a minimum spatial multiplexing gain constraint, respectively. With joint rate and power control, we show that the achievable diversity gain can be improved significantly. The conducted analysis adopts the diversity-multiplexing tradeoff framework and uses the notions of diversity gain and multiplexing gain to study the impact of noisy CSITR in MIMO fading channels.

The achievable diversity and multiplexing tradeoff (DMT) in MIMO fading channels with channel training is analyzed in this paper. We first consider a typical training scenario, where the transmitter transmits training symbols followed by data symbols, and the receiver performs channel estimation using the training symbols and then uses the imperfect channel estimates to decode the data symbols. From the DMT perspective, our results show that as long as the training power is equal to the data power, the obtained DMT result based on imperfect channel state information at receiver (CSIR) is the same as the original DMT result with perfect CSIR given in Zheng and Tse's seminal work . We extend the analysis to two-way training scenarios, with single and multiple training rounds. Our results show that two-way training together with power control can substantially improve the achievable diversity gain. Specifically, the achievable DMT with multiple training rounds can be described as a single straight line; while in the case of single training round, the achievable DMT is much lower and can be described as a single straight line or a collection of line segments, depending on the underlying training strategy and channel qualities.

OFDM is playing a more and more important role in wireless communication systems. Unfortunately, it is not trivial to conduct a performance analysis of OFDM systems. Therefore, it is highly desired to develop an analytical design and performance analysis framework for OFDM channels. In this paper, we consider a unified performance metric for OFDM channels, which we shall refer to as outage exponent. The outage exponent, which is a special exponentially tight upper bound on outage probabilities, presents the fundamental relationship among the outage probability, target transmission rate, capacity of AWGN channel, SNR, and the number of diversity branches. The SNR gains of different coding schemes and the (finite-SNR and asymptotic) diversity-multiplexing tradeoff can be obtained from the outage exponent directly. In order to calculate the outage exponent for OFDM channels, we shall apply the large deviations theory, which will not only obtain an accurate estimation of the rate function, but also the coefficient of the exponential function. It is shown that the obtained outage exponent can allow the accurate estimation of the additional power required to decrease the outage probability by a specified value. Therefore, the outage exponent can be easily used to design and evaluate the performance of existing and upcoming OFDM systems.

The secure transmission of information over wireless systems in the presence of an eavesdropper has attracted much attention. In this paper, we consider the case that full CSI of the legitimate user but only the average channel gain of the eavesdropper is known at the transmitter. In such setting, some information will be inevitably leaked to the eavesdropper from the information-theoretic view and this was not quantitatively evaluated before. A secrecy transmission framework with leakage threshold is thereby proposed. The secrecy capacity satisfying this leakage probability constraints is derived along with an optimal power allocation strategy. For Rayleigh and Nakagami-m fading channels, numerical results indicate that our framework can work well with transmitter knowledge of only average channel gain of the eavesdropper.

One of the main challenges in cognitive radio (CR) communications lies in the system robustness to uncertainties. In this paper, we examine the impact of the noise power uncertainty on the performance of various detectors in CR networks. We consider both single and multiple CR nodes. For the single CR case, we compare the performance of the energy and likelihood ratio test (LRT) detectors in the presence of noise uncertainties. It is shown that both detectors perform about the same. We shall also investigate cooperative spectrum sensing based on soft-information combining, where the cooperating CR nodes experience different noise power uncertainties. We then propose a simple detection scheme that is more robust to noise variations and uncertainties than the conventional detection schemes. Numerical results are presented to verify the theory and demonstrate the robustness improvement of the proposed detection scheme.

The resource allocation optimization for multicast service in orthogonal frequency division multiple access (OFDMA) systems is an important research topic. It is known that in traditional multicast schemes the rate in each multicast group is severely limited by the user who has the minimum channel gain (MCG) on each subchannel. To counteract this effect, in this paper, we introduce the multiple description coding (MDC) into the multiple-group multicast service optimization for the downlink OFDMA, and study the weighted sum rate (WSR) maximization methods under various system constraints and assuming discrete-rate modulation. Simulation results show that the proposed method provides a significant performance enhancement than the MCG scheme and an MDC-based near-optimal iterative bit loading (IBL) approach. Besides this, our method converges very fast and enjoys a low complexity that is linear in the numbers of users, subchannels, and modulation bit levels.

This paper considers resource allocation for two-hop interference channels where N independent source-destination pairs share the same spectrum band and interfere with each other. The absence of a central coordinating system motivates us to tackle the problem from a non-cooperative game-theoretic perspective. It is shown that when the number of transmission pairs is greater than two, analyzing the properties of the Nash Equilibrium (NE) becomes greatly challenging. With the help of a game scalarization technique, we derive sufficient conditions that guarantee the uniqueness of the NE, as well as the global convergence of the best response dynamics. Numerical results are presented to illustrate the significant rate enhancement over conventional schemes.

The diversity-multiplexing tradeoff, which relates the transmission reliability and efficiency, is an important performance metric in wireless communications. So far only an asymptotic tradeoff result has been obtained for OFDM channels, and such result is only valid for high SNRs. To characterize the outage performance of OFDM systems in realistic SNRs, a finite-SNR framework that analyzes and describes the diversity-multiplexing tradeoff will be proposed in this paper. New upper and lower bounds on outage probabilities will be derived by using the method of integral round a contour, Laurent series, and the properties of Meijer's G-function and Gamma function. The finite-SNR diversity gain, as a function of the multiplexing gain and SNR, will also be computed by Meijer's G-function. We will then show that the finite-SNR diversity-multiplexing tradeoff will converge to the corresponding asymptotic results as SNR tends to infinity. As a result, the finite-SNR diversity-multiplexing tradeoff can be used to estimate the additional SNR required to decrease the outage probability by a specified amount for a given multiplexing gain.

Cooperative communication has been receiving significant attention in recent years because of the great potential for performance improvement. In this paper, we will consider the joint design of transmit and cooperative beamforming vectors in cooperative systems with a multi-antenna source and multiple single-antenna relays. The cooperative beamforming, performed by distributed relay nodes, is different from the conventional transmit beamforming in multiple-input and multiple-output (MIMO) systems because each node only has access to the channel information of its own links. With instantaneous channel state information (CSI), the optimal beamforming vectors (transmit and cooperative) will maximize the received SNR by allocating power among different relay links, and each relay can determine the optimal operation in a distributed manner. If only statistical CSI is available, it is shown that the optimal transmit scheme degenerates to a relay selection algorithm where only one relay link is active.

Due to its lower peak-to-average power ratio (PAPR) compared with orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA) has been recently accepted as the uplink multiple access scheme in the Long Term Evolution (LTE) of cellular systems by the Third Generation Partnership Project (3GPP). However, similar to OFDMA, carrier frequency offset (CFO) can destroy the orthogonality among subcarriers and degrade the performance of SC-FDMA. To mitigate the effect of CFOs, we propose a combined minimum mean square error frequency-domain equalization (MMSE-FDE) and interference cancellation scheme. In this scheme, joint FDE with CFO compensation (JFC) is utilized to obtain the initial estimation for each user. In contrast to previous schemes, where the FDE and CFO compensation are done separately, in JFC, the MMSE FDE is designed to suppress the MUI after CFO compensation. To further eliminate the MUI, we combine JFC with parallel interference cancellation (PIC). In particular, we iteratively design the MMSE FDE equalizer to suppress the remaining MUI at each stage and obtain better estimation. Simulation results show that the proposed scheme can significantly improve the system performance.

We analyze the optimality of beamforming in multiple-input single-output relay channels without a direct link between the source and the destination. Assuming that the source has access to the channel correlation information, and that the relay operates in amplify and forward mode with a long term power constraint, we first show that the optimal transmission strategy is to transmit along the eigen-vectors of the transmit correlation matrix. The optimality of beamforming is then studied via the power allocation problem at the source. In particular, we derive a necessary and sufficient condition under which beamforming achieves the capacity. Our results demonstrate that the finite relay gain decreases the range at which beamforming is optimal.

We consider the problem of power control for two independent relay-assisted wireless systems that operate in the same spectral band. A general scenario where both systems are allowed to select between amplify-and-forward relaying and decode-and-forward relaying is studied. We adopt a game-theoretic approach and show that once both systems act non-cooperatively to optimize their own rate, they can always reach a unique Nash equilibrium (NE) from an arbitrary starting point in a totally distributed and asynchronous manner. We also provide insights on the impacts of network topology and relaying protocols on the sum rate performance of the NE through extensive numerical results.

In cognitive OFDMA networks, spectrum sensing is highly needed to accurately observe the spectrum environment, so as to avoid harmful interference to licensed users. However, to save energy, selfish users may not be willing to perform sensing. In order to encourage cognitive users to sense the multiple channels as well as guarantee fairness among the sensing users, this paper presents a joint PHY-MAC framework for cognitive OFDMA systems. In this framework, a higher access priority in the MAC layer will be rewarded to sensing users and the scheduling decision is made based on both the channel quality and sensing contribution of each user. The throughput for the cognitive system and the fairness for each cognitive user are analyzed under the joint framework. Simulation results will then show that the proposed protocol can achieve fairness and efficiency for cognitive users.

Nowadays proportional fairness (PF) scheduling has attracted much attention in various wireless systems. But most previous work just considers the systems with only one base station (or data center), which just achieves local PF. In this paper we consider the problem of achieving global PF for the multiple base station multiple user scenario. Compared with previous works in the literature, the main contributions of this paper are threefold: (1) The PF rule is employed in the multiple base station multiple user case. Here we propose an approximate gradient projection based PF scheduling scheme, GP-PF, to approach the global PF optimality. And the convergence of the proposed algorithm is proved. (2) We study the communication and computation complexity of GP-PF and show that the developed GP-PF algorithm can be implemented either in a user selection mode, or in a random accessing way. And GP-PF applies to distributed systems in the sense that it does not need any inter base station cooperation at all. (3) By simulation, it is shown that global PF leads to higher throughput and greater fairness for users than local PF.

OFDMA performs a fundamental role in wired/wireless communications. One of the key techniques in OFDMA is the resource allocation, which has been attaching much attention from both academia and industry. In this paper, we describe an innovative combinatorial method to study this problem. An OFDMA system will first be formulated into a random bipartite graph (RBG). To meet various system configurations and requirements, different matching methods will be proposed to perform subcarrier allocation. By studying the properties of RBG matching, we will obtain close-form formulas for outage probabilities so as to evaluate the performance of subcarrier allocation algorithms. It is then demonstrated that by exploiting the frequency diversity and multi-user diversity, the proposed matching method can minimize the outage probability with fairness assurance, and achieve the same diversity-multiplexing tradeoff as point-to-point OFDM systems. The induced subcarrier allocation algorithms also enjoy a sub-linear computation complexity of O(N2/3) for parallel implementations, where N is the number of subcarriers. Besides, the proposed RBG matching method only needs one-bit CSI feedback.

Diamond relay network is a kind of efficient cooperative network, in which the source user selects two neighbors as relays and cooperates with both of them. In our previous work, we have studied its transmission rates for different coding and relaying schemes and proved that the performance of SRP scheme (Spatial Reuse Pattern) is better than others. To further improve the performance, we incorporated the opportunistic scheduling and studied how to use buffers adapted to the time varying Rayleigh fading channel. However, the position information of users has not been considered yet which actually has a great influence on the performance of the diamond relay channel. Thus, in this paper, it is the first time that we study how to select the position of two relays to guarantee the performance of the throughput and delay constraint. This study facilitates the practical application of diamond relay networks since the source user knows that from where selecting neighbors as relays will bring nice performance. Finally, the simulation results confirm our analysis and some interesting illustrations show much novelty.

Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous works on PF only consider the single cell scenario. This paper focuses on the problem of achieving global PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using dual method. By decomposing the dual objective function, we get a pricing based PF algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still achieve network wide PF in the presence of time varying rates. We show that the proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that compared with traditional local PF scheduling, global PF scheduling achieves higher throughput, lower throughput oscillation and greater fairness.

This paper considers the problem of optimal subcarrier allocation in OFDMA systems to achieve the minimum outage probability while guaranteeing fairness. The optimal subcarrier allocation algorithm and the maximum frequency diversity gain are both analyzed through the maximum matching method based on the random bipartite graph theory. Accordingly, a surprising result is found, which shows that the maximum frequency diversity gain in subcarrier-sharing OFDMA systems is the same as that in point-to-point OFDM systems that serve only one user by using N subcarriers. It is then demonstrated that this maximum frequency diversity gain can be achieved by a proposed Random Vertex Rotation based Hopcroft-Karp (RVRHK) algorithm with the time complexity of O(N2.5), where N is the number of subcarriers. Because the theoretical analysis and the RVRHK algorithm are both based on the maximum matching method, the maximum frequency diversity in OFDMA systems is referred to as the max-matching diversity in this paper.

In this paper, we propose a limited feedback scheme to improve outage performance for a wireless cooperative decode-and-forward network. Specifically, based on the instantaneous conditions of the source-destination and relay-destination channels, the destination will allocate the transmission time of the source and relay and feed back the allocation result to the source. Both limited and full (or infinite) rate feedback are considered. Under the practical assumption that only imperfect channel estimation is available at the receiver, we analyze the outage performance by deriving upper bounds on the outage probabilities. It will be demonstrated that, even with only one-bit feedback, the proposed feedback scheme can outperform the no feedback case. Furthermore, the outage performance can approach the optimality by exploiting limited (only a small number of bits) uniformly quantized feedback from the destination.

In this paper, we investigate the impact of imperfect channel state information at the transmitters (CSIT) on the achievable diversity gain in a cooperative relaying channel with multiple destination antennas, where the CSIT comes from channel estimation at the transmitters. Both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols are considered. We show that transmit power control based on the imperfect CSIT significantly improves the achievable diversity gain. The diversity and multiplexing tradeoff (DMT) as a function of the CSIT quality of the source-relay link, source-destination link and relay-destination link is derived for each of the considered relaying schemes. An upper bound on the DMT of the relaying channel is also provided.

We consider frame synchronization for fixed frame length communication system with periodically embedded sync words. In the past few decades, numerous researchers have made significant contributions in various aspects. However, it is almost surprising that a scrutinization over the literature reveals to us an important common assumption: frame synchronization decision-making through single-frame observation. Intuition tells us that an enlarged multiple-frame observation set bears the potential to reduce sync word identification ambiguities and also to combat multi-path fading through ¿frame diversity¿. The objective of this work is thus to characterize the performance improvement of using multiple-frame decision rules rather than the single-frame decision rules considered in the previous work. We select the work of Lui and Tan as our standing point, and have conducted a comparative study in a very comprehensive manner. In particular, we shall show that the benefits of multipleframe based decision rules can be substantial. For example, in many cases, the performance of the correlation rule based on double-frame observation is superior to that of the optimum maximum likelihood rule with single-frame observation. While further enlarging the observation period beyond double frames was found to yield diminishing returns.

Spectrum sensing is critical for cognitive systems to locate spectrum holes. In the IEEE 802.22 proposal, short quiet periods are arranged inside frames to perform a coarse intra-frame sensing as a pre-alarm for fine inter-frame sensing. However, the limited sample size of the quiet periods may not guarantee a satisfying performance and an additional burden of quiet-period synchronization is required. To improve the sensing performance, we first propose a quiet-active sensing scheme in which inactive customer-provided equipments (CPEs) will sense the channels in both the quiet and active periods. To avoid quiet-period synchronization, we further propose to utilize (optimized) active sensing, in which the quiet periods are replaced by 'quiet samples' in other domains, such as quiet sub-carriers in OFDMA systems. By doing so, we not only save the need for synchronization, but also achieve selection diversity by choosing quiet sub-carriers based on channel conditions. The proposed active sensing scheme is also promising for spectrum sharing applications where both the cognitive and primary systems can be active simultaneously.

Cooperative diversity has emerged as a promising technique to facilitate fast handoff mechanisms in mobile ad-hoc environments. The key concept behind a prominent cooperative diversity based protocol, namely, Partner-based Hierarchical Mobile IPv6 (PHMIPv6), is to enable mobile nodes anticipate handover events by selecting suitable partners to communicate on their behalves with Mobility Anchor Points (MAPs). In the original design of PHMIPv6, mobile hosts choose partners based on their signal strength. Such a naive selection procedure may lead to scenarios where mobile hosts lose communication with the selected partners before the completion of the handoff operations. In addition, PHMIPv6 overlooks security considerations, which can easily lead to vulnerable mobile hosts and/or partner entities. As a solution to these two shortcomings of PHMIPv6, this paper first proposes an extended version of PHMIPv6 called Connection Stability Aware PHMIPv6 (CSA-PHMIPv6). In CSA-PHMIPv6, mobile hosts select partners with whom communication can last for a sufficiently long time by employing the Link Expiration Time (LET) parameter. To tackle the security issues, the simple yet effective use of two distinct authentication keys is envisioned. Furthermore, to shorten the communication time between mobile hosts and their corresponding partners, a second handoff management approach called Partner Less Dependable PHMIPv6 (PLD-PHMIPv6) is proposed.

Single carrier interleaved frequency division multiple access (SC-IFDMA) has been recently receiving much attention for uplink multiuser access in the next generation mobile systems because of its lower peak-to-average transmit power ratio (PAPR). In this paper, we investigate the effect of carrier frequency offset (CFO) on SC-IFDMA and propose a new low-complexity time domain linear CFO compensation (TD-LCC) scheme. The TD-LCC scheme can be combined with successive interference cancellation (SIC) to further improve the system performance. The combined method will be referred to as TD-CC-SIC. We shall study the use of user equipment (UE) ordering algorithms in our TD-CC-SIC scheme and propose both optimal and suboptimal ordering algorithms in the MMSE sense. We also analyze both the output SINR and the BER performance of the proposed TD-LCC and TD-CC-SIC schemes. Simulation results along with theoretical SINR and BER results will show that the proposed TD-LCC and TD-CC-SIC schemes greatly reduce the CFO effect on SC-IFDMA. We also propose a new blind CFO estimation scheme for SC-IFDMA systems when the numbers of subcarrier sets allocated to different UEs are not the same due to their traffic requirements. Compared to the conventional blind CFO estimation schemes, it is shown that by using a virtual UE concept, the proposed scheme does not have the CFO ambiguity problem, and in some cases can improve the throughput efficiency since it does not need to increase the length of cyclic prefix (CP).

Abstract Diamond relay network model, as a basic transmission model, has recently been attracting considerable attention in wireless ad hoc networks. Node cooperation and opportunistic scheduling scheme are two important techniques to improve the performance in wireless scenarios. In the paper we consider such a problem how to efficiently combine opportunistic scheduling and cooperative modes for the Rayleigh fading scenario in the diamond relay network. To do so, we first compare the throughput of SRP (Spatial Reused Pattern) and AFP (Amplify Forwarding Pattern) in the half‐duplex case with the assumption that channel side information is known to all and then come up with a new scheduling scheme. It will be verified that only switching between SRP and AFP simply does little help to obtain an expected improvement because SRP is always superior to AFP on average due to its efficient spatial reuse. To improve the throughput further, we put forward a new processing strategy in which buffers are employed at both relays in SRP mode. By efficiently utilizing the links with relatively higher gains, the throughput can be greatly improved at a cost of queuing delay. Furthermore, we shall quantitatively evaluate the queuing delay and the tradeoff between the throughput and the additional queuing delay. Finally, to realize our developed strategy and make sure it always run at stable status, we present two criteria and an algorithm on the selection and adjustment of the switching thresholds. Copyright © 2009 John Wiley & Sons, Ltd.

OFDMA technology can significantly improve the transmission reliability and efficiency because of its inherent frequency diversity and frequency multiplexing. In our recent work [B.Bai,W.Chen, Z.Cao and K. B. Letaief (2009) ], we have derived the optimal diversity-multiplexing tradeoff for OFDMA systems under the assumption that each subcarrier occupies the entire coherence bandwidth. However in practical OFDMA systems, such as IEEE 802.16, there are many subcarriers in one coherence bandwidth, i.e., each coherence bandwidth is split into multiple subcarriers which brings the correlation of channel gains among these subcarriers. In this paper, we focus on the diversity-multiplexing tradeoff in this kind of OFDMA systems. First, a correlated random bipartite graph is adopted to formulate this problem. To resolve the user conflicts in subcarrier allocation, the maximum proper /-matching method is introduced to minimize the user outage probability with fairness assurance at given multiplexing gains. Based on this model, the optimal diversity-multiplexing tradeoff curve is obtained. Two extreme points are considered: (1) the full diversity gain is the number of coherence bands, i.e., the same as that in point-to-point OFDM systems; and (2) given a coherence bandwidth, the maximum multiplexing gain is equal to the frequency band equally allocated to each user. The random vertices rotation and extension based Hopcroft-Karp algorithm is then proposed as an optimal subcarrier allocation scheme, which can achieve the optimal tradeoff curve with the time complexity of O(S2.5), where S is the total number of subcarriers.

OFDMA is a potential technology that can flexibly allocate subcarriers while providing diversity gain to multiple users. In our recent work, we showed a surprising result that the maximum frequency diversity gain in OFDMA systems is equal to the number of independent subcarriers, i.e., the same as that in point-to-point OFDM systems despite of the fact that multiple users will share a common set of subcarriers. However, the diversity gain only characterizes the first-order outage performance in the high SNR regime, and the outage performance in the low SNR regime, which is very important in practice, is still an open problem. In this paper, we first formulate the subcarrier allocation problem in OFDMA systems as a random bipartite graph model. Then, a more precise outage probability is derived in the high SNR regime using a high-order analysis of the maximum matching on a random bipartite graph. An approximate outage probability in the low SNR regime is also obtained by studying the complement of a random bipartite graph. It is then demonstrated that the coefficient of the second-order term in the outage probability expression is zero except for the scenario of two users with two or three subcarriers.

Single carrier interleaved frequency division multiple access (SC-IFDMA) has been recently receiving much attention for uplink multiuser access in the next generation mobile systems because of its lower peak-to-average transmit power ratio. In this paper, we investigate the effect of carrier frequency offsets (CFO) on SC-IFDMA and propose a new low complexity time domain CFO compensation (TD-CC) scheme. The TD-CC scheme can be combined with successive interference cancellation technique to further improve the system performance. Simulation results will show that the proposed TD-CC and TD-CC-SIC schemes greatly reduce the CFO effect on SCIFDMA. Furthermore, we propose a new blind CFO estimation scheme for SC-IFDMA systems, which is based on the ESPRIT algorithm. Compared to the conventional blind CFO estimation scheme using ESPRIT, it is shown that in some cases the proposed scheme does not need to increase the length of cyclic prefix, and thus improve the data throughput efficiency.

OFDMA technology can significant improve the transmission reliability in multi-user communication systems because of its inherent frequency diversity. In a recent work, we have derived a surprising result which demonstrates that OFDMA systems can achieve a frequency diversity gain which is equal to the total number of independent subcarriers. In this paper, we shall show that the frequency diversity and the frequency multiplexing can be simultaneously achieved in OFDMA systems with a fundamental tradeoff between them. The random bipartite graph theory is used to model and analyze this diversity-multiplexing tradeoff problem. In particular, the maximum proper f-matching is introduced as a subcarrier allocation method which can minimize the user outage probability with fairness assurance given some multiplexing requirements. Similar to the Zheng-Tse tradeoff in MIMO systems, the optimal diversity-multiplexing tradeoff in multi-user OFDMA systems and it will be shown that its curve can be characterized by a piecewise linear function, despite of the user conflicts in the subcarrier allocation.

In this paper, we consider a wireless cooperative multimedia decode-and-forward network wherein one relay may assist multiple source-destination pairs. By exploiting the global channel state information at the relay, we propose a power allocation, a time allocation and an iterative joint power-time allocation algorithm to minimize the sum expected distortion. Firstly, we separately optimize the relay's transmission power and the system transmission time allocated to each source-destination pair such that the sum expected distortion can be minimized. Then, we propose an iterative joint power-time allocation algorithm subject to the relay's total power constraint to further improve the distortion performance. The proposed iterative algorithm is guaranteed to converge to an optimal, despite not necessarily globally optimal, point and can achieve the minimum sum expected distortion among all the schemes.

In this paper, we consider two fundamental problems in log-normal fading networks. One is the energy efficiency. The other is the characterization of the internode interferences and spacial reuse. We first introduce a power efficiency factor as a new parameter to measure the transmit efficiency and obtain an optimal transmit power allocation of the concurrent transmitters in such fading environment. The internode interference caused by the simultaneous transmissions within a local area is then analyzed. The analysis will also be extended to the whole network within unlimited region where the interference accumulative impact is taken into account. By doing so, we set up a direct link between the interference double disc model with the statistical accumulative interference model and derive the optimal spatial reuse distance to guarantee the network efficiency. Finally, we propose a concurrent access strategy, OCMAC (opportunistic concurrent MAC), which can efficiently mitigate the internode interference through the node coordination before the simultaneous transmissions while keeping a relatively low transmit power and high spatial reuse efficiency.

Multiuser diversity is inherent in wireless networks due to independent channel variations of different users. It requires that the transmitter has the knowledge of the channel state information (CSI) of each user in the downlink. Too much feedback will bring a heavy load to the system in some cases. This paper investigates the problem of exploiting multiuser diversity in a one source multiple destinations wireless transmission system with limited feedback. A strategy for obtaining the optimal multiple rate level and the maximum achievable sum rate is proposed in this paper. Moreover, the scheduling outage probability, the probability that rates of all users are below a threshold, is also analyzed. For one-bit feedback, a tight bound of the achievable rate is obtained. It is shown that the achievable rate nearly capture the order of the double-logarithmical of the number of users in full CSI systems. Numerical results are also presented. The achievable sum rate is close to the full CSI capacity via limited feedback as the number of users is large enough.

In this paper, we propose a new open-loop rate/link adaptation algorithm (ARFHT) for the emerging high-speed IEEE 802.11n WLANs. ARFHT extends the legacy rate adaptation algorithms for SISO WLANs to make it applicable in the context of MIMO-based 802.11n WLANs. It adapts the MIMO mode in terms of spatial multiplexing and spatial diversity, the two fundamental characteristics of the 802.11n MIMO PHY. It also modifies the link estimation and probing behavior of legacy SISO algorithms. The combined adaptation to the appropriate MIMO mode as well as the appropriate modulation coding scheme selection achieves high channel utilization. In this paper, we provide the intuition and the design details of the ARFHT algorithm. A comprehensive simulation study using ns-2 will demonstrate that ARFHT achieves excellent throughput performance in most scenarios and is highly responsive to varying link conditions, with minimum overhead.

In this paper, we first investigate the network information flow over a relay network topology and find that the reliable achievable information rate is beyond the achievable upper bound using the conventional encoding/modulation techniques when the relay and the destination are with carrier sensing. We then propose a signal-time coding approach which combines the traditional encoding/modulation mode in the signal domain with the signal pulse phase modulation in time domain. Such a hybrid signal-time coding approach can be considered as an integrated codec/modem processing in the two dimensional combinatorial space: Signal domain and temporal domain. The main feature of the proposed signal-time coding is that one can separately design the codec/modem in the signal domain and in the time domain. Therefore, the well known efficient codec/modems, such as LDPC, Turbo coding, TCM etc. in the signal domain can be employed here. A tight lower bound is explicitly presented for the signal phase coding/modulation efficiency in the time domain. In addition, we also present an iterative method to construct the code book for the signal phase coding/modulation in the time domain. Finally, consider its applications in the additive white Gaussian noisy (AWGN) relay networks and obtain some interesting results.

Cognitive radio has the ability to listen to the wireless channel, detect the vacant spectrum bands, and make use of them. However, the sensing ability of this smart radio may not be perfect which may induce interference to the primary system. One of the main challenges in cognitive communication lies therefore in striking a balance between the conflicting goals of minimizing the interference to the primary system and maximizing the performance of the secondary one. In this paper, we formulate a cross-layer optimization problem to design the sensing time and optimize the transmit power in order to maximize the cognitive system throughput while keeping the interference to the primary user under a threshold constraint. We study the impact of sensing time and power adaptation on the performance of the cognitive radio system. Through numerical analysis, we find that optimizing the transmit power of the cognitive user and the sensing time play an important role in maximizing the system throughput.

Wireless network coding has attracted significant attention recently over relay channels. In this paper, we are interested in two-way wireless communication and explore the comparative advantages of two popular network coding strategies, binary network coding (NetC) and superposition coding (SupC), also named as analog network coding. In the literature, most works focused on NetC under a three-step framework and recently also SupC but under a two-step one. However, there is still little understanding on the truly optimal configuration. In addition, most works assumed channel knowledge at the transmitters while ignoring the presence of the direct link, which may not be valid in a fading environment. In this work, we focus on the outage performance instead and take into consideration also the time-varying nature of the direct link Outage regions of various schemes are theoretically characterized and their optimal operating conditions are identified, from which the best combined strategies are derived in terms of the maximum goodput and robustness to channel knowledge defects. Interesting new findings together with insightful discussions are provided.

In cognitive radio networks, knowledge of the position of the primary users is very important as it can be used to avoid harmful interference to the primary users, while at the same time be exploited to improve the spectrum utilization. In this paper, a semi range-based localization algorithm is proposed for the secondary users in cognitive radio networks to estimate the positions of the primary users. The basic idea of the proposed algorithm is to take advantage of the estimated detection probabilities, which can be obtained from the binary detection indictors of the secondary users, in order to estimate the distances between themselves and the primary users. The accuracy of the proposed localization algorithm is further improved by introducing an iterative least squares algorithm. The Cramer-Rao lower bound of the mean square error of the proposed localization estimator is also derived. Extensive simulations will then show that the actual mean square error achieved by the proposed localization algorithm is reasonably close to the lower bound, which demonstrates that the proposed method is near optimal.

In cognitive radio systems, cooperative spectrum sensing in the physical layer is highly desired to detect the primary user accurately and to guarantee the quality of service (QoS) of the primary user. Due to the energy consumption in sensing the channels, the selfish users may not be willing to contribute to the cooperative sensing while they want to occupy more idle channels observed. To deal with this problem, we propose in this paper a game theoretic approach which will advocate users to spend power to sense the channel by using the access opportunity in the MAC layer as a payoff. In this protocol, the users who sense the channel are given higher priority to access the idle channel and meanwhile, the multiuser diversity in the MAC layer is exploited to increase the throughput for cognitive systems. The expressions for the average throughput and consumed power for a single user will be derived and then Nash equilibrium will be studied for the game model. It will be shown that the game will be characterized by the prisoner's dilemma. To guarantee the fairness and achieve higher throughput, we will design an evolutionary game protocol in which the Nash equilibrium can be dynamically changed based on the behaviors of cognitive users. Simulation results will show that the proposed protocol can achieve fairness and efficiency for cognitive users.

Fast handover management in mobile IPv6 environments has been a research subject for a long time. Exploiting the cooperative diversity paradigm in partner-based hierarchical MIPv6 (PHMIPv6) promises an acceleration of the handoff management operation by relaying some signaling over a selected partner node prior to the actual handover to the new access point. For this purpose, a suitable partner node, that stays in communication range for sufficient time until the signaling in the pre-handoff phase is finalized, should be selected. PHMIPv6 proposes to select the node with the highest signal strength as the partner node. In this paper, we show that using the link expiration time (LET) metric to select the partner node can significantly improve handovers in mobile IP (MIP) networks. The basis of this new metric is the relative position and the relative speed of the mobile node to the potential partner nodes. A set of simulations is conducted to evaluate the performance of the proposed scheme and encouraging results are obtained.

We first consider a topology consisting of one source, two destinations and one relay. For such a topology, it is shown that a network coding based cooperative (NCBC) multicast scheme can achieve a diversity order of two. In this paper, we discuss and analyze NCBC in a systematic way as well as compare its performance with two other multicast protocols. The throughput, delay and queue length for each protocol are evaluated. In addition, we present an optimal scheme to maximize throughput subject to delay and queue length constraints. Numerical results will demonstrate that network coding can bring significant gains in terms of throughput.

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space-time-frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.

This paper addresses Rayleigh fading networks, and in particular, wireless ad-hoc and sensor networks over Rayleigh fading channels. First, we will model Rayleigh fading networks and show how to map the wireless fading channel to the upper layer parameters for cross-layer design. Based on the developed fading network model, we will consider two scarce resources of wireless networks, namely energy and medium, and develop a cross-layer way to improve their efficiency. In particular, we will first study the energy-efficiency and introduce a new parameter, energy cost factor, as the counterpart of transport capacity in wireless transmission. The new parameter will be used to design energy-efficient networks. As to the medium resource, we will bring forward the medium resource space, which not only organizes various medium resources in a systematic way but also considers a third dimension related to space reuse and internode interference. Finally, we will give a general discussion on the cross-layer design and show how power control and route selection jointly contribute to improving the resource efficiency. A few particular routing algorithms will also be studied in detail.

In this paper, we propose a resource allocation scheme to increase the effective capacity subject to the queue-overflow statistical Quality-of-Service (QoS) requirement for a multi-relay cooperative wireless network. Firstly, we consider the block fading channels and derive an algorithm in which each relay is allocated a time slot of optimal length during the cooperation phase, based on the channel statistics. Our analysis indicates that when the QoS requirement is loose, only the relay with the best average channel condition should be selected for cooperation. On the other hand, when the QoS requirement becomes more stringent, more relays should participate in cooperation. The asymptotic case when either the transmit power or the number of relays goes to infinity is discussed, and we shall reveal a tradeoff between the transmit power and the number of relays, given a target effective capacity. By modeling the channel correlation by a two-state Markov model, we will develop two sub-optimal time-slot allocation algorithms which can substantially increase the effective capacity compared with the opportunistic and equal allocation schemes. Our results will show that the channel correlation can sharply decrease the effective capacity and that applying the optimal time-slot allocation result obtained in block fading channels directly to correlated fading channels is no longer optimal.

Due to the broadcasting nature of wireless transmission, concurrently active links can cause mutual interference to each other. This greatly limits the throughput, as well as, results in poor communication reliability especially for wireless systems with multiple links or hops. To overcome this limitation, many interference cancellation techniques, which have mainly focused on the interference among single-hop links, have been designed. In this paper and in contrast to most previous work, we present an efficient method, which we refer to as network interference cancellation or NICE, for effectively mitigating the interference from multi-hop transmissions. This method will make use of the prior knowledge about the interference, which an interfered node can obtain by receiving and processing the signals from the source node of a multi-hop transmission. Two NICE protocols, namely, decode-and-cancel, and amplify-and-cancel are proposed and analyzed. The two proposed protocols will be considered in relay-assisted wireless access networks as well as wireless ad hoc networks without fixed infrastructure to demonstrate the potential of NICE. It will be shown that by using NICE, more links are able to transmit simultaneously in the same frequency band, thereby, highly improving the spatial reuse of spectrum along with the throughput. Numerical results will also show that both of the two NICE protocols can achieve more than 30% throughput gain over conventional interference free scheduling methods.

In ad-hoc networks, cooperative diversity is especially beneficial where the use of multiple antennas may be impractical. There has been a lot of work on improving the peer-to-peer link quality by using advanced coding or power and rate allocation between a single source node and its relays. However, how to fairly and efficiently allocate resources among multiple users and their relays is still unknown. In this paper, a multiuser cooperative protocol is proposed, where a power reward is adopted by each node to evaluate the power contributed to and by others. It will be shown that the proposed fair cooperative protocol (FAP) can significantly improve the fairness performance compared to full cooperation. It is further demonstrated that in energy-constrained cooperative ad-hoc networks, fairness can actually bring significant throughput gains. The tradeoff between fairness and throughput is analyzed and two price-aware protocols, FAP-R and FAP-S, will be further proposed to improve fairness. Simulation results will validate our analysis and show that compared to the direct transmission (i.e., without cooperation) and the full cooperation, our proposed FAP, FAP-R and FAP-S can achieve much better fairness performance along with substantial throughput gains.

In this paper, we describe an efficient and energy conservative unicast routing technique for multi-hop wireless sensor networks over Rayleigh fading channels, which we shall refer to as assistant opportunistic routing (AsOR) protocol. In contrast to previous works, this protocol is presented from a systematic energy conservation perspective. During the source-destination transmission, the AsOR protocol forwards the data stream through a sequence of nodes, which are classified as three different node sets, namely, the frame node, the assistant node and the unselected node. The frame nodes are indispensable to decode-and-forward while the assistant nodes provide protections for unsuccessful opportunistic transmissions. Based on the AsOR protocol, each predetermined route can be divided into several disjoint segments, and we establish a mathematical model to characterize the energy consumptions for each node in one segment. Furthermore, we provide a method for selecting the optimal value N*, the number of nodes in one transmission segment, which can lead to the minimum average energy consumption. Numerical results will confirm that the proposed protocol is energy conservative compared with other two traditional routing protocols both in slow and fast Raleigh fading channels and that the method for searching the optimal value N* is efficient. Finally, robustness analysis for the theoretical results are presented.

Cognitive radio has been recently proposed as a promising technology to improve the spectrum utilization. In this paper, we consider the spectrum sharing between a large number of cognitive radio users and a licensed user in order to enhance the spectrum efficiency. With the deployment of M antennas at the cognitive base station, an opportunistic spectrum sharing approach is proposed to maximize the downlink throughput of the cognitive radio system and limit the interference to the primary user. In the proposed approach, cognitive users whose channels are nearly orthogonal to the primary user channel are pre-selected so as to minimize the interference to the primary user. Then, M best cognitive users, whose channels are mutually near orthogonal to each other, are scheduled from the preselected cognitive users. A lower bound of the proposed cognitive system capacity is derived. It is then shown that opportunistic spectrum sharing approach can be extended to the multipleinput/ multiple-output (MIMO) case, where a receive antenna selection is utilized in order to further reduce the computational and feedback complexity. Simulation results show that our proposed approach is able to achieve a high sum-rate throughput, with affordable complexity, when considering either single or multiple antennas at the cognitive mobile terminals.

This paper considers the multiuser power control problem in Gaussian frequency-flat interference relay channels using a game-theoretic framework. While a lot of attention has been paid to Gaussian interference games, where sufficient conditions for the uniqueness of the Nash equilibrium (NE) have been established, these types of games have not been studied in the context of interference relay channels. We consider here Gaussian interference relay games (GIRGs), where instead of allocating the power budget across a set of sub-channels, each player aims to decide the optimal power control strategy across a set of hops. We show that the GIRG always possesses a unique NE for a two-player version of the game, irrespective of any channel realization or initial system parameters such as power budgets and noise power. Furthermore, we derive explicitly a sufficient condition under which the NE achieves Pareto-optimality. To facilitate decentralized implementation, we propose a distributed and asynchronous algorithm. We also prove that the proposed algorithm always converges to the unique NE from an arbitrary starting point. We then conclude that the distributed game-theoretic approach exhibits great potential in the context of interference relay channels and qualifies as a practically appealing candidate for power control.

We consider cooperative spectrum sensing in which multiple cognitive radios collaboratively detect the spectrum holes through energy detection and investigate the optimality of cooperative spectrum sensing with an aim to optimize the detection performance in an efficient and implementable way. We derive the optimal voting rule for any detector applied to cooperative spectrum sensing. We also optimize the detection threshold when energy detection is employed. Finally, we propose a fast spectrum sensing algorithm for a large network which requires fewer than the total number of cognitive radios in cooperative spectrum sensing while satisfying a given error bound.

Abstract Network topology construction and its channel assignment for each node in the constructed network topology are two main problems in the initialization of topology building. Topology control is an effective way to solve the problem of topology building. To investigate the joint effect of topology control and channel assignment, we propose a joint processing scheme composed of a k‐Neighbor topology control algorithm and a greedy channel assignment (GCA) algorithm in this paper. Based on this joint processing scheme, the relationships between the energy consumption, the total required channel number and the network connectivity are discussed. We also discuss the impact of some parameters on the performance of networks in terms of the path loss factor, node density, maximum node degree, etc. Our main contributions in this paper is that we find that topology control has a good effect on improving the performance of channel assignment, and the proposed joint processing scheme can reduce the required channel number effectively, compared with its theoretical upper bound. In particular, if the node degree in a network is not more than k, various simulations indicate that the required channel number is not more than 2k + 1. Copyright © 2008 John Wiley & Sons, Ltd.

OFDM can provide frequency diversity gain for point-to-point communications over frequency-selective slow fading channel. Recent works show that OFDM may also form a flexible and efficient multiple access method, which is often referred to OFDMA. However, the user outage probability and the optimal frequency diversity gain in OFDMA systems are not known. In this paper, random bipartite graph is used to model and analyse the multi-user subcarrier allocation problem over frequency-selective slow fading channels. Our aim is to minimize the user outage probability as well as guarantee fairness by dynamic allocating various subcarrier to each user. An optimal subcarrier allocation algorithm, which we shall refer to as the Hungarian method with random vertices rotation, is introduced to achieve these objectives. A simple but effective approximation equation for user outage probability is then derived. It is shown that the optimal frequency diversity gain in OFDMA system is the same as the point-to-point OFDM system. In particular, the frequency diversity gain does not decay as the number of users increases.

In this paper, we propose a limited feedback scheme to minimize the outage probability for a wireless cooperative decode-and-forward network. Specifically, based on the instantaneous conditions of the source-destination and relay-destination channels, the destination will allocate the transmission time of the source and relay and feed back the allocation results to the source. Both full, or infinite-rate, feedback and limited feedback are considered. To simplify the expression of the outage probability in the full feedback case, a lower bound is proposed, based on which we find the sub-optimal location of the relay that can result in a close-to-optimal outage performance. Our results show that, even with only one-bit feedback, a significant improvement in terms of the outage probability can be achieved compared to the no feedback case. Furthermore, the outage performance can approach optimality by exploiting only two or three bits feedback from the destination.

We address the power allocation problem for interference relay channels. Due to the competitive nature of the multi-user environment, we model the problem as a strategic non- cooperative game and show that this game always has a unique Nash equilibrium (NE), for any system profile. Two iterative algorithms, based on sequential and simultaneous updating, are proposed to achieve the unique NE in a distributed manner. We shall also prove that both algorithms always converge to the unique NE, from an arbitrary starting point. More importantly, the global optimality in terms of the sum information rate is achieved by the NE, when the interference is relatively low.

Power allocation is an important issue for spectrum sharing of unlicensed bands, in which multiple unlicensed systems may coexist and operate. Recently some works have been reported on game theoretical analysis for multiple systems cooperating in frequency-flat unlicensed bands. However, there has not been much work on the cooperative and competitive strategic behavior of multiple mutually interfering systems in frequency-selective unlicensed bands. In this paper, we construct a game theoretical framework for multiple selfish systems on frequency-selective Interference Channels (IC). This framework enables us to utilize existing protocols designed for frequency-flat ICs in frequency-selective scenarios and can be regarded as an extension of previous results for frequency-flat scenarios.

In Mobile Ad hoc NETworks (MANETs), random access and dynamic routing are two critical techniques for mobile nodes to convey information without centralized scheduling. Conventionally, random access and dynamic routing are implemented at the Medium Access Control (MAC) and the network layer, respectively. However, the current MAC protocol Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) cannot support dynamic routing efficiently. To overcome this limitation, we shall propose a cross-layer protocol which takes dynamic routing into consideration when the mobile nodes contend to access the channel. In the proposed distributed protocol, the routing packets of the network layer are transmitted within the contention period. Since the transmission of routing packets is separated from data transmission in the time-domain, our cross- layer protocol eliminates the collision caused by the transmission of short routing packets. Simulation results will show that our design could significantly improve the system performance at both the MAC and network layers.

In wireless relay networks, the use of multiple antennas at both the source and destination can increase the capacity linearly with the number of antennas. In this paper, we investigate amplify-and-forward dual-hop MIMO relay networks without channel state information at the relay nodes, i.e., noncoherent case. In contrast to the conventional approach where the available power is equally distributed over all relay nodes, we propose an opportunistic relaying approach in which only a few very important relays (VIR) are selected to share the power. By doing so, the array gain of MIMO relay networks can be enhanced and thus maximize the ergodic capacity. Simulation results show that the optimal number of selected VIR to maximize the ergodic capacity is identical to the number of antennas employed at the source or destination. Compared to the conventional approach, the proposed opportunistic relaying approach can achieve a higher capacity.

In future wireless communication area, a key issue is the resource allocation and scheduling over wireless channel. Various aspects of this issue have been studied. However, few studies are on the QoS guaranteed uplink multiple traffic scheduling in multi-user wireless network. The scheduling problem is addressed in this paper. We consider the TDM-OFDMA uplink multi-access queuing system with four types of traffic. Each type has specific QoS requirements, such as minimum rate, maximum latency and maximum jitter. This QoS guaranteed cross-layer scheduling issue is modeled as a convex optimization problem. We also prove our scheduling method can guarantee the minimum rate, maximum latency and maximum jitter asymptotically, meanwhile it also minimizes the residual integrated workload. According to the solvability of this optimization problem, we define the scheduling algorithm stability region, and design a heuristic algorithm for connection admission control. The numerical results show the substantial performance of the proposed algorithm.