The growing demand for intelligent, adaptive resource management in next-generation wireless networks has underscored the importance of accurate and scalable wireless traffic prediction. While recent advancements in deep learning and foundation models such as large language models (LLMs) have demonstrated promising forecasting capabilities, they largely overlook the spatial dependencies inherent in city-scale traffic dynamics. In this paper, we propose TIDES (Traffic Intelligence with DeepSeek-Enhanced Spatial-temporal prediction), a novel LLM-based framework that captures spatial-temporal correlations for urban wireless traffic prediction. TIDES first identifies heterogeneous traffic patterns across regions through a clustering mechanism and trains personalized models for each region to balance generalization and specialization. To bridge the domain gap between numerical traffic data and language-based models, we introduce a prompt engineering scheme that embeds statistical traffic features as structured inputs. Furthermore, we design a DeepSeek module that enables spatial alignment via cross-domain attention, allowing the LLM to leverage information from spatially related regions. By fine-tuning only lightweight components while freezing core LLM layers, TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead. Extensive experiments on real-world cellular traffic datasets demonstrate that TIDES significantly outperforms state-of-the-art baselines in both prediction accuracy and robustness. Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.

This work investigates a Field of View (FoV) prediction-based Adaptive Bitrate Streaming (ABS) strategy aimed at enhancing the Quality of Experience (QoE) for users watching 360-degree videos at wireless mobile devices. In doing so, there are two challenges: 1) how to achieve accurate FoV prediction, and 2) how to provide a flexible bitrate adaption service given the limited storage space of the video server. To address these issues, we propose a novel adaptive streaming strategy for 360-degree videos that integrates a Transformer-based FoV prediction model with on-demand video transcoding under dynamic wireless network conditions. To strike a balance between user QoE and transcoding overhead, we formulate a QoE-driven system utility maximization problem that jointly optimizes computing resource allocation and bitrate adaptation. Given the dynamic and multi-slot nature of the problem, it is inherently complex. To overcome this, we transform the original problem into a Markov decision process (MDP) and solve it using the residual learning and deep deterministic policy gradient (DDPG) method. Simulation results demonstrate that the proposed methods outperform the state-of-the-art baselines in terms of FoV prediction accuracy and utility improvement.

Due to the reliance of task processing on specific service models, service caching has emerged as a promising technology for enhancing low-latency performance in vehicular edge computing (VEC) networks. When vehicular long-term service caching mismatches real-time task requirements, the mobility and autonomy of service vehicles enable them to fetch service models from providers actively. However, highly dynamic topology and resource constraints of VEC networks call for a more adaptable approach for jointly designing vehicular service fetching and task offloading. To this end, this paper aims at jointly optimizing active service fetching, task offloading, and communication and computing resource allocation to boost low-latency performance in two-tier caching-assisted VEC networks. In doing so, a total task completion delay minimization problem is formulated, where the social-mobility-aware network topology, vehicle classification, delay tolerance requirements, and resource constraints are taken into account. Since it is a mixed-integer non-linear programming (MINLP) problem, it is typically NP-hard. To solve it effectively, we decompose it into three subproblems, which can be efficiently solved by an iterative algorithm based on block coordinate descent (BCD) with low complexity. Extensive simulation results demonstrate that our proposed algorithm achieves fast convergence and reduces the task completion delay by 9.27%-59.77% compared to five representative baselines.

This paper presents an innovative framework that boosts computing power by utilizing ubiquitous computing power distribution and enabling higher computing node accessibility via adaptive UAV positioning, establishing a UAV-enabled Computing Power Network (UAV-CPN). In a UAV-CPN, a UAV functions as a dynamic relay, outsourcing computing tasks from the request zone to an expanded service zone with diverse computing nodes, including vehicle onboard units, edge servers, and dedicated powerful nodes. This approach has the potential to alleviate communication bottlenecks and overcome the "island effect" observed in multi-access edge computing. A significant challenge is to quantify computing power performance under complex dynamics of communication and computing. To address this challenge, we introduce task completion probability to capture the capability of UAV-CPNs for task computing. We further enhance UAV-CPN performance under a hybrid energy architecture by jointly optimizing UAV altitude and transmit power, where fuel cells and batteries collectively power both UAV propulsion and communication systems. Extensive evaluations show significant performance gains, highlighting the importance of balancing communication and computing capabilities, especially under dual-energy constraints. These findings underscore the potential of UAV-CPNs to significantly boost computing power.

Reconfigurable intelligent surface-assisted integrated sensing and communication (RIS-ISAC) presents a promising system architecture to leverage the wide bandwidth available at millimeter-wave (mmWave) frequencies, while mitigating severe signal propagation losses and reducing infrastructure costs. To enhance ISAC functionalities in the future air-ground integrated network applications, RIS deployment must be carefully designed and evaluated, which forms the core motivation of this paper. To ensure practical relevance, a multi-RIS-ISAC system is established, with its signal model at mmWave frequencies demonstrated using ray-launching that calibrated to real-world environments. On this basis, an energy-efficiencydriven optimization problem is formulated to minimize the multi-RIS size-to-coverage sum ratio, comprehensively considering real-world RIS deployment constraints, positions, orientations, as well as ISAC beamforming strategies at both the base station and the RISs. To solve the resulting non-convex mixed-integer problem, a reformulation based on equivalent gain scaling method is introduced. A two-step iterative algorithm is then proposed, in which the deployment varaibles are determined under fixed RIS positions in the first step, and the RIS position set is updated in the second step to progressively approach the optimum solution. Simulation results based on realistic parameter benchmarks present that the optimized RISs deployment significantly enhances communication coverage and sensing accuracy with the minimum RIS sizes, outperforming existing approaches.

Collaborative machine learning has been a key technology to enhance the development of intelligent transportation systems with the advantage of privacy protection and communication efficiency. However, model training on vehicles requires a large amount of computation resource especially that the machine learning (ML) models are massive nowadays. In this paper, we shed light on the situation that a part of the vehicles may have low computational capability and not able to participate in the training process. To address this problem, we propose a Vehicular Edge Hybrid Federated Split Learning (VEHFSL) paradigm considering the data privacy protection and resource constraints in Internet of Vehicles (IoV). Firstly, we select the vehicles to participate in the training process by sorting the weighted sum of the expected sojourn time and training data volume of each vehicle. Then, we formulate a joint optimization problem to minimize the training latency of one round by jointly optimizing the training mode selection, computation frequency and transmit power allocation. Given that the formulated optimization problem is mixed-integer nonlinear programming (MINLP), which is typically NP-hard, we design a joint mode selection and resource allocation algorithm (JMSRA) by performing block coordinate descent (BCD) technique to decompose it into three sub-problems and iteratively solving them to achieve a near-optimal solution efficiently with the balance of the computational complexity and optimality of the solution. Finally, extensive simulations are performed to validate the superiority of our proposed VEHFSL paradigm in resource-constrained vehicular networks.

Orbital angular momentum (OAM) can enhance the spectral efficiency by multiplying a set of orthogonal modes on the same frequency channel. To maintain the orthogonal among different OAM modes, perfect alignments between transmitters and receivers are strictly required. However, in multi-user OAM communications, the perfect alignments between the transmitter and all the receivers are impossible. The phase turbulence, caused by misaligned transmitters and receivers, leads to serious inter-mode interference, which greatly degrades the capacity of OAM transmissions. To eliminate the negative effects of phase turbulence and further enhance the transmission capacity, we introduce RIS into the system, and propose a joint precoder and reflector design for reconfigurable intelligent surface (RIS)-assisted multi-user OAM communication systems. Specifically, we propose a three-layer design at the transmitter side, which includes inter-user OAM mode interference cancellation, intermode self-interference elimination and the power allocation among different users. By analyzing the characteristics of the overall channels, we are able to give the specific expressions of the precoder designs, which significantly reduce the optimization complexity. We further leverage RIS to guarantee the line-of-sight (LoS) transmissions between the transmitter and users for better sum rate performance. To verify the superiority of the proposed multi-user OAM transmission system, we compare it with traditional MIMO transmission schemes, numerical results have shown that our proposed design can achieve better sum rate performance due to the well-designed orthogonality among different users and OAM modes.

This paper focuses on the energy-efficient unmanned aerial vehicles (UAVs) assisted vehicular networks, where multiple rotary-wing UAVs are deployed to provide uplink service to numerous ground vehicles. The objective is to maximize the long-term system energy efficiency (EE) over the entire service period, through jointly optimizing the vehicle-UAV association, the sub-carrier assignment, the power control of vehicles and the trajectory design of UAVs. The formulated problem involves the limitations on the transmit power of vehicles and the propulsion power of UAVs, the quality of service (QoS) requirements of vehicles, the UAV movement constraints, and co-channel interference among vehicles. This makes the problem a mixed integer non-convex fractional programming problem accompanied by a mass of variables and diverse constraints, which is difficult to be solved within the polynomial time through traditional optimization methods. To cope with the timely decision-making requirement and dynamic moving scenario, we opt to the deep reinforcement learning (DRL) approach. To do so, the formulated problem is first transformed to a Markov decision process (MDP). Then, an improved k-means proximal policy optimization (IKPP) algorithm is proposed to solve the MDP problem. The proposed algorithm involves action reconstruction, the improved k-means algorithm, and proximal policy optimization-clip algorithm, which can help obtain the solutions with low complexity. Simulation results demonstrate the convergence, scalability and real-time of the proposed algorithm, along with its performance advantage over other benchmark algorithms.

This paper investigates the Integrated Sensing and Communication (ISAC) empowered predictive beamforming design for Uncrewed Aerial Vehicle (UAV) assisted networks, where the ground Base Station (BS) explores the echoes of communication signal for real-time UAV tracking. To ensure practicality and generalizability, we establish a random UAV mobility model incorporating position fluctuations and attitude variations during flight, which poses challenges to accurate UAV tracking. To address this, we propose Historical Echoes-based Convolutional Time Attention Network (HECTA-Net), a novel deep learning framework for end-to-end beamforming prediction. The proposed HECTA-Net integrates Convolutional Neural Network (CNN) and Temporal Convolutional Network (TCN) to jointly extract the spatio-temporal features from historical ISAC echoes across multiple time slots, where the attention mechanism is also embedded to dynamically identify and weight the critical time slots. Thus it enables accurate prediction on the transmit and receive beamforming matrices. Extensive simulations are conducted to validate the robustness and performance of proposed scheme. Results demonstrate that the proposed HECTA-Net outperforms the other state-of-art baselines, with its performance closely approaching the theoretical upper bound even in the high randomized UAV motion patterns.

Semantic communication emerges as a pivotal technology for realizing the 6G vision. However, existing semantic-aware reconstruction systems rely on syntactic-level loss functions for optimization, failing to focus on the precise recovery of semantic information and resulting in suboptimal semantic fidelity. Meanwhile, the autoregressive decoding architecture adopted in text semantic communication introduces prohibitive high latency. To address these two issues, we propose a robust information bottleneck (RIB) guided non-autoregressive semantic communication (NASC) scheme, named RIB-NASC. First, we pioneer the integration of the RIB criterion into semanticaware reconstruction systems, formulating a direct optimization objective tailored for underlying semantic recovery and establishing an informativeness-robustness trade-off. Second, we derive a tractable variational lower bound for the RIB objective via variational approximation and a novel synonymous mapping-based semantic posterior estimation strategy. Third, we design a lightweight non-autoregressive semantic decoder architecture based on Transformer encoder, enabling high-speed parallel semantic decoding during inference. Extensive simulation results demonstrate that the RIB-NASC scheme significantly outperforms baseline schemes in terms of semantic recovery performance (BLEU score and sentence similarity) and achieves a decoding delay reduction of nearly 96.7% compared to traditional autoregressive decoding.

Asynchronous federated edge learning (Asy-FEEL) has drawn intensive attention due to its ability to effectively address the straggler issue caused by the heterogeneity of the participated mobile devices (MDs). The quality of Asy-FEEL depends highly on the number of participating MDs. Since the local training of federated learning consumes computation resources of MDs, it inevitably reduces the resources that can be devoted to their own tasks (OTs). Therefore, there is always a decreased incentive of MDs to participate in Asy-FEEL, subsequently reducing the amount of Asy-FEEL tasks executed by MDs, thereby failing in achieving satisfied Asy-FEEL performance. How to effectively utilize the limited resources and schedule Asy-FEEL tasks and OTs to satisfy the quality of service requirements on the OTs and at the same time encourage MDs to participate to execute Asy-FEEL tasks is of vital importance. To this end, a joint tasks scheduling and resource allocation problem is formulated and investigated within a dynamic multitasks mobile edge computing (MEC) network, where Asy-FEEL tasks and OTs coexist. Since the problem is a dynamic stochastic optimization problem, a Lyapunov-based dynamic joint tasks scheduling and resources allocation (Lya-DJTR) algorithm is proposed to determine tasks scheduling, computational resource and bandwidth allocation, and transmit power control at MDs simultaneously. Simulation results demonstrate the superiority of the proposed algorithm in improving the efficiency of Asy-FEEL while ensuring the real-time processing of OTs when compared to baseline algorithms.

This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial relays, outsourcing tasks generated in the request zone to an expanded service zone, consisting of a diverse range of computing devices, from vehicles with onboard computational capabilities and edge servers to dedicated computing nodes. This approach has the potential to alleviate communication bottlenecks in traditional computing power networks and overcome the "island effect" observed in multi-access edge computing. However, how to quantify the network performance under the complex spatio-temporal dynamics of both communication and computing power is a significant challenge, which introduces intricacies beyond those found in conventional networks. To address this, in this paper, we introduce task completion probability as the primary performance metric for evaluating the ability of UAV-CPNs to complete ground users’ tasks within specified end-to-end latency requirements. Utilizing theories from stochastic processes and stochastic geometry, we derive analytical expressions that facilitate the assessment of this metric. Our numerical results emphasize that striking a delicate balance between communication and computational capabilities is essential for enhancing the performance of UAV-CPNs. Moreover, our findings show significant performance gains from the widespread distribution of computing nodes.

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffer catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware nonorthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a nondeterministic polynomial-time (NP)-hard problem and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment, and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i.e., 1) joint vehicle pairing and spectrum assignment subproblem and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-Munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a 3-D geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum rate and outperforms the baseline algorithms around 24%–53% within a specific range.

Caching 360° videos at the network edge can reduce user content request latency and mitigate transmission congestion in backbone networks. Given the fact that user only views a part of content of 360° scope at any time, caching the entire video is resource inefficient. To address this, we focus a multiaccess edge computing (MEC)-based 360° video service system, where the edge server only caches a portion of each video, that is, most likely falling in the Field of View (FoV) of users. To minimize the average video request latency of all users in the system, we formulate a large-scale$\{0-1\}$knapsack problem, which is NP-hard. To tackle it, we proposed a heuristic algorithm where the user viewing patterns extracted from the historical request information are taken into account. Specifically, we first design a cascading cache space allocation method to assign the total cache space of edge server to each segment of videos. After that, the original problem is decomposed into several small-scale yet individual tile caching subproblems with compressed solution space. Then, they are solved by using the dynamic programming algorithm with moderate complexity. To further enhance the caching performance, the PSO-based algorithm is designed to fine tune the parameters involved in the proposed caching algorithm. In addition, we introduce a content-based method to calculate the request probability of the newly generated videos. The effectiveness of the proposed algorithm is evaluated through simulations based on a real world dataset, where the results demonstrate a substantial improvement in both video request latency and cache hit rate compared to the benchmark methods.

The number of categories of instances in the real world is normally huge, and each instance may contain multiple labels. To distinguish these massive labels utilizing machine learning, eXtreme Label Classification (XLC) has been established. However, as the number of categories increases, the number of parameters and nonlinear operations in the classifier also rises. This results in a Classifier Computational Overload Problem (CCOP). To address this, we propose a Multi-Head Encoding (MHE) mechanism, which replaces the vanilla classifier with a multi-head classifier. During the training process, MHE decomposes extreme labels into the product of multiple short local labels, with each head trained on these local labels. During testing, the predicted labels can be directly calculated from the local predictions of each head. This reduces the computational load geometrically. Then, according to the characteristics of different XLC tasks, e.g., single-label, multi-label, and model pretraining tasks, three MHE-based implementations, i.e., Multi-Head Product, Multi-Head Cascade, and Multi-Head Sampling, are proposed to more effectively cope with CCOP. Moreover, we theoretically demonstrate that MHE can achieve performance approximately equivalent to that of the vanilla classifier by generalizing the low-rank approximation problem from Frobenius-norm to Cross-Entropy. Experimental results show that the proposed methods achieve state-of-the-art performance while significantly streamlining the training and inference processes of XLC tasks. The source code has been made public at https://github.com/Anoise/MHE.

The future machine-type communication in internet-of-things (IoT) systems involves a massive number of devices sporadically communicating with a base station (BS) equipped with multiple antennas. Detecting active devices and estimating their associated channels are crucial but challenging due to the large number of potential devices and the small fraction of active devices. Existing studies assume high-resolution analog-to-digital converters (ADCs) at the BS, while there is a growing interest in implementing low-resolution ADCs, particularly one-bit ADCs, in massive multiple-input multiple-output (MIMO) systems. This paper focuses on the joint one-bit active device detection and channel estimation problem. We consider the maximum-likelihood approach and propose a novel expectation maximization (EM) algorithm with acceleration. On the theoretical aspect, we provide the convergent computational complexity analysis for the accelerated EM algorithm. The proposed method, evaluated through numerical simulations, outperforms benchmark algorithms in terms of both estimation accuracy and computational complexity.

In this article, we focus on the problem of product defect detection (PDD) in air conditioner (AC) manufacturing. The challenges are twofold: first, the scale of the objects undergoes significant variations, thereby increasing the difficulty of detection; second, the computing power of terminals is limited, and it may be difficult to meet the quasi-real-time detection requirements. Therefore, to improve detection accuracy and speed, a lightweight object detection model tailored for deployment on the compact wireless Raspberry Pi, is proposed: a deep cascaded U-shape network is presented to effectively capture both global context and local details of objects, which can reduce the feature redundancy and the number of the model parameters. An adaptive multiscale squeeze-and-excitation is designed for feature reuse and fusion, enhancing both detection accuracy and efficiency. Then, to meet the quasi-real-time detection demands and better utilize end-edge-cloud resources in industrial internet of things (IIoTs), an actor–critic-based dynamic offloading (ACDO) algorithm is proposed to minimize the long-term cumulative time of task detection. ACDO utilizes the elapsed time as the reward to directly optimize the mixed variables of the task to achieve efficient offloading. The proposed methods are verified at an AC manufacturing line, which demonstrates accurate and quasi-real-time defect detection, achieving a 64% reduction in runtime and a 1.2% improvement in average mean average precision. In addition, we publish two PDD datasets to accelerate the related research.

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets’ radar cross section (RCS), the blockage-resilient multi-BS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages.

Integrated sensing and communication (ISAC), which integrates sensing capabilities into wireless communication networks, is emerging as a key technology for future millimeter wave (mmWave) communication networks. Given the limited ISAC capability and energy budget of a single base station (BS), this paper studies how to enable energy-efficient sensing and communication via multi-BS collaborative sensing, where each sensing task is served by its most energy-efficient BS as much as possible, with the help of other BSs. Since unregulated multi-BS collaboration may lead to energy wastage and further aggravates the energy consumption in mmWave networks, an energy-efficient collaborative ISAC scheme is proposed, where multi-BS collaborative sensing and dual-functional radar and communication (DFRC) beams are judiciously utilized to reduce the network’s energy consumption. We formulate the design of the energy-efficient collaborative ISAC scheme as a mixed integer nonlinear programming problem by jointly considering task allocation, beam scheduling, and transmit power control. Then, an energy-efficient cooperative beam scheduling (EE-CBS) algorithm is developed for efficient solution finding. Through extensive simulations, the proposed scheme is shown to significantly reduce the network’s energy consumption when compared to the scheme without multi-BS cooperation or the utilization of DFRC waveforms.

This paper investigates a joint resource allocation (RA) algorithm to maximize the average sum rate (ASR) of the sparse code multiple access (SCMA) random model-based networks. The ASR optimization problem turns out to be non-convex mixed-integer nonlinear programming (MINLP) problem which is challenging to address. The previous works strongly depend on the upper/lower bound of ASR, successive convex approximation (SCA) methods, initial assignment definition, post-processing procedures, and generating/training the data sets, thus cannot be applied. Given the above challenges, considering the ability of the whale optimization algorithm (WOA) in solving the non-convex MINLP NP-hard problems, we first construct the resource element assignment (REA) matrix utilizing the binary WOA (BWOA) through the reshaping the pattern of the arrangement of non-zero elements (PANs) and converting the structural constraints. Then we enhance the continuous WOA (CWOA) to mine the power allocation (PA) coefficients by using an acceleration factor that improves the exploration and exploitation phases. We also develop a Kuhn-Munkres (KM)-based algorithm as an REA benchmark which needs no for post-processing requirements. Finally, our proposed algorithms are compared with the state-of-the-art works in the literature. Results show that SCMA CWOA-BWOA algorithm (CBWOA) provides more robust optimality in massive connectivity situations with a larger number of users. Besides, the CBWOA method guarantees the structural constraints by increasing the number of search agents (NSAs) through the exploitation phase.

This work explores the utilization of rotary-wing unmanned aerial vehicles (UAV) as aerial base station to provide downlink data services to ground users. The objective is to maximize the energy efficiency (EE) of the UAV assisted communication system while guaranteeing fairness among users. In pursuit of this goal, taking the propulsion power consumption of UAV, the flight constraints of UAV, and the limited communication resources into account, we jointly optimize the power allocation, bandwidth allocation, and trajectory design. Since the corresponding formulated problem is non-convex, to solve it efficiently, we first addressed its non-smoothness and then decompose the problem into two sub-problems: the joint power and bandwidth assignment and the trajectory design sub-problems. We prove that the power allocation and bandwidth assignment sub-problem is quasi-convex fractional optimization. A low-complexity iterative algorithm based on the Dinkelbach’s algorithm and the Lagrange duality is proposed to get optimal solution. Successive convex approximation (SCA) together with Dinkelbach’s algorithm are adopted to convert the non-convex trajectory optimization problem into convex to get a suboptimal solution for UAV trajectory. Through solving the two sub-problems iteratively by adopting block coordinate descent (BCD) untill convergence, the original problem is solved. Extensive simulation results demonstrate that the proposed algorithm not only has robust convergence properties but also achieves significant improvements in system EE while ensuring user fairness.

Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users’ heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning.

To provide a better support for various vehicular applications, digital twin (DT), as an emerging technology, can enable a virtual presentation of physical vehicular networks to reflect the current network state through real-time data updating. However, the constrained resources and high data updating cost may degrade the performance of DT. In this paper, we trade off the data updating cost and the performance of DT to adaptively determine the resource management and computation offloading in vehicular networks. Specifically, we propose a novel vehicle to vehicle pairing prediction algorithm assisted by DT to improve the offloading decision efficiency and investigate the effect of data updating frequency on prediction accuracy. Based on the prediction results, we formulate a joint data updating frequency selection, offloading decision and channel allocation problem with the objective of minimizing the computation and communication costs. To solve the formulated problem, we propose a prediction-based stability maximum pairing algorithm to obtain the proper task offloading strategy. Moreover, a deep Q-learning network algorithm is proposed to select the optimal DT data updating frequency according to the real-time vehicular network state. Based on the obtained optimal solution, we further propose an alternating direction method of multipliers-based iteration algorithm to optimize the computation and channel resource allocation and minimize the total costs. Numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms.

Network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the Quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.

In unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) systems, where UAVs act as aerial relays to forward tasks from ground users (GUs) to remote edge servers (ESs) for processing, a crucial observation is that the computing power in the system depends on the computing capabilities at a single ES and the number of ESs covered by the UAV. The latter is essentially influenced by the UAV altitude, ES density, transmit power of the UAV, channel condition, etc. In this paper, we model a UAV-assisted MEC system featuring adjustable UAV altitude, random GU distribution, and random ES distribution. We adopt the signal-to-noise ratio-based coverage probability and derive a computing model to characterize communication-aware altitude-dependent computing power. Upon this, we model the sequential task-processing process, including task uploading, forwarding, and computing, as a three-stage tandem queue (M/D/1 →D/1 →D/1). Employing queueing theory, we derive analytical results for the end-to-end (e2e) service latency. Besides, we address the optimization problem of maximizing the number of completed tasks within the e2e latency constraint, referred to as task service throughput. Simulation and analytical results show that optimal UAV altitudes, yielding the maximum task computing throughput, can be obtained under given network parameters.

To provide satisfying services for ever-emerging mission-critical applications, the ultra-reliable and low-latency communications (URLLC) need novel design to improve the spectrum efficiency and enhance the robustness. To achieve this, we design a robust rate-splitting and beamforming scheme for the downlink multiuser URLLC system in finite blocklength regime under imperfect channel state information at the transmitter (CSIT) acquisition. Rate-splitting is utilized to deal with the complex inter-user interference and improve the spectrum efficiency. Considering the norm-bounded CSIT error model, we formulate a minimum user rate maximization problem to guarantee the URLLC performance requirements by jointly designing the rate-splitting factors and the common/private beamforming vectors. The corresponding constraints are infinite due to the uncertainty of CSIT and the constraint set is also non-convex. To tackle it, we convert the infinite constraints into finite ones utilizing S-Procedure, and transform the original problem into difference of convex (DC) programming. Efficient approaches based on constrained concave convex procedure and Gaussian randomization are proposed to solve the DC programming and generate initial feasible points. Through extensive simulations, the convergence, robustness and effectiveness proprieties of the design are investigated and confirmed. Compared with the baselines, our design can achieve obvious performance improvement for different blocklength and block error rate requirements.

By encoding the video into different bitrate versions, dynamic adaptive streaming over HTTP (DASH) demonstrates its unique advantages in providing flexible bitrate adaption service in dynamic environments. But, the price is that the amount of video data is dramatically increased. The interaction of massive video data tends to exacerbate the network congestion and degrades the quality of experience (QoE) of users. Edge caching and mobile edge computing (MEC) have been adopted to solve this problem and enhance the QoE. But it is still difficult because of the highly coupled nature of caching and computing, which makes it extremely challenging to coordinate them across multiple edge nodes. To address the problem, this paper devotes itself to investigating collaborative edge caching and computing to maximize QoE for adaptive video streaming. In doing so, an optimization problem is formulated by jointly designing the caching, computing and user bitrate adaption, which turns out to be an integer nonlinear programming (INLP) problem and is NP-hard in strong sense. To solve it, we include caching placement, joint computing and bitrate adaption into a two-stage optimization framework. Specifically, considering the fact that the caching placement is implemented at a relatively long timescale, the caching problem is reformulated based on the statistics of user requests. The reformulated problem is a multiple-choice knapsack problem (MCKP), which is solved by Lagrange dual method after relaxation. The joint computing and bitrate adaption problem is transformed into Markov decision process (MDP) problem, and is solved by deep deterministic policy gradient (DDPG) algorithm. Simulation results validate that the proposed scheme can significantly improve QoE when compared with state-of-the-art baselines.

Meeting the strict Quality of Service (QoS) requirements of terminals has imposed a significant challenge on Multi-access Edge Computing (MEC) systems, due to the limited multi-dimensional resources. To address this challenge, we propose a collaborative MEC framework that facilitates resource sharing between the edge servers, and with the aim to maximize the long-term QoS and reduce the cache switching costs through joint optimization of service caching, collaborative offloading, and computation and communication resource allocation. The dual timescale feature and temporal recurrence relationship between service caching and other resource allocation make solving the problem even more challenging. To solve it, we propose a deep reinforcement learning (DRL)-based dual timescale scheme, called DGL-DDPG, which is composed of a short-term genetic algorithm (GA) and a long short-term memory network-based deep deterministic policy gradient (LSTM-DDPG). In doing so, we reformulate the optimization problem as a Markov decision process (MDP) where the small-timescale resource allocation decisions generated by an improved GA are taken as the states and inputted into a centralized LSTM-DDPG agent to generate the service caching decision for the large-timescale. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms in terms of the average QoS and the cache switching costs.

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neither consider the interference caused by undesired scattering of RISs, nor the performance balancing between different transmission paths. To address this, we study an innovative multi-hop multi-RIS communication system, where a base station (BS) transmits information to a set of distributed users over multi-RIS configuration space in a multi-hop manner. The signals for each user are subsequently reflected by the selected RISs via multi-reflection line-of-sight (LoS) links. To ensure that all users have fair access to the system to avoid excessive number of RISs serving one user, we aim to find the optimal beam reflecting path for each user, while judiciously determining the path scheduling strategies with the corresponding beamforming design to ensure the fairness. Due to the presence of interference caused by undesired scattering of RISs, it is highly challenging to solve the formulated multi-RIS multi-path beamforming optimization problem. To solve it, we first derive the optimal RISs’ phase shifts and the corresponding reflecting path selection for each user based on its practical deployment location. With the optimized multi-reflection paths, we obtain a feasible user grouping pattern for effective interference mitigation by constructing the maximum independent sets (MISs). Finally, we propose a joint heuristic algorithm to iteratively update the beamforming vectors and the group scheduling policies to maximize the minimum equivalent data rate of all users. Numerical results demonstrate that the proposed transmission framework achieves superior throughput performance than benchmark schemes. Useful insights on how to leverage multi-reflection paths over RISs to boost the throughput performance are also drawn under different settings for the multi-hop multi-RIS communication systems.

Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmitting only task-related information does protect a certain level of user privacy, adversaries could apply model inversion techniques to reconstruct the raw data or extract useful information, thereby infringing on users’ privacy. To mitigate privacy infringement, this paper proposes an information bottleneck and adversarial learning (IBAL) approach to protect users’ privacy against model inversion attacks. Specifically, we extract task-relevant features from the input based on the information bottleneck (IB) theory. To overcome the difficulty in calculating the mutual information in high-dimensional space, we derive a variational upper bound to estimate the true mutual information. To prevent data reconstruction from task-related features by adversaries, we leverage adversarial learning to train encoder to fool adversaries by maximizing reconstruction distortion. Furthermore, considering the impact of channel variations on privacy-utility trade-off and the difficulty in manually tuning the weights of each loss, we propose an adaptive weight adjustment method. Numerical results demonstrate that the proposed approaches can effectively protect privacy without significantly affecting task performance and achieve better privacy-utility trade-offs than baseline methods.

Compressed sensing (CS) has emerged as a promising technique for reducing transmission data volume. Despite its significance, achieving a balance between the delay, energy consumption and data distortion caused by CS and transmission remains an understudied area in resource-constrained IoT systems. The emergence of multi-access edge computing provides a potential solution to the aforementioned issue by enabling the strategic implementation of CS either at IoT devices or an edge server (ES), depending on both bandwidth resources and computing resources at ES. In this paper, we investigate where to perform CS computation and how to determine the compression ratio and bandwidth allocation to minimize the weighted energy and distortion cost (WEDC) of all devices under latency requirements. We formulate a WEDC minimizing problem by jointly optimizing the task scheduling, compression ratio, and bandwidth allocation. Since the formulated problem is a mixed-integer and nonlinear programming, which is typically NP-hard, we decompose the original problem into two sub-problems and then develop an iterative algorithm to find the suboptimal solution. Extensive numerical results demonstrate the superiority of the proposed algorithm in reducing WEDC of all devices under delay constraints.

Unmanned aerial vehicles (UAVs) play a pivotal role in augmenting multi-access edge computing by facilitating low-latency services for ground units (GUs), especially in areas where the ground infrastructure is inadequate or damaged. In this context, the UAV trajectory planning and caching strategies assume paramount importance to ensure low-latency service delivery. Due to the limited caching and computing resources at a single UAV, it alone cannot provide effective services for a large number of GUs. In this paper, we design a novel cooperative framework for low-latency service provisioning by coordinating multiple UAVs's trajectories and service caching strategies. We formulate a latency minimization problem to jointly optimize both service caching and trajectory planning of multiple UAVs. However, due to the high dimension and coupling of multiple UAVs' movement and service caching, the optimization problem is a mixed-integer nonlinear programming, which is typically an NP-hard problem, and we propose an effective algorithm based on deep deterministic policy gradient to solve the high dimensional, non-convex, and continuous long-term optimization problem. Numerous experiments confirm that the proposed algorithm achieves significantly better performance in reducing the total system delay than other baseline algorithms.

Along with the deployment of Industrial Internet of Things (IIoT), massive amounts of industrial data have been generated at the network edge, driving the evolution of edge machine learning (ML). But during the ML model training, it may bring privacy leakage by traditional central methods. To address this issue, federated learning (FL) has been proposed as a distributed learning framework for training a global model without uploading raw data to protect data privacy. Since the communication and computing resources are usually limited in IIoT networks, how to reasonably select device and allocate bandwidth is crucial for the FL model training. Therefore, this article proposes a joint edge device selection and bandwidth allocation scheme for FL to minimize the time-averaged cost under the given long-term energy budget and delay constraints in the IIoT system. To tackle with this long-term optimization problem, we construct a virtual energy deficit queue and leverage the Lyapunov optimization theory to transform it into a list of round-wise drift-plus-cost minimization problems first. Then, we design an iterative algorithm to allocate reasonable bandwidth and select appropriate devices to achieve cost minimization while satisfying the energy consumption constraints. Besides, we develop an optimality analysis of the average cost and energy violation for our proposed scheme. Extensive experiments verify that our proposed scheme can achieve superior performance in cost efficiency over other schemes while guaranteeing FL training performance.

This work investigates proactive edge caching for device-to-device (D2D)-assisted wireless networks, where user equipment (UE) can be selected as caching nodes to assist content delivery to reduce the content transmission latency. In doing so, there are two challenges: 1) how to precisely get the user’s preference to cache the proper contents at UEs and 2) how to replace the contents cached at UEs when there are new popular contents emerging. To address these, we develop a user preference learning-based proactive edge caching (UPL-PEC) strategy. In the strategy, we first propose a novel context and social-aware user preference learning method to precisely predict user’s dynamic preferences by jointly exploiting the context correlation among different contents, the influence of social relationships and the time-sequential patterns of user’s content requests. Specifically, the bidirectional long short-term memory networks are adopted to capture the time-sequential patterns of the user’s content requests. And, the graph convolutional networks are developed to capture the high-order similarity representation among different contents from the constructed content graph. To learn the social influence representation, an attention mechanism is designed to generate the social influence weights to users with different social relationship. Based on the learned user preference, a proactive edge caching architecture is proposed to integrate the offline caching content placement and the online caching content replacement policy to continuously cache the popular contents at UEs. Simulation results show that the proposed UPL-PEC strategy outperforms the existing similar caching strategies at about 3.13%–4.62% in terms of the average content transmission latency.

In vehicular ad hoc networks (VANETs), emergency message broadcasting has been considered as one of the important parts for safety-related applications. The efficiency and reliability of the emergency message broadcasting are severely affected by the network structure, message redundancy, channel contention, etc. To address this problem, in this article, we propose roadside unit (RSU)-assisted hybrid emergency message broadcasting (RA-HEMB) protocol for two-way grid roads in urban VANETs. In doing so, the broadcasting target region is first generated based on the instantaneous traffic status and types of the emergency messages. To balance the deliver latency and the reliability, an adaptive forwarding nodes selection scheme based on position is proposed to dynamically determine the forwarding area according to different vehicle densities and vehicle-to-vehicle (V2V) communication ranges. Then, based on the forwarding nodes selection scheme and RSU deployment, an RA-HEMB protocol is established by properly utilizing both the wired links via RSUs and the wireless links to broadcast the emergency message in hybrid vehicular networks. Simulation results show that the proposed RA-HEMB can greatly shorten the broadcasting time needed to notice the nodes within the target region performance compared to the benchmark protocols.

Due to the limited energy, communication bandwidth and computing ability of edge devices in Industrial Internet of Things (IIoT) networks, it is incredibly challenging to compress and transmit those massive manufacturing data collected at the edge, thus greatly degrading the transmission and computing efficiency and finally results in long latency. To address this, we propose a quantized deep compressed sensing network (QDCS-Net) for both linear and nonlinear measurements to help better compress the industrial data to reduce the transmission volume of data and achieve good reconstruction performance. The joint design of customized quantization layers, dual-path structures, and swish activation function in QDCS-Net is adopted to achieve high-precision data reconstruction at high compression ratios. The latency is analyzed for different transmission deployment schemes to get a better edge-cloud collaboration strategy. We evaluate QDCS-Net by using real-world datasets collected from a vibration signal acquisition system. Experimental results demonstrate that the proposed QDCS-Net performs better in recovering industrial signals even at extremely low compression ratios of 1/128, thus can effectively improve data reconstruction accuracy and communication efficiency.

Communication and sensing are two important features of connected and autonomous vehicles (CAVs). In traditional vehicle-mounted devices, communication and sensing modules exist but in an isolated way, resulting in a waste of hardware resources and wireless spectrum. In this paper, to cope with the above inefficiency, we propose a vehicular behavior-aware integrated sensing and communication (VBA-ISAC) beamforming design for the vehicle-mounted transmitter with multiple antennas. In this work, beams are steered based on vehicular behaviors to assist driving and meanwhile provide spectral-efficient uplink data services with the help of a roadside unit (RSU). Specifically, we first predict the area of interest (AoI) to be sensed based on the vehicles’ trajectories. Then, we formulate a VBA-ISAC beamforming design problem to sense the AoI while maximizing the spectral efficiency of uplink communications, where a trade-off factor is introduced to balance the communication and sensing performance. A semi-definite relaxation-based beampattern mismatch minimization (SDR-BMM) algorithm is proposed to solve the formulated problem. To reduce the hardware cost and power consumption, we further improve the proposed VBA-ISAC beamforming design by introducing the hybrid analog-digital (HAD) structure. Numerical results verify the effectiveness of VBA-ISAC scheme and show that the proposed beamforming design outperforms the benchmarks in both spectral efficiency and radar beampattern.

This work investigates proactive edge caching for D2D-assisted wireless networks, where user equipments (UEs) can be selected as caching nodes to assist content delivery. The objective of this work is to achieve a trade-off between the cost for providing caching services and the content transmission latency. Doing so, there are two challenges: 1) Which UEs can be selected as caching nodes; 2) How to place contents on these selected UEs without user’s privacy disclosure. To address these, a novel community detection and attention-weighted federated learning based proactive edge caching (CAFLPC) strategy is proposed. In the strategy, we first group UEs into different communities based on both the mobility and social properties of UEs, and then select important users (IUs) as caching nodes for each community by considering the social importance of UEs. To determine how to place the popular contents in these selected IUs, an attention-weighted federated learning (AWFL) based content popularity prediction framework is proposed. It integrates the attention-weighted federated learning with Bidirectional Long Short Term Memory Network (AWFL_BiLSTM) to achieve a higher content popularity prediction accuracy while protecting user’s privacy. Considering the imbalance of UEs’ active levels and local computing capacities, an attention-weighted aggregation mechanism is proposed to improve the training efficiency and prediction accuracy. Simulations results show that the proposed CAFLPC strategy outperforms the compared existing caching strategies at about 2.2%-35.1% in terms of the transmission latency reduced by per unit cost.

Edge caching has emerged as a promising technique against latency caused by explosive growth of mobile data traffic through caching popular contents at the edge networks. However, the dynamically changing content popularity nature and limited caching capacity make it challenging to design an effective caching scheme to reduce latency. To solve this, a learning-based hierarchical edge caching (LHEC) scheme is proposed in this work. We first propose a novel deep learning architecture, namely Stacked Autoencoder-Long Short Term Memory Network (SAE-LSTMNet) to capture both the correlation of the request patterns among different content and the periodicity in time domain to improve the prediction accuracy of the content popularity. Then, to predict the popularity of these newly-added contents, a dynamic content catalog is introduced and a similarity-based content popularity prediction (SCPP) approach is proposed. Based on the content popularity prediction, a hierarchical edge caching optimization problem is formulated to minimize the average content downloading latency. Since the formulated problem is NP-hard and difficult to be solved, a low-complexity algorithm is proposed to obtain the near-optimal solutions. Simulation results show that the proposed content popularity prediction approach outperforms up to 6.36% in terms of the mean absolute error compared with the state-of-the-art methods and the proposed LHEC scheme reduces the average downloading latency at about 5.3%~7.9% compared with those existing caching schemes.

This paper investigates the dense multi-user sparse code multiple access (SCMA) cellular networks, where base stations (BSs) and users are randomly deployed in a 3D space by using stochastic geometry tools. First, a method increasing the dimension of the assignment matrix is presented to provide massive connectivity in each cell. The random pattern of arrangement of non-zero elements access (RPA) policy is utilized to allocate the available SCMA resources. Then the coverage probability is characterized under the nearest-BS (NBS) association mechanism by developing a multi-user connectivity model based on the RPA policy and average connectivity ratio (ACR). A compact closed-form expression of the coverage probability and its approximation are derived by utilizing the Toeplitz matrix and finite sum of power series, respectively. We then extract the area potential spectral efficiency (APSE) based on the coverage probability, and provide new insights into the 3D dense multi-cell SCMA networks. Simulation results confirm the precision of the theoretical results and demonstrate the advantage of the SCMA scheme.

With the development of computation-intensive applications, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) provides task offloading service for the users with or without terrestrial infrastructure support. Meanwhile, the next generation of UAVs communication systems are expected to be user-centric. Therefore, more attention should be paid to users’ satisfaction with the offered service. In this paper, we study the service satisfaction-oriented task offloading and UAV scheduling problem for UAV-enabled MEC networks, where the task priorities are considered based on the delay requirements of users’ tasks and the remaining energy status of users. Specifically, we firstly divide the users into different groups via the K-means-based grouping algorithm. Then, we develop a novel user satisfaction model by jointly considering the task processing delay and energy saving, based on which a total user satisfaction maximization problem is formulated to jointly optimize the task offloading decisions and UAV scheduling strategy. To solve the formulated problem, we decompose it into two sub-problems, i.e., the UAV scheduling sub-problem and the task offloading sub-problem. To solve the first sub-problem, we develop a genetic algorithm (GA)-based UAV scheduling algorithm through dealing with multiple balanced assignment problems. To address the second sub-problem, a GA-based task offloading algorithm is developed. Then, we propose a joint task offloading and UAV scheduling optimization algorithm to solve the original optimization problem. Finally, simulation results demonstrate that the proposed optimization algorithm not only converges fast, but also improves the total users’ satisfaction greatly. The total users’ satisfaction improved by the proposed scheme is up to 16.9% in the case of 170 users.

Abstract In this paper, a novel co‐operative mean field type game based task offloading strategy is proposed for multi‐access edge computing networks. The objective is to optimally determine the tasks offloaded to each edge computing node (ECN) in the network so that to balance the overall system performance and individual revenue of each ECN. To do so, the utility function of each ECN is first formulated, which is the payback of his time and energy cost for processing the tasks, tailed with the penalty from the network for not processing the tasks on time. A mean field type game is then formulated, where each ECN can either cooperate or compete with each other to maximize their own utility. With respect to the proposed game, the direct method is applied to find the final solutions. Simulation results reveal that the proposed strategy can well balance the utility of each ECN and the overall efficiency of the system, leading to a better performance compared with its cooperative‐ and competitive‐counterparts.

Abstract An interference leakage minimisation transceiver design for multiple‐input multiple‐output Interference Channel networks is proposed by making use of the full rank constraint of the desired signal, the low rank constraint and low power constraint of the interference signal. The objective is to suppress interference leakage caused by not only the signal from other users, but also the other streams from the same user. To do so, the transmit precoding matrix and the receive filtering matrix are iteratively optimised through convex optimisation tools at stream level. Furthermore, a Min–Max interference leakage algorithm is also proposed to suppress the maximum interference from user, with the purpose of guaranteeing the fairness among users. Simulation results demonstrate that taking inter‐stream interference into consideration can significantly improve the effectiveness of multiple‐input multiple‐output Interference Channel networks, while the Min–Max method can slightly increase the system capacity under certain conditions. It can be also confirmed that the trade‐off among effectiveness, fairness and robustness exists in the transceiver optimisation of multiple‐input multiple‐output Interference Channel networks.

Vehicle-to-vehicle communications can offer direct information interaction, including security-centered information and entertainment information. However, the rapid proliferation of vehicles and the diversity of communications services demand for a more intelligent and efficient resource allocation framework to enhance network performance. In this article, a multi-agent deep reinforcement learning-based resource allocation framework is developed to jointly optimize the channel allocation and power control to satisfy the heterogeneous Quality-of-Service (QoS) requirements in heterogeneous vehicular networks. In the proposed framework, the utility maximization problem is formulated by considering two types of traffics, i.e., the strict ultrareliable and low-latency requirements for safety-centric applications and the high-capacity requirements for entertainment applications. The utility of each vehicular users is formulated as a multicriterion objective function by taking into account the heterogeneous traffic requirements. To overcome the drawbacks of the traditional totally centralized and distributed deep reinforcement learning-based resource allocation approaches, we propose a multi-agent deep deterministic policy gradient algorithm with centralized learning and decentralized execution to solve the formulated optimization problem. The normalization of the input states and reward functions is introduced to speed up the training and learning progress of the proposed algorithm. Simulation results show the superiority of the proposed algorithm in terms of the convergence and system performance through the comparison with the other methods and schemes for the delay-sensitive applications and delay-tolerant applications.

Unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks have shown a huge advantage in providing on-demand communication and computation service for the ground users. To reap the benefit of these integrated networks, reducing their energy consumption becomes a key issue, since both UAVs and ground users are energy-limited devices. To address this problem, this article attempts to provide a novel method that optimizes communication, computation, and control (3C), i.e., user association, computational task offloading, and UAVs flight control, to reduce the communication and computation energy consumption. Specifically, in order to find out the appropriate deployment positions for UAV MEC servers to provide on-demand communication and computation service, we propose the concept of the virtual force field (VFF) based on the user statistical distribution model and then devise a coordinated flight control algorithm for UAV MEC servers. After that, the user association and computational task offloading are optimized alternately. Utilizing the optimal transport theory (OTT), we derive the boundary formulation of the optimal user association and develop an iterative algorithm to approach the optimal association boundary. Then, given the user association, the optimal computational task offloading scheme is investigated. The convergence of the proposed iterative algorithm and the alternating optimization algorithm is proved. The complexity of the 3C optimization method is also analyzed. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithm. Comparisons with the benchmark scheme show that the proposed scheme can reduce about 88% energy consumption and also improve energy efficiency performance greatly under the same simulation setups.

Device-free gesture recognition is a potential noncontact human–computer interaction technique. It leverages the unique influence of the conducted gesture on surrounding wireless signals to accomplish gesture recognition. Existing methods usually leverage doppler spectrogram of the influenced wireless signals to characterize the motion pattern of gestures. These methods have achieved satisfactory accuracy when the gestures are conducted in a relatively fixed location, direction, and speed. However, when gestures are conducted in a different scenario, the recognition accuracy will drop dramatically. In this article, we try to solve this issue by characterizing the gesture motion pattern using a novel robust intrinsic spectrogram, which is independent of the conducted scenario. Specifically, we create a virtual coordinate system in which the coordinates of a gesture trajectory remain unchanged no matter where and how the gesture is conducted. Then, we design a coordinate transformation method to transform the raw doppler spectrogram into the robust intrinsic spectrogram to characterize the intrinsic motion pattern of the gesture. We further feed the intrinsic spectrogram into a deep network to realize gesture recognition. Extensive evaluations on a 77-GHz mmWave testbed show that the proposed method could achieve an average recognize accuracy of 88.4% with ten types of gestures.

With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this article, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on five benchmark datasets. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

We propose a full-duplex cooperative rate-splitting (FD-CRS) scheme in a downlink two-group multicast system. At the transmitter, two distinct messages requested by the two groups respectively are split and then encoded into one common stream and two private streams, based on the principles of rate splitting multiple access (RSMA). The cell-center-users (CCUs) in one group decode the common stream and their own private stream successively, then cooperatively form a distributed beamformer to assist the cell-edge-users (CEUs) in common stream transmission. To make full utilization of the time resources during cooperation, all the CCUs operate in FD mode to enable information receiving and forwarding simultaneously. Moreover, since it is unfair to sacrifice the cooperator’ energy to forward, each CCU is enabled to harvest energy from the received signal by adopting power-splitting protocol. With the objective of minimizing the system transmission power while guaranteeing all the groups’ target rates, an optimization problem is formulated to jointly design the beamformers, message splitting and power-splitting ratio. We reformulate the non-convex problem by using the difference of convex (DC) programming, and then propose an iterative algorithm based on successive convex approximation to solve it to obtain a local minimum. Further, a robust algorithm combining the semi-positive definite relaxation (SDR) technique and penalty function method is developed for the case with imperfect channel state information. Although our proposed FD-CRS scheme adopts the seemingly energy-wasting wireless power transfer technique, the simulation results still confirm the superiority of the proposed scheme, i.e., it outperforms the other baseline schemes in terms of power consumption under various user deployment, network loads and target rates. That is attributed to the comprehensive utilization of FD cooperation gain, spatial multiplexing gain as well as power multiplexing gain.

Reconfigurable intelligent surface (RIS) provides a promising way to build programmable wireless transmission environments. Owing to the massive number of controllable reflecting elements on the surface, RIS is capable of providing considerable passive beamforming gains. At present, most related works mainly consider the modeling, design, performance analysis and optimization of single-RIS-assisted systems. Although there are a few of works that investigate multiple RISs individually serving their associated users, the cooperation among multiple RISs is not well considered as yet. To fill the gap, this paper studies a cooperative beamforming design for multi-RIS-assisted communication systems, where multiple RISs are deployed to assist the downlink communications from a base station to its users. To do so, we first model the general channel from the base station to the users for arbitrary number of reflection links. Then, we formulate an optimization problem to maximize the sum rate of all users. Analysis shows that the formulated problem is difficult to solve due to its non-convexity and the interactions among the decision variables. To solve it effectively, we first decouple the problem into three disjoint subproblems. Then, by introducing appropriate auxiliary variables, we derive the closed-form expressions for the decision variables and propose a low-complexity cooperative beamforming algorithm. Simulation results have verified the effectiveness of the proposed algorithm through comparison with various baseline methods. Furthermore, these results also unveil that, for the sum rate maximization, distributing the reflecting elements among multiple RISs is superior to deploying them at one single RIS.

Exploiting unmanned aerial vehicles (UAVs) as flying relays is becoming an indispensable strategy to assist terrestrial cellular networks to enhance coverage. One challenging problem for UAV-integrated cellular networks is how to design their deployment and association schemes to provide on-demand coverage with minimum network power consumption. In this paper, the uplink transmission in a UAV-assisted cellular network is studied with the objective of minimizing the transmit power consumption of users and UAVs through designing proper UAV deployment and association schemes. To avoid the computational complexity caused by the estimation of instantaneous position of users, we investigate UAV deployment and association schemes based on the statistical user position. By discretizing the space where UAV can be located, we build a centralized multi-agent$Q$-learning algorithm, with which multiple UAVs update their positions in a joint manner. In the training process of$Q$-learning algorithm, a reward function is built based on the optimal association scheme and its corresponding power consumption. By adopting the optimal transport theory, the existence of the unique optimal association scheme for given statistical user distribution and UAVs’ state is proved. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithms. Comparisons with the benchmark scheme show that the proposed scheme can bring about 85% energy efficiency improvement under the same simulation setups.

Wireless traffic prediction has drawn increasing research interests as it can provide guidance to the network optimization. With the predicted information, one can preassign the resources on demand and perform network congestion control adaptively. The network efficiency is therefore enhanced. However, the wireless traffic prediction in the context of mobile scenario, such as Internet of Vehicles (IoVs), is still a challenge issue. The mobile nature of devices, which dynamically changes the topology of network, would brings difficulties to the prediction. This paper focuses on the deep learning based wireless traffic prediction in the IoVs scenario. We first propose a novel method to match up the movement- and communication-behavior of users, by merging two independent datasets on the trajectories of vehicles and communication traffic volumes together. Then a novel STeP-UNet is proposed, in which the SpatioTemporal Partial (STeP) Convolutional Neural Network module is embedded to capture cross-domain features of the wireless traffic pattern, and the UNet structure is utilized to realize the skipping connection from front layer to back layer to fuse different resolutions. Experimental results confirms the promising performance of the proposed model, where 4%~8% performance improvement over other benchmark methods can be achieved.

With the development of Industrial Internet of Things (IIoT), the computation intensive tasks with restrict delay constraints generated at network edge emerge as the main challenge to the terminals with limited power and processing capability. The combination of edge and cloud computing has been demonstrated as one promising solution to such problem. In the edge-cloud collaboration (ECC) framework, the task scheduling among edges and cloud is of great importance on impacting the performance of the system. In this paper, we investigate the task offloading strategy to minimize the energy consumption of the networks. To achieve that, a time delay penalty mechanism, which searches the optimal power for edge to cloud task offloading under given delay constraint, is first proposed. On that basis, a low complexity edge-cloud matching algorithm leveraging the bipartite matching method is developed, to further minimize the execution energy consumption of all devices. Finally, to evaluate its efficiency, the proposed algorithm is deployed and tested on an novel edge-cloud computing collaboration platform. Both simulation and experiment results revel that our proposed scheme can achieve the less energy consumption compared with other alternatives. In addition, it also indicates that our proposed scheme can effectively matching resources from the edge to the cloud, especially for the issues that edge devices fail to meet demands due to limit processing ability.

Edge caching has been explored as an effective technology to alleviate the heavy traffic burden of the backhaul and avoid transmission congestion in vehicular networks. However, high mobility of vehicles could lead to repetitive content caching, resulting in high system cost. Because content popularity changes very frequently in vehicular networks, to provide better service for vehicle users, it is essential to update content frequently. This leads to expensive update cost at the same time. To reduce such cost, we propose a mobility-aware cost effective edge caching strategy, in which vehicle mobility, file encoding technology and dynamic content popularity are jointly taken into consideration. To reduce the complexity of formulated problem, deep reinforcement learning (DRL) approach is adopted. Simulation results show that the proposed mobility-aware coded edge caching strategy can dramatically reduce the system cost (up to 36% compared with classic caching algorithm).

We propose a cooperative nonorthogonal multicast scheme for multiple-input-single-output (MISO) systems, where the transmitter sends the superimposed signal to two multicast groups. After successfully detecting all signals, the cell-center users (CCUs) in one multicast group help relay signal to the cell-edge users (CEUs) in another group to enhance signal reception. Simultaneous wireless information and power transfer is adopted at CCUs to assist information relaying. In such a system, to minimize the transmission power of the system while satisfying the quality of service requirements of all the users, an optimization problem is formulated to jointly design the transmitter beamformers, distributed CCUs beamformer, as well as the power-splitting (PS) ratios. To solve the nonconvex problem, we first equivalently transform it into a difference of convex (DC) programming. Then, a low-complexity iterative algorithm based on the constrained concave convex procedure (CCCP) is proposed to solve the DC programming one. In addition, a robust joint beamforming and PS scheme is proposed by assuming imperfect channel state information (CSI). The infinite constraints caused by CSI uncertainties are converted into finite ones utilizing the S-procedure. To obtain a rank-one locally optimal solution, the penalty function method and CCCP algorithm are adopted. The simulation results reveal that the proposed cooperative scheme can greatly reduce the transmission power and outperform the baselines within a certain range. Moreover, the robustness and effectiveness of the robust design under imperfect CSI case have been validated.

Due to the booming of various devices in Internet-of-Things (IoT) networks, more data should be transmitted over the networks, which will thereby consume more transmission bandwidth and more transmit power. Compressed data aggregation (CDA) has been proposed as an effective way to reduce the amount of the collected data in IoT networks. Although adopting compressed sensing (CS), CDA can sample the source data efficiently, and sparse data reconstruction is still a big challenge. In this work, inspired by this, we propose a learning-based sparse data reconstruction scheme by jointly utilizing CS and deep learning. Our objective is to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy. A deep CS network is designed by adopting an end-to-end learning method to build a measurement matrix and an efficient and high-accuracy reconstruction network. To show the performance of the proposed scheme, six data sets with different structured sparse models and a real sensor data set are utilized in doing experiments. The performance of the proposed scheme in terms of mean-squared error, peak-signal-noise-ratio, and structural similarity is investigated. The results demonstrate the effectiveness of the proposed scheme in reconstruction accuracy for given compression ratio. The results also show that the proposed scheme is suitable for the process of CDA, thus can effectively reduce the amount of data to be transmitted in IoT networks.

When doing image classification, the core task of convolutional neural network (CNN)-based methods is to learn better feature representation. Our analysis has shown that a better feature representation in the layer before softmax operation (BSM-layer) means a better feature embedding location that has a larger distance to the separating hyperplane. By defining this property “Location Property” of CNN, the core task of CNN-based methods can be regarded as to find out the optimal feature embedding location in the BSM-layer. In order to achieve this, in this work, we first propose two feature embedding directions, principal embedding direction (PE-direction) and secondary embedding direction (SE-direction). And then, we further propose a loss-based optimization framework, location property loss (LP-loss), which can make feature representation move in the PE-direction and the SE-direction simultaneously during the training phase. LP-loss consists of two parts, LPPEand LPSE, where LPPEfocuses on PE-direction, and LPSEfocuses on SE-direction. Any loss function focusing on these two embedding directions can be chosen as LPPEand LPSE. Based on the analysis that softmax, L-softmax, and AM softmax can make the feature representation move in PE-direction to a different extent, any of them can be chosen as LPPE. Since there is no existing works can fulfill the purpose of LPSE, a novel loss, secondary optimal feature plane loss (S-OFP loss), is developed. S-OFP loss is designed to make feature representations belonging to the same category embed onto their corresponding S-OFP. It is proved that S-OFP loss is the optimal feature plane in the SE-direction. Experiments are done with shallow, moderate, and deep models on four benchmark data sets, including the MNIST, SVHN, CIFAR-10, and CIFAR-100, and results demonstrate that CNN models can obtain remarkable performance improvements with LPsoftmax, S-OFP and LPAM softmax, S-OFP, which verify the effectiveness of location property.

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices’ energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

Reconfigurable intelligent surface (RIS) provides a promising way to build the programmable wireless transmission environments in the future. Owing to the large number of reflecting elements used at the RIS, joint optimization for the active beamforming at the transmitter and the passive reflector at the RIS is usually complicated and time-consuming. To address this problem, this article proposes a low-complexity joint beamforming and reflecting algorithm based on fractional programing (FP). Specifically, we first consider a RIS-aided multi-user communication system with perfect channel state information (CSI) and formulate an optimization problem to maximize the sum rate of all users. Since the problem is nonconvex, we decompose the original problem into three disjoint subproblems. By introducing favorable auxiliary variables, we derive the closed-form expressions of the beamforming vectors and reflecting matrix in each subproblem, leading to a joint beamforming and reflecting algorithm with low complexity. We then extend our approach to handle the case when transmitter-RIS and RIS-receiver channels are not perfect and develop corresponding low-complexity joint beamforming and reflecting algorithm with practical channel estimation. Simulation results have verified the effectiveness of the proposed algorithms as compared to various benchmark schemes.

With the rapid development of the Internet of Things (IoT) technology, how to meet the execution requirements of sensitive services has become a key point to be solved in application scenarios such as smart cities and Internet of Vehicles. Combining with the advantages over edge computing and cloud computing, building the edge-cloud collaboration framework is currently a hot research area. In this paper, a Raspberry Pi edge-cloud collaboration framework (RPECCF) is proposed to effectively reply the complicated application requirements in multi-scenes. Furthermore, to evaluate the performance of the RPECCF, we develop an experiment platform. Experimental results show the RPECCF platform is stable, also allocate the edge and cloud resources properly. The proof-of-concept demonstration of the platform is studied in terms of task latency and framerate of both edge only, edge-cloud collaboration, and cloud only.

This paper deals with the joint time and power allocation problem in the cooperative Non-Orthogonal Division Multiple Access (NOMA) based Mobile Edge Computing (MEC) system. We consider a basic three-node MEC system consisting of a Far User (FU), a Near User (NU), and a Base Station (BS) equipped with the MEC server. In the proposed system, the tasks of the users are divided into two parts which are executed locally and at the edge server, respectively. Moreover, the NU would help offload the FU's task together with his own through NOMA transmission. The optimization problem of joint cooperation time slots assignment and power allocation at both NU and FU is formulated to minimize the total energy consumption of users. To efficiently solve it, an algorithm is proposed through employing Lagrange duality method. Simulation results demonstrate that the proposed algorithm can outperform the existing schemes and improve the energy efficiency of the system remarkably.

IEEE 802.11p standard, operating over the 75-MHz spectrum at 5.9-GHz band with one control channel (CCH) and six service channels (SCHs), has been poised to provide V2X services over vehicular ad hoc networks (VANETs). However, due to the absence of central coordinator and the nature of high vehicular mobility, it is difficult to achieve reliable multichannel coordination and adaptive resource reservation to make full use of SCHs, resulting in dramatic throughput degradation. To mitigate this, in this article, we propose an adaptive high-throughput multichannel medium access control (MAC) protocol, namely, AHT-MAC, which can effectively handle the data transmissions over SCHs. With AHT-MAC, the data transmission range (TR) is adjusted according to the beacon TR over the CCH so that a transmitting node can determine proper communication candidates and prepare available resources for both communication nodes before transmissions. Moreover, the communication coordination is done through a two-way handshake. During the handshake, adaptive resource reservation is realized following the proposed resource sharing mechanism, where nodes first utilize as much resource as possible and then share them with others proactively. To increase the success probability of the communication handshake, a request conflict resolution mechanism is also designed to nullify improper handshakes. Therefore, AHT-MAC can reduce the resource wastage due to handshake failures and extra overheads for retransmission requests. Our performance analysis shows that AHT-MAC can significantly improve the system throughput and reduce the channel access period.

Reconfigurable intelligent surface (RIS) has emerged as a promising technique for future wireless communication networks. How to reliably transmit information in a RIS-based communication system arouses much interest. This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information. Depending on that the transmitter and RIS jointly or independently deliver information, RM is further classified into two categories: jointly mapped RM (JRM) and separately mapped RM (SRM). JRM and SRM are naturally superior to existing schemes, because the transmit signal vectors, reflecting patterns, and bit mapping methods of JRM and SRM are more flexibly designed. To enhance transmission reliability, this paper proposes a discrete optimization-based joint signal mapping, shaping, and reflecting (DJMSR) design for JRM and SRM to minimize the bit error rate (BER) with a given transmit signal candidate set and a given reflecting pattern candidate set. To further improve the performance, this paper optimizes multiple reflecting patterns and their associated transmit signal sets in continuous fields for JRM and SRM. Numerical results show that JRM and SRM with the proposed system optimization methods considerably outperform existing schemes in BER.

This paper presents an asymptotic capacity analysis for multiple-input multiple-output (MIMO) communications with insufficient transmit radio frequency (RF) chains and sufficient receive RF chains, which is named as iMIMO communications. We characterize the iMIMO channel capacity by the maximum mutual information given any vector inputs subject to not only an average power constraint but also a sparsity constraint. It is proven that an optimized Gaussian mixture input distribution is capacity-achieving in the high signal-to-noise-ratio (SNR) regime. The optimal mixture coefficients and the covariance matrices of the Gaussian mixtures are derived and also the corresponding asymptotic capacity. Furthermore, we discuss the impact of insufficient receive RF chains on the achievable spectral efficiency. We investigate the superiority of the capacity-achieving technique, which is an optimized non-uniform subspace modulation (NUSM), by comparing it with the best subspace selection (BSS) and the uniform subspace modulation (USM). The comparison results reveal that the optimized NUSM is optimal in the high SNR regime. Numerical results are presented to validate our analysis.

As one of the indispensable components in convolutional neural network (CNN), loss function assists in updating parameters of CNN models during the training phase. Generally, different loss functions can assist convolutional neural network (CNN) to learn different feature representations, and different feature representations can be treated as different knowledge learned from objects. In this paper we introduce a novel training framework, namely Loss Transferring (LT), to improve the generalization ability of CNN. LT contains multiple training phases, and each training phase uses a different loss function. Under this framework, CNN models can combine different knowledge of objects by transferring the knowledge learned via one loss function to another. LT contains two components, i.e., loss function set and training strategy. In order to build appropriate loss function set, we establish two basic guides. And according to these basic guides, we design a new loss function in the last layer of CNN models (layer before softmax operation), namely Near Classifier Hyper-Plane (N-CHP) loss, which makes the learned object features belonging to the same category have the minimum intra-class distance and be near the classifier hyper-plane. Based on the two loss function set (MSE, softmaxl and (N-CHP, softmaxl, we setup two specific training methods, LTMSE, softmax and LTN-CHP, softmax, which can be universally applied to different CNN models with low additional computation cost. Meanwhile, two training strategies, multi-phase strategy 1 and multi-phase strategy 2, are further proposed to improve the training efficiency of LT. Extensive experimental results on shallow, moderate and deep models with four benchmark datasets, including MNIST, SVHN, CIFAR-10 and CIFAR-100, demonstrate that CNN models can bring obvious performance improvements when working with LTMSE, softmax and LTN-CHP, softmax, which verifies the effectiveness of LT and the proposed two basic guides.

This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.

In cellular Internet of Things networks, massive access and highly dynamic traffic of machine type communication devices may cause network congestion, heavy energy consumption or even service unavailability. To reduce the amount of data transmission and improve the efficiency of data aggregation in sporadic machine type communications, we develops a two-stage compressed data aggregation scheme by jointly utilizing compressive sensing and sparse autoencoder. A deep compressive sensing network (DCSNet) is designed by using deep learning method to reconstruct signals from compressive measurements. Experimental results demonstrate that, our scheme can effectively reduce the data traffic, achieve higher reconstruction accuracy in different sparse conditions of signal and brake the obstacles that compressive sensing method has a large reconstruction error when the measured data is small.

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mm ave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (F-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM). Inspired by the concept of spatial modulation, we not only use the selected beamspace to transmit information but also use the selection operation to carry information. e prove that GBMM is superior to BBS in terms of SE and can break through the well known upper bound. That is, GBMM renews the upper bound of the system SE. e investigate SE-oriented precoder activation probability optimization, fully-digital precoder design and hybrid precoder design for GBMM. Comparisons with the benchmark (i.e., F-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM.

This paper investigates an energy efficiency (EE) and delay aware user association problem in millimeter wave (mmWave) downlink networks, in which the wire backhaul links are replaced by the wireless backhaul links to transmit traffic data. To characterize the mmWave network delay performance, the queue theory is adopted and an M/M/1 tandem queuing model is introduced to capture the data transmission latency over mmWave backhaul links and access links. Besides, in order to measure the network EE and delay performance simultaneously, a novel EE-delay aware utility function is constructed and the user association problem is formulated. It is proved that the formulated problem is NP-hard. To solve it, one many-to-one matching game is reformulated and a two-steps user association algorithm is proposed. Simulation results show that the proposed association scheme can obtain significant utility improvement, achieving up to 21% gains compared with the classic association schemes. Moreover, the proposed scheme also demonstrates its superiority on load balancing management in mmWave backhaul networks.

Diabetic retinopathy severity assessment is an important domain in which deep learning has benefited medical imaging analysis. In this regard, CNNs which perform well in ImageNet are incapable of extracting subtle lesion features from high-resolution retinal fundus images. So novel convolutional networks with higher input size were developed. But no prior work give deep investigation on the impact of image resolution in the context of DR severity assessment. In this paper, we first explore how the performance of diabetic retinopathy severity assessment task would change if higher-resolution input images were used. Next, we adopt the stepwise strategy of training convolutional networks with high input scales to avoid overfitting. Finally, rigorous analyses on the impact of image resolution are given, showing that as model expands with higher input image resolutions, the performance grows logarithmically while both time and space complexity increase exponentially. Our model obtains new state-of-the-art kappa score in the task of diabetic retinopathy severity assessment task on EyePACS dataset with convolutional networks whose input size is 896 × 896, and great progress in classification of mild diabetic retinopathy. There is great potential for generalizing this solution to other medical image analysis problems.

With the rapid growth on data traffic, spectrum shortage becomes increasingly serious, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to utilize shared spectrums effectively for service provisioning is not straightforward due to its uncertain availability, known as spectrum uncertainty. In this paper, we propose a new metric to evaluate the achievable rate of a link on a share band under a confidence level, called probabilistic link capacity, which offers us an effective way to guarantee the quality of service statistically when using the shared spectrum for service delivery. Different from most existing works where the distributional information is explicitly given based on certain structural assumption, we develop a data-driven distributionally robust approach by using the first and second order statistical information. To achieve the result, we formulate it into a tractable semidefinite programming problem based on the worst-case of conditional-value-at-risk. Finally, as a use case, we design a service-based spectrum-aware transmission scheme, so that different kinds of spectrums (licensed and shared) can be efficiently utilized to satisfy the diverse service requirements.

This paper proposes a generalized beamspace modulation using multiplexing (GBMM) scheme for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications with reduced radio frequency (RF) chains. Besides achieving a multiplexing gain over the selected beamspace set, GBMM additionally makes use of the index of the beamspace set to carry information. In the proposed GBMM, the beamspace sets are non-uniformly activated. We investigate the spectral efficiency (SE) of the proposed GBMM and the SE-oriented beamspace set activation probability optimization as well as the hybrid precoder design. In the hybrid precoder design procedure, we first design the fully-digital precoders and then adopt the optimized fully-digital precoders to design the hybrid precoders. A gradient ascent algorithm is developed to find the optimal fully-digital precoders and precoder activation probabilities. In the high signal-to-noise-ratio (SNR) regime, closed-form solutions of the fully-digital precoders and the precoder activation probabilities are derived. Moreover, we investigate the impact of the hybrid receiver structure on the performance of GBMM, propose a coding method to realize the optimized precoder activation, and discuss the extension to orthogonal frequency division multiplexing (OFDM)-based mmWave broadband communications. Both analytical and numerical results show that GBMM outperforms the spatial multiplexing over the best beamspace set in terms of SE, which has been well recognized as the best transmission solution in mmWave MIMO communications.

Machine (deep) learning-enabled accurate traffic modeling and prediction is an indispensable part for future big data-driven intelligent cellular networks, since it can help autonomic network control and management as well as service provisioning. Along this line, this paper proposes a novel deep learning architecture, namely Spatial–Temporal Cross-domain neural Network (STCNet), to effectively capture the complex patterns hidden in cellular data. By adopting a convolutional long short-term memory network as its subcomponent, STCNet has a strong ability in modeling spatial–temporal dependencies. Besides, three kinds of cross-domain datasets are actively collected and modeled by STCNet to capture the external factors that affect traffic generation. As diversity and similarity coexist among cellular traffic from different city functional zones, a clustering algorithm is put forward to segment city areas into different groups, and consequently, a successive inter-cluster transfer learning strategy is designed to enhance knowledge reuse. In addition, the knowledge transferring among different kinds of cellular traffic is also explored with the proposed STCNet model. The effectiveness of STCNet is validated through real-world cellular traffic datasets using three kinds of evaluation metrics. The experimental results demonstrate that STCNet outperforms the state-of-the-art algorithms. In particular, the transfer learning based on STCNet brings about 4%~13% extra performance improvements.

This paper investigates the generic signal shaping methods for the multiple-data-stream generalized spatial modulation (GenSM) and the generalized quadrature spatial modulation (GenQSM). Three cases with different channel state information at the transmitter (CSIT) are considered, including no CSIT, statistical CSIT, and perfect CSIT. A unified optimization problem is formulated to find the optimal transmit vector set under size, power, and sparsity constraints. We propose an optimization-based signal shaping (OBSS) approach by solving the formulated problem directly and a codebook-based signal shaping (CBSS) approach by finding the sub-optimal solutions in discrete space. In the OBSS approach, we reformulate the original problem to optimize the signal constellations used for each transmit antenna combination (TAC). Both the size and the entry of all signal constellations are optimized. Specifically, we suggest the use of a recursive design for the size optimization. The entry optimization is formulated as a non-convex large-scale quadratically constrained quadratic programming (QCQP) problem and can be solved by the existing optimization techniques with rather high complexity. To reduce the complexity, we propose the CBSS approach using a codebook generated by the quadrature amplitude modulation (QAM) symbols and a low-complexity selection algorithm to choose the optimal transmit vector set. The simulation results show that the OBSS approach exhibits the optimal performance in comparison with existing benchmarks. However, the OBSS approach is impractical for large-size signal shaping and adaptive signal shaping with instantaneous CSIT due to the demand of high computational complexity. As a low-complexity approach, the CBSS shows comparable performance and can be easily implemented in large-size systems.

In this paper, a novel 3-dimensional (3-D) constellation design is proposed for spatial modulation (SM) multiple-antenna system (MIMO) employing maximum ratio combing (MRC) detector. The 3-D constellation is established for spatial modulated systems to minimize the system symbol error rate (SER) by jointly exploiting the potential of both antenna and signal domains. A low-complexity and efficient design algorithm is proposed to find out the 3-D constellation sub-optimally. The performance of the jointly mapped SM (JM-SM) with the designed 3-D constellation is investigated. Simulation results show that the proposed JM-SM schemes can bring obvious performance gain compared with the existing adaptive SM systems under the same spectral efficiency.

This paper investigates the downlink transmission of massive multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) systems. The base station (BS) is equipped with large scale antenna array to provide users with concurrent information and energy supplies. Considering the short communication range between users and the BS in SWIPT systems, the transmission channels are modeled as Rician fading channels to capture both the line-of-sight (LOS) and non-LOS propagations. The approximate and asymptotic expressions of the achievable rate are first derived, and a sum achievable rate optimization problem is formulated based on the asymptotic expression subject to the quality-of-service (QoS) and transmit power constraints. An iterative optimization framework is proposed to solve the original non-linear non-convex problem, and iterative successive convex approximation (SCA) method is introduced in the framework to transmit the non-convex subproblem into convex form. The convergence and effectiveness of the proposed framework are analyzed and proved through analysis and intensive simulations. Results show that the proposed framework can achieve the optimal system performance as the exhaustive search method does.

This paper considers wireless information surveillance systems, in which one suspicious communication link is intercepted by a legitimate monitor to prevent possible criminal or terrorist attacks from the link. To enhance the information surveillance performance, a novel proactive eavesdropping mechanism is proposed with the help of a cooperative relay. It is assumed that the relay operates in full-duplex (FD) mode to assist monitor for information relaying and jamming transmission simultaneously. Based on the proposed mechanism, the joint relay beamforming and power allocation scheme is further proposed and the closed form optimal solutions are derived. Simulation results show that the proposed proactive eavesdropping mechanism can achieve better surveillance performance than both proactive eavesdropping schemes without the help of cooperative relay and cooperative surveillance with pure information forwarding. Simulation results also reveal that the proposed power allocation scheme boosts surveillance performance significantly compared with those equal power allocation scheme.

This paper focuses on the secure transmission of wireless-powered relay systems with the imperfect eavesdropper channel state information. For efficient energy transfer and information relaying, a novel two-phase protocol is proposed, in which the relay operates in a full-duplex (FD) mode to achieve the simultaneous wireless power and information transmission. Compared with those existing protocols, the proposed design possesses two main advantages: 1) it fully exploits the available hardware resource (antenna element) of relay and can offer a higher secrecy rate and 2) it enables the self-energy recycling (S-ER) at relay, in which the loopback interference generated by the FD operation is harvested and reused for information relaying. To maximize the worst-case secrecy rate (WCSR) through jointly designing the source and relay beamformers coupled with the power allocation ratio, an optimization problem is formulated. This formulated problem is proved to be non-convex and the challenge to solve it is how to concurrently solve out the beamformers and the power allocation ratio. To cope with this difficulty, an alternative approach is proposed by converting the original problem into three subproblems. By solving these subproblems iteratively, the closed-form solutions of robust beamformers and power allocation ratio for the original problem are achieved. Simulations are done and results reveal that the proposed S-ER -based secure transmission scheme outperforms the traditional time-switching based relaying scheme at a maximum WCSR gain of 80%. Results also demonstrate that the WCSR performance of the scheme reusing all antennas for information reception is much better than that of schemes exploiting only one antenna.

The medium access probability (MAP) of a random access protocol can severely impact network throughput especially for delay-sensitive applications, since it determines whether a node should transmit packets in a given slot or not. This paper focuses on network throughput maximization through optimizing the MAPs of all users under delay quality-of-service constraints in wireless interference-limited networks. Specifically, first, the total delay for transmitting one packet for a user is analyzed and derived based on an M/G/1 model. Then, the stochastic property of the aggregated interference is analyzed and its distribution is modeled as a log-normal distribution. Based on the delay and interference models, an optimization problem is formulated to derive the optimal MAPs so that the network throughput is maximized under the delay constraints. Two network traffic scenarios, homogeneous and heterogeneous user traffic, are discussed, respectively. For the case of homogeneous traffic, a closed-form expression of the optimal MAP is derived; for the case of heterogeneous traffic, a global E-optimal algorithm based on the branch-and-bound framework and convex relaxation technology is proposed with relatively low complexity. Simulations results show that the proposed algorithms can achieve superior network throughput performance over existing schemes.

Spatial modulation (SM) conveys information bits by utilizing both the antenna index and complex symbols to form a 3-D constellation. Similar to 2-D modulation, the structure of 3-D constellation could greatly affect the transmission reliability. In this paper, a generalized 3-D constellation design is proposed to optimize the constellation diagram used for each antenna, i.e., to optimize the complex symbols and their total number for each antenna and finally to enhance the transmission reliability. The optimal design method with exhaustive search algorithm may cause prohibitive computational complexity, especially when the cardinality of 3-D constellation is large. To overcome this issue, a recursive design algorithm is proposed with a computational complexity increasing polynomially with the cardinality of 3-D constellation. Extensions of the proposed methods to the SM constellation design for massive multiple-input multiple-output transmission, generalized spatial modulation (GSM) constellation design, and the SM constellation design with transmit antenna correlation are also discussed. Simulations are done to validate those theoretical analysis, and results show that the proposed 3-D constellation design is a generalized design scheme and can be adopted in any SM/GSM systems without constraints on the number of transceiver antennas. It is also shown that the proposed approach offers better symbol-error-rate performance than other solutions.

The IEEE 1609.4 standard suggests that the control channel (CCH) should be devoted to the beacon delivery, where the multiple access control (MAC) plays an important role in scheduling the network resources to multiple nodes. This paper proposes a novel OFDM-based group contention MAC (OGCMAC) for vehicular ad hoc networks (VANETs), in which a new CCH architecture, a modified multi-carrier burst contention (MCBC) and a group contention strategy are established. The CCH architecture is proposed to reduce the resource consumption of contention windows (CWs). Based on the proposed architecture, MCBC is modified to adapt to VANETs. The proposed CCH architecture supports parallel contention mechanisms and allows losers to change their target resource blocks (RBs) during the modified MCBC. Furthermore, to accelerate the RB assignment, a group contention strategy is developed by dividing the entire frame into several groups with different sizes. With the objective to maximize the ratio of the RBs allocated free of collisions within a single frame, a greedy group partition algorithm is proposed to determine the appropriate group sizes according to the MCBC capability. Evaluations are done to validate the proposed mechanism. Results show that OGCMAC achieves the highest throughput among all the evaluated MACs, because of its high channel usage rate. RBs are allocated faster by employing the proposed optimal group contention policy. It is also shown that the proposed greedy group partition algorithm can reduce the resource waste by properly adopting the existing CWs without extra resource consumption.

This paper proposes a spatial modulated simultaneous wireless information and power transfer (SM SWIPT) scheme to save multiple radio frequency (RF) chains at the transmitter. As SM is adopted, only one RF chain is equipped at the transmit side. To harvest energy and in the same time to transfer information, the received signal is split into two parts according to the defined power splitting factors. The power splitting factors are determined by maximizing the throughput of the information decoding (ID) receiver under the given energy harvesting (EH) constraint. An iterative power splitting algorithm (ISPA) is developed to solve the maximization problem. Its performance is investigated through simulations. In addition, the computational complexity of the proposed algorithm is analyzed. Results show the superiority of the proposed SM SWIPT in throughput through comparison with single-input multiple-output (SIMO) SWIPT and beamformed MIMO SWIPT. What is more, unlike the conventional beamformed MIMO SWIPT that requires perfect channel state information at the transmitter (CSIT), the proposed scheme is open loop and requires no any CSIT, thus can further enhance the superiority in energy efficiency.

This paper studies the joint transceiver design and power splitting (PS) for a downlink multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. The objective of this work is to minimize transmit power by jointly optimizing the transmitter at a base station (BS), the PS factors and information decoding (ID) receivers at mobile stations (MSs) subject to both the mean-square-error (MSE) and energy harvesting (EH) constraints. To solve the formulated nonconvex optimization problem, a framework is proposed to iteratively solve a joint transmitter and PS factors optimization (JTxPS) sub-problem and a receiver side minimum mean-square error (MMSE) minimization subproblem. The nonconvex JTxPS sub-problem is reformulated as a convex semidefinite programming (SDP) and thus solved. Simulation results show the effectiveness of the proposed scheme.

By providing substantial gains in terms of both spectral and energy-efficiency, Massive MIMO is expected to be the promising enabler for the fifth generation (5G) communications. However the performance of massive MIMO is greatly affected by pilot contamination due to the insufficiency of pilot sequences. To overcome this, we propose a linear programming based pilot allocation with the purpose of alleviating the effect of pilot contamination and maximizing the system throughput. We first formulate the pilot allocation as a user clustering problem, which can be converted to a linear programming one by introducing the integer factor and constraint relaxation. An efficient linear programming algorithm is proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms the other candidates in the presence of pilot contamination.

Cross-layer communication could bring a significant performance improvement for wireless communication systems through allowing information exchanges among multiple layers. For delay-sensitive video application, cross-layer optimization could be more critical by jointly considering the network resource allocation and application performance requirement, especially in an interference-limited distributed network. To improve the quality of video transmission over an ad hoc network, this paper proposes a cross-layer scheme to maximize the average received video quality by jointly considering the application performance and network transmission strategy. In this scheme, important system parameters including the distribution of the node and the resulting interference, link transmission policy, and queueing delay, as well as the video encoding rate are jointly considered to achieve the best received video quality. To this end, a threshold-based transmission strategy and an interference approximation model are developed first. Then, the queueing delay is analyzed based on the M/M/1 queue model. Finally, the problem of maximizing video transmission quality is formulated as a cross-layer optimization problem. To solve this optimization problem, a distributed algorithm is proposed based on the optimization and game theories. The convergence performance of this distributed algorithm is analyzed and shown with relatively lower complexity through simulation. Furthermore, both numerical and simulation results show that the proposed scheme improves the system performance considerably based on the comparison with other two existing schemes.

Physical layer security approaches enable secure message transmission without upper layer data encryption and thus draw intensive attention recently years. Following this topic, this paper proposes a novel approach to improve the security of multiple-input single-output (MISO) communications links in the presence of non-colluding passive Poisson distributed eavesdroppers. In the proposed approach, it is assumed that the channel state information (CSI) of the main channel is known and that of the eavesdropper channel is unknown. Through beamforming vectors, the transmitter transmits information signal to the legitimate receiver along with artificial noise (AN) to confuse the eavesdroppers. Secrecy outage probability (SOP) is adopted to describe the secrecy performance, and based on it, security region (SR) is used from the perspective of space to illustrate the security. In obtaining the SOP of the described transmission link, stochastic geometry theory is adopted. It is shown that the stochastic geometry theory provides a powerful tool in obtaining a solution of SOP. Furthermore, the secrecy performance between transmitting approaches with AN and without AN is compared. The SR is plotted and the factors impacting security are analyzed accordingly.

The secrecy rate optimization of wireless communication systems with full-duplex (FD) relays and jamming signals is investigated in this work. Cooperated with FD relays, a novel secrecy transmission mechanism is proposed targeting at creating interference at eavesdroppers by adopting jamming signals. In the proposed mechanism, relays work in FD mode to receive information signals and forward them together with extra jamming signals. The global channel state information (CSI) is assumed available at all transmit nodes. Based on the proposed scheme, the secrecy rate of relay communication system is analyzed. Simulation results are also included to support the theoretical analysis. Results show that the proposed scheme can obviously enhance the secrecy rate of relay communication systems.

Sums of exponentially distributed random variables (RVs) play important roles in performance analysis of various communication systems. Their logarithmic expectations can not only facilitate capacity analysis but also provide efficient analytical expressions of the system capacity. However, the analytical expressions for the logarithmic expectations have been rarely systematically provided in literature, resulting in inconvenience for related performance analysis. To overcome this issue, in this work, the analytical expressions for the logarithmic expectations of the sums of independent exponential RVs are summarized. Especially, for the case where the sum is composed of both independent non-identically distributed (i.n.i.d.) exponential RVs and independent identically distributed (i.i.d.) exponential RVs, a new closed-form probability density function (PDF) is derived. Compared to the previous PDF expressions, the derived PDF expression is much concise and easy to be determined. To demonstrate the effectiveness of the derived logarithmic expectations, case studies are performed by applying the derived logarithmic expectations to the analysis and derivations of the ergodic capacity of several multiple antenna systems. It is shown that the proposed approach can significantly facilitate the performance evaluation of multiple antenna communication systems.

This paper proposes a power allocation scheme to maximize the sum capacity of all users for signal-to-leakage-and-noise ratio SLNR precoded multiuser multiple-input single-output downlink. The designed scheme tries to explore the effect of the power allocation for the SLNR precoded multiuser multiple-input single-output system on sum capacity performance. This power allocation problem can be formulated as an optimization problem. With high signal-to-interference-plus-noise ratio assumption, it can be converted into a convex optimization problem through the geometric programming and hence can be solved efficiently. Because the assumption of high signal-to-interference-plus-noise ratio cannot be always satisfied in practice, we design a globally optimal solution algorithm based on a combination of branch and bound framework and convex relaxation techniques. Theoretically, the proposed scheme can provide optimal power allocation in sum capacity maximization. Then, we further propose a judgement-decision algorithm to achieve a trade-off between the optimality and computational complexity. The simulation results also show that, with the proposed scheme, the sum capacity of all the users can be improved compared with three existing power allocation schemes. Meanwhile, some meaningful conclusions about the effect of the further power allocation based on the SLNR precoding have been also acquired. The performance improvement of the maximum sum capacity power allocation scheme relates to the transmit antenna number and embodies different variation trends in allusion to the different equipped transmit antenna number as the signal-to-noise ratio SNR changes.Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a novel trellis coded generalized spatial modulation (TCGSM) scheme is presented and analyzed. Similar to that of the traditional generalized spatial modulation (GSM), a subset of the entire transmit antennas is selected for transmission at each time slot. Nevertheless, in the proposed TCGSM scheme, the bits in the spatial domain are first coded by the trellis encoder before antenna selection. The purpose is to combat the correlation of the MIMO channel and hence improve the system performance. We give the detailed system model as well as the trellis encoding/decoding algorithm for the proposed scheme. The performance of the scheme is evaluated through both theoretical analysis and simulations. The results indicate that the proposed scheme is spectral efficient and robust against the channel correlation.

Interference alignment (IA) is a promising interference management technique to efficiently eliminate multiuser interference in a K-user interference network. However, channel state information (CSI) at the transmitter is indispensable for the interference alignment precoding matrices design. In spite of the fact that the CSI can be obtained through feedback from receivers to transmitters in a frequency-division duplex (FDD) system or through the reciprocity in a time-division duplex (TDD) system, it may be imperfect for the reason such as estimation inaccuracy, feedback delay and time-varying of channel. In this paper, the impact of imperfect CSI on the achievable sum rate of interference alignment network is considered and a channel prediction technique based on Kalman filtering is proposed to verify the theoretical analysis. Through analysing, we find that the sum rate performance of an interference aligned network is affected by an integrated parameter, i.e., the product of user number, transmit power and channel error variance. Simulation results reveal that the performance of interference alignment is more sensitive to the uncertainty of CSI at high signal-to-noise ratio (SNR) regime than that at low SNR regime. It is also verified that the performance can be improved through channel prediction, comparing with interference alignment based on the delayed feedback CSI.

In this paper, we proposed a scheme, with which the transmitted signals at the eavesdropper node can be nulled out to avoid intercepting in the case of one eavesdropper. The amplify-and-forward (AF) based cooperative protocol is considered to form the transmission link. Assuming the globe channel state information is available at all the transmission nodes, we simulate the transmission system and show the secrecy rate of the proposed scheme. The simulation results show that the performance of the secrecy capacity of the proposed scheme is better than the bound method and the direct transmission.

This study addresses the issue of intercell interference coordination in a multicell orthogonal frequency division multiple access network with universal frequency reuse, which aims at improving the network spectral efficiency, especially for the cell edge areas. According to different degrees of information sharing within the engaged network, both a joint and a distributed solution are proposed respectively trying to determine the most appropriate power control strategy along with the user scheduling policy. The joint power control and user scheduling scheme with full access to the network information is capable of gaining considerable enhancement on the network capacity and also a performance improvement for users suffering from strong intercell interferences. The distributed scheme with the same purpose manages to adapt the power control and user scheduling strategy on the basis of dynamic programming with respect to both current and future utility of the network with only local information available. Numerical results have shown the advantages of the proposed schemes in achieving better spectral efficiency under both joint and distributed circumstances.

ABSTRACT We consider a cognitive radio system where a secondary network shares the spectrum band with a primary network. Aiming at improving the frequency efficiency of the secondary network, we set a multiantenna relay station in the secondary network to perform two‐way relaying. Three linear processing schemes at the relay station based on zero forcing, zero forcing‐maximum ratio transmission, and minimum mean square error criteria are derived to guarantee the quality of service of primary users and to suppress the intrapair and interpair interference among secondary users (SUs). In addition, the transmit power of SUs is optimized to maximize the sum rate of SUs and to limit the interference brought to PUs. Numerical results show that the proposed multiuser two‐way relay processing schemes and the optimal power control policies can efficiently limit the interference caused by the secondary network to primary users, and the sum rate of SUs can also be greatly improved. Copyright © 2011 John Wiley & Sons, Ltd.

Cognitive radio (CR) has great potential to improve the spectral efficiency of future wireless networks. This paper focuses on maximizing the sum-capacity of cognitive wireless networks based on signal to leakage noise ratio (SLNR) precoding and hybrid opportunistic spectrum access scheme. We propose a globally optimal power allocation scheme based on a combination of the Branch and Bound framework (B&B) and convex relaxation technique to maximize the sum capacity of all secondary users (SUs). Simulation results indicate that, with the proposed power allocation scheme, the sum capacity of the secondary network can be improved compared to conventional SLNR-precoding-based power allocation schemes.

ABSTRACT This paper proposes two power‐efficient resource allocation policies with statistical delay Quality of Service (QoS) guarantees for uplink time‐division multiple access (TDMA) communication links. Specifically, the first policy aims at maximizing the system throughput while fulfilling the delay QoS and average power constraints, and the second policy is devised as an effort to minimize the total average power subject to individual delay QoS constraints. Convex optimization problems associated with the resource allocation policies are formulated based on a cross‐layer framework, where the queue at the data link layer is served by the resource allocation policy. By employing the Lagrangian duality theory and the dual decomposition theory, two subgradient iteration algorithms are developed to obtain the globally optimal solutions. The aforementioned resource allocation policies have been shown to be deterministic functions of delay QoS requirements and channel fading states. Moreover, numerical results are provided to demonstrate the performance of the proposed resource allocation policies. Copyright © 2010 John Wiley & Sons, Ltd.

This paper investigates the power allocation algorithms based on convex theory for multiuser multi-relay amplify-and-forward (AAF) wireless cooperative network. In this system, there are multiple single-antenna users to communicate with one destination with the help of multiple relay nodes at the same time. In order to guarantee the quality-of-service (QoS) for all the users and reduce the power consumption, we propose two relay power allocation strategies, one is to minimize the total relay transmission power and the other is to maximize the minimum signal-to-noise ratio (SNR) among all the users. Simulation results and analysis demonstrate the efficiency and fairness of our proposed strategies.

To improve power efficiency in vehicle-to-roadside infrastructure (V2I) communication networks, this paper proposes a joint power and sub-carrier assignment policy under delay aware quality of service (QoS) requirements. Due to the real-time nature of the V2I transmissions, the proposed policy should satisfy delay aware QoS requirements with a minimized power consumption. In particular, we develop a cross-layer framework in which orthogonal frequency division multiplexing (OFDM) is employed at the physical layer and the proposed power and sub-carrier assignment policy works at the data link layer. Under the assumption that the instantaneous channel state information (CSI) of all users is known, the optimization problem can be formulated and solved with the help of a time-sharing factor. The obtained results show that both the optimal power allocation and the optimal sub-carrier assignment depend on the delay aware QoS requirements of each user. We also theoretically prove that the proposed power allocation policy converges to the classical water-filling policy if we do not consider the QoS requirements. Experimental results reveal that the proposed policy offers a superior performance over the existing resource allocation policies.

Abstract In this paper, we extend the geometrical one‐ring multiple‐input multiple‐output (MIMO) channel model with respect to frequency selectivity. Our approach enables the design of efficient and accurate simulation models for wideband space‐time MIMO channels under isotropic scattering conditions. Two methods will be provided to compute the parameters of the simulation model. Especially, the temporal, frequency and spatial correlation properties of the proposed wideband space‐time MIMO channel simulator are studied analytically. It is shown that any given specified or measured discrete power delay profile (PDP) can be incorporated into the simulation model. The high accuracy of the simulation model is demonstrated by comparing its statistical properties with those of the underlying reference model with specified correlation properties in the time, frequency and spatial domain. As an application example of the new MIMO frequency‐selective fading channel model, we study the influence of various channel model parameters on the system performance of a space‐time coded orthogonal frequency division multiplexing (OFDM) system. For example, we investigate the influence of the antenna element spacings of the base station (BS) antenna as well as the mobile station (MS) antenna. It turns out that an increasing of the antenna element spacing at the BS side results in a higher diversity gain than an increasing of the antenna element spacing at the MS side. Furthermore, the diversity gain brought in by space‐time block coding schemes is investigated by simulation. Our results show that transmitter diversity can significantly reduce the symbol error rate (SER) of multiple antenna systems. Finally, the influence of the Doppler effect and the impact of imperfect channel state information (CSI) on the system performance is also investigated. Copyright © 2009 John Wiley & Sons, Ltd.

This paper focuses on using the mindset of co-opetition for collaborative multimedia resource allocation. The co-opetition suggests a judicious mixture of competition and cooperation. We present a novel co-opetition strategy based on the Kalai-Smorodinsky bargaining solution (KSBS), and apply it to video rate allocation. The proposed strategy makes satisfied users stop competing for resources such that QoS of unsatisfied users can be improved. Our strategy is evaluated through comparing to existing competition-based strategies. Numerical results indicate that, the co-opetition strategy can result in an improved number of satisfied users. Algorithm with low complexity is also presented.

We investigate the power control policy which minimizes the average transmit power subject to delay quality of service (QoS) constraints for continuous and discrete constellation multi-level quadrature amplitude modulation (MQAM) schemes. The problem is formulated based on a cross-layer framework. In the framework, the queue at the data link layer is serviced by the power control and modulation schemes at the physical layer, using the theory of effective capacity. The solution for the continuous constellation MQAM case is derived by solving the Karush-Kuhn-Tucker (KKT) optimality conditions. For the discrete constellation MQAM case, we propose the region boundaries related to the required delay QoS. Our analysis provides the fundamental power limits for the fading channels while fulfilling the delay QoS requirement. Furthermore, the numerical results are presented to show the power saving performance of the proposed power control schemes.

This paper presents a novel coopetition paradigm based on bargaining theory for collaborative multimedia resource management. The paradigm consists of a judicious mixture of competition and cooperation. For competition, the well-known Kalai-Smorodinsky Bargaining Solution (KSBS) is adopted as the fairness criteria, and for cooperation each user stops competing for resources as long as it achieves a predefined threshold of Quality o Service (QoS). We apply the propose paradigm to rate allocation amongst multiple video users and compare its performance to other two schemes, traditional KSBS, and generalized KSBS for similar video quality. Results indicate that our paradigm adapts the best to the variation of resources as well as the user number. Also, importantly, our paradigm can result in an improved number of satisfied users while simultaneously avoid penalizing same users in the case of scarce resources. Complexity of the proposed paradigm is also analyzed.

We present a model for multiuser multi-input multi-output (MIMO) high speed downlink packet access (HSDPA) with covariance based linear precoders. The linear procoders, which are designed for single stream detection with rake receivers, are independent of the equalizers employed in multiple antenna receivers to support multistream detection. These symbol based equalizers are designed to minimize the mean square error (MSE) of the signal estimates. Both theoretical analysis and numerical results show the applicability of the proposed approach in HSDPA. There is no limitation on the number of users as long as enough code channels are available. Also, all the users have free choice on how many antennas they can deploy. This approach extends the covariance based precoding concept from MISO to MIMO systems.

Abstract This paper studies the peak‐to‐average power ratios (PAPRs) in multicarrier modulation (MCM) systems with seven different orthogonal bases, one Fourier base and six wavelet bases. It is shown by simulation results that the PAPRs of the Fourier‐based MCM system are lower than those of all wavelet‐based MCM (WMCM) systems. A novel threshold‐based PAPR reduction method is then proposed to reduce the PAPRs in WMCM systems. Both numerical and simulation results indicate that the proposed PAPR reduction method works very effectively in WMCM systems. Copyright © 2006 John Wiley & Sons, Ltd.

A novel peak to average power ratio (PAPR) reduction method, threshold method, for wavelet based multicarrier modulation (WMCM) systems is proposed and presented in this paper. Theory analysis and simulation results both prove that our proposed PAPR reduction method to be a feasible and efficient one for multicarrier modulation system (MCM)

In this paper performance of different basis in multicarrier modulation (MCM) system over time-variant channels are analyzed and filter bank generated by different orthogonal Daubechies wavelets and bi-orthogonal wavelets are tested and compared. Due to the high spectral containment of wavelet filters, the wavelet packet MCM (WP-MCM) system performs better than Fourier based MCM system over time-variant channels. With turbo code applied in WP-MCM system, the BER performance improves greatly over time-variant channels with few iterations in the regions of low signal to noise ratio.

Unequal error protection (UEP) is applied in image transmission with the nonuniform signal constellation design over multilevel coded wavelet packet multicarrier modulation (MLC-WP-MCM) system. Different data division schemes are analyzed and compared. Simulation results show that image transmission by UEP with bit-based data division scheme presents much higher PSNR values and surprisingly better image quality in our proposed system

Based on different partitioning rules for MQAM constellations. Nonstandard MLC/MQAM schemes had been proposed to reduce the calculation of the individual capacity. In this paper, performance of the proposed MLC/MQAM schemes in well-defined OFDM systems are discussed with punctured convolutional codes as component codes. Compared with traditional MLC/MQAM and MQAM without MLC in OFDM systems, simulation results show that the nonstandard MLC/MQAM schemes can get better performance and lower complexity.

In this paper, the influences of iterations and the length of the interleaver on the performance of turbo codes in regions of low signal to noise ratio over Rayleigh fading channels are studied on the wavelet based orthogonal frequency division multiplexing (WOFDM). The results show that to improve the performance of turbo codes in WOFDM systems you have two ways to choose: increase the iteration number or enlarge the length of your interleaver. But, both the ways can bring side effects to your systems, it is also the case on coded OFDM systems. According to the demand of concrete system there must be a trade-off.

Abstract For the narrow band Wireless Code Division Multiple Access (WCDMA) system, there are some channel coding schemes proposed and applied like Turbo code and convolutional codes. But for the 4G Code Division Multiple Access (CDMA) wideband systems, we have to use new channel coding schemes with high bandwidth efficiency. In this case, multilevel coding (MLC) scheme is easy to map to Multiple Quardrature Amplitude Modulation (MQAM) modulation strategy to be used for 4G, and MLC+MQAM will be a potential channel coding scheme for the error correcting of next generation of mobile systems. A novel criterion, that is ‘capacity rule’ plus ‘mapping rule’, for the design of the optimum MLC scheme for radio systems over Rayleigh fading channels is proposed in this paper. Based on this theory, a few of key issues related to design an optimum MLC system are investigated. These include a novel optimum design criterion proposed, different mapping strategies, different decoding methods of MLC/MSD and MLC/Parallel Decoding on Levels (PDL) and their performance comparison over Additive White Gaussian Noise (AWGN) and Rayleigh fading channels respectively. Copyright © 2003 John Wiley & Sons, Ltd.