Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environments. This paper introduces MARBLE-Net (Multipath-Aware Rainbow Beam Learning Network), a deep learning framework that jointly optimizes the analog beamforming parameters of a frequency-dependent rainbow beam and a neural localization network for high-accuracy position estimation. By treating the phase-shifter (PS) and true-time-delay (TTD) parameters as learnable weights, the system adaptively refines its sensing beam to exploit environment-specific multipath characteristics. A structured multi-stage training strategy is proposed to ensure stable convergence and effective end-to-end optimization. Simulation results show that MARBLE-Net outperforms both a fixed-beam deep learning baseline (RaiNet) and a traditional k-nearest neighbors (k-NN) method, reducing localization error by more than 50\% in a multipath-rich scene. Moreover, the results reveal a nuanced interaction with multipath propagation: while confined uni-directional multipath degrades accuracy, structured and directional multipath can be effectively exploited to achieve performance surpassing even line-of-sight (LoS) conditions.

Learning-based joint source-channel coding (JSCC) is widely used in task-oriented communication, which aims to extract and transmit only task-relevant information to improve communication efficiency. However, the learning-empowered algorithms in task-oriented communication may lead to information leakage on sensitive attributes and cause discrimination towards specific groups, resulting in fairness issues in social equity. Meanwhile, directly adopting fair representation learning techniques in the source encoder of communication systems poses significant challenges: First, the favorable fairness-utility tradeoff in the encoded feature representations would be deteriorated by channel noise and dynamic variations. Second, the inherent separation of source and channel design precludes the efficiency offered by JSCC for end-to-end transmission. To address these issues, we propose a task-oriented JSCC communication scheme, namely Fair-RIB, that achieves efficient encoding and inference while preserving group fairness. Our approach leverages an information bottleneck-based framework that maximizes the task utility information while limiting the sensitive information leakage to ensure fairness, and adopts a hypernetwork-parametrization mechanism to adapt to varying channel conditions. We also provide theoretical bounds for fairness guarantees by fully exploiting the characteristics of the channel noise, and introduce a selective noise injection mechanism to better manage the fairness-utility tradeoff. To overcome the intractability of the high-dimensional mutual information terms, we adopt variational approximations to derive a tractable upper bound for objective optimization. Experiments on benchmark tabular and image datasets demonstrate the superiority of our framework in achieving a fairness-utility tradeoff and the adaptability to channel variations.

Since decades ago, multi-antenna has become a key enabling technology in the evolution of wireless communication systems. In contrast to conventional multi-antenna systems that contain antennas at fixed positions, position-flexible antenna systems have been proposed to fully utilize the spatial variation of wireless channels. In this paper, fluid antenna systems (FAS) are analyzed by channel measurement, channel modeling, and performance evaluation. First, we fill the gap in experimental analysis on terahertz (THz) FAS by developing a broadband channel measurement with physical FAS operating in the THz band, for which the extremely high movable resolution reaches 0.02 mm and the temporal resolution is 16.7 ps. Channel measurement is conducted for a two-dimensional position-flexible antenna system across 32×32 planar port positions at 300 GHz. Then, in light of the measurement results, spatial-correlated channel models for the two-dimensional FAS are proposed, which is statistically parameterized by the complex covariance matrix extracted from the measurement. Furthermore, by applying either the signal-to-interference-and-noise ratio (SINR)-maximized position selection algorithm or the movable array scheme, FAS are verified to achieve 99% of the optimal performance in terms of spectral efficiency. Finally, the performance of different FAS types is evaluated and compared for both planar and linear FAS. Extensive results demonstrate the advantage of FAS over fixed-position antennas in coping with the multi-path fading and improving the spectral efficiency by over 10% in a 300 GHz measured channel.

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

This paper investigates distributed source-channel coding for correlated image semantic communication, where different but correlated image sources from distributed transmitters are separately encoded and transmitted through dedicated wireless channels to a common receiver. We propose a novel distributed nonlinear transform coding (NTC)-based approach to explicitly model source correlation from both probabilistic and geometric perspectives. The probabilistic perspective is achieved by a joint entropy model that approximates the joint distribution of latent representations and guides adaptive rate allocation, while the geometric perspective is achieved by a feature alignment module that aligns latent features for maximal correlation learning at the decoder.We implement this distributed NTC framework for both source coding only, referred to as D-NTSC, and joint source-channel coding (JSCC), referred to as D-NTSCC. Variational inference is employed to derive principled loss functions that jointly optimize encoding, decoding, and joint entropy modeling. Extensive experiments on real-world multi-view datasets demonstrate that D-NTSC and D-NTSCC outperform existing distributed source coding and distributed JSCC baselines, respectively, achieving state-of-the-art performance in both pixel-level and perceptual quality metrics.

Phase-time arrays (PTAs), integrating phase shifters and true-time delays, emerge as a cost-effective and energy-efficient architecture for frequency-dependent beamforming in wideband communications. In this work, we investigate a wideband system in which a base station equipped with a PTA and single RF chain serves multiple near-field and far-field users. The goal is to jointly optimize PTA-based beamforming, subband allocation, and power allocation to maximize overall system performance. To this end, we formulate a system utility maximization problem, which includes sum-rate and geometric mean rate maximization as special cases and is highly non-convex. We first develop a three-step alternating optimization (AO) algorithm that iteratively optimizes the beamforming and resource allocations. To further enhance efficiency, we propose an unsupervised learning-based approach that combines a convolutional neural network, a graph attention network (GAT), and a normalization module with a utility-driven loss and a learnable adjacency initialized from hardware couplings. Simulation results confirm that PTAs strike a superior balance between energy efficiency and spectral efficiency compared with fully-digital and phased array architectures. The proposed GAT achieves AO-level performance with orders-of-magnitude lower computational complexity, i.e., only about 0.1% in our simulations.

Maritime channel modeling is crucial for designing robust nearshore communication systems, yet reliable models that account for the dynamic marine environment with varying sea waves, wind conditions, and vessel motions remain scarce. This article investigates land-to-ship maritime wireless channel characteristics at 5.8 GHz based upon an extensive measurement campaign, with concurrent hydrological and meteorological information collection. First, a novel large-scale path loss model with physical foundation and high accuracy is proposed for dynamic marine environments. Then, we introduce the concept of sea-wave-induced fixed-point (SWIFT) fading, a peculiar phenomenon in maritime scenarios that captures the impact of sea surface fluctuations on received power. An enhanced two-ray model incorporating vessel rotational motion is propounded to simulate the SWIFT fading, showing good alignment with measured data, particularly for modest antenna movements. Next, the small-scale fading is studied by leveraging a variety of models including the two-wave with diffuse power (TWDP) and asymmetric Laplace distributions, with the latter performing well in most cases, while TWDP better captures bimodal fading in rough seas. Furthermore, maritime channel sparsity is examined via the Gini index and RicianKfactor, and temporal dispersion is characterized. The resulting channel models and parameter characteristics offer valuable insights for maritime wireless system design and deployment.

As a promising 6G enabler beyond conventional bit-level transmission, semantic communication can considerably reduce required bandwidth resources, while its combination with multiple access requires further exploration. This paper proposes a knowledge distillation-driven and diffusion-enhanced (KDD) semantic non-orthogonal multiple access (NOMA), named KDD-SemNOMA, for multi-user uplink wireless image transmission. Specifically, to ensure robust feature transmission across diverse transmission conditions, we firstly develop a ConvNeXt-based deep joint source and channel coding architecture with enhanced adaptive feature module. This module incorporates signal-to-noise ratio and channel state information to dynamically adapt to additive white Gaussian noise and Rayleigh fading channels. Furthermore, to improve image restoration quality without inference overhead, we introduce a two-stage knowledge distillation strategy, i.e., a teacher model, trained on interference-free orthogonal transmission, guides a student model via feature affinity distillation and cross-head prediction distillation. Moreover, a diffusion model-based refinement stage leverages generative priors to transform initial SemNOMA outputs into high-fidelity images with enhanced perceptual quality. Extensive experiments on CIFAR-10 and FFHQ-256 datasets demonstrate superior performance over state-of-the-art methods, delivering satisfactory reconstruction performance even at extremely poor channel conditions. These results highlight the advantages in both pixel-level accuracy and perceptual metrics, effectively mitigating interference and enabling high-quality image recovery.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves great performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results on different devices, including IoT device JETSON AGX ORIN, show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay. We have released our code and pre-trained models at https://github.com/Wireless3C-SJTU/MambaJSCC, allowing full reproduction of our results.

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

Learning-based joint source-channel coding (JSCC) is widely used in task-oriented communication, which aims to extract and transmit only task-relevant information to improve communication efficiency. However, the learning-empowered algorithms in task-oriented communication may bring potential bias towards sensitive groups, and the adaptability to dynamic channel conditions still remains a challenge. To address these issues, we propose a task-oriented communication scheme that achieves efficient encoding and inference while preserving group fairness. Our approach leverages an information bottleneckbased framework that maximizes the task utility information while limiting the dependence of the inference result on the sensitive attribute and adopts a hypernetwork-parametrization mechanism to adapt to varying channel conditions. We also provide a theoretical bound for fairness guarantee and design a noise injection module to control the fairness-utility tradeoff. Experiments on benchmark datasets demonstrate the superiority of our framework in achieving a fairness-utility tradeoff and the adaptability to channel variations.

Score-based diffusion models represent a significant variant within the family of diffusion models and have found extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the ScoreBased Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to better align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions.

Joint phase-time arrays (JPTA) provide a low-cost and energy-efficient solution for enabling flexible frequency-dependent beams in wideband scenarios by incorporating both true-time delays (TTDs) and phase shifters. This paper explores the potential of JPTA with a single radio frequency chain to serve multiple users simultaneously in hybrid near- and far-fields. We concentrate on optimizing subband allocation and JPTA-based hybrid beamforming to maximize the total concave utility function associated with user rates. To achieve this, we propose an unsupervised deep learning (DL) approach. Our DL framework includes a two-layer convolutional neural network for feature extraction, followed by a three-layer graph attention network (GAT) and a normalization module for optimizing resource allocation and beamforming. The GAT effectively captures the interactions between resource allocation and analog beamformers. Simulation results demonstrate the superiority of JPTA over conventional phased arrays in terms of user rate when serving multiple users concurrently. Furthermore, adopting a logarithmic function of user rates as the utility function results in greater fairness than simply maximizing sum rates.

In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., 1.73 dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from 64.89% to 70.31 % for classification.

In future air-to-ground integrated networks, the scattering effects from ground-based scatterers, such as buildings, cannot be neglected in millimeter-wave and higher frequency bands, and have a significant impact on channel characteristics. However, current scattering measurement studies primarily focus on single incident angles within the incident plane, leading to insufficient characterization of scattering properties. In this paper, we present scattering measurements conducted at 28 GHz on various real-world building surfaces with multiple incident angles and three-dimensional (3D) receiving angles. The measured data are analyzed in conjunction with parameterized scattering models in ray tracing and numerical simulations. Results indicate that for millimeter-wave channel modeling near building surfaces, it is crucial to account not only for surface materials but also for the scattering properties of the building surfaces with respect to the incident angle and receiving positions in 3D space.

Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we propose a novel approach, diffusion model-based data synthesis aided FSSL (DDSA-FSSL), which utilizes a diffusion model (DM) to generate synthetic data, bridging the gap between heterogeneous local data distributions and the global data distribution. In DDSA-FSSL, clients address the challenge of the scarcity of labeled data by employing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. This process allows clients to generate more comprehensive synthetic datasets aligned with the global distribution. Extensive experiments conducted on multiple datasets and varying non-IID distributions demonstrate the effectiveness of DDSA-FSSL, e.g., it improves accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

This article considers methods for delivering ultrareliable low-latency communication (URLLC) to enable mission-critical Internet of Things (IoT) services in wireless environments with unknown channel distribution. The methods rely upon the historical channel gain samples of a few locations in a target area. We formulate a nontrivial transmission control adaptation problem across the target area under the URLLC constraints. Then we propose two solutions to solve this problem. The first is a power scaling scheme in conjunction with the deep reinforcement learning (DRL) algorithm with the help of the channel knowledge map (CKM) without retraining, where the CKM employs the spatial correlation of the channel characteristics from the historical channel gain samples. The second solution is model agnostic meta-learning (MAML)-based meta-reinforcement learning algorithm that is trained from the known channel gain samples following distinct channel distributions and can quickly adapt to the new environment within a few steps of gradient update. Simulation results indicate that the DRL-based algorithm can effectively meet the reliability requirement of URLLC under various Quality-of-Service (QoS) constraints. Then the adaptation capabilities of the power scaling scheme and meta-reinforcement learning algorithm are also validated.

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

We consider multi-user semantic communications over broadcast channels. While most existing works consider that each receiver requires either the same or independent semantic information, this paper explores the scenario where the semantic information desired by different receivers is different but correlated. In particular, we investigate semantic communications over Gaussian broadcast channels where the transmitter has a common observable source but the receivers wish to recover hierarchical semantic information in adaptation to their channel conditions. Inspired by the capacity achieving property of superposition coding, we propose a deep learning-based superposition coded modulation (DeepSCM) scheme. Specifically, the hierarchical semantic information is first extracted and encoded into basic and enhanced feature vectors. A linear minimum mean square error (LMMSE) decorrelator is then developed to obtain a refinement from the enhanced features that is uncorrelated with the basic features. Finally, the basic features and their refinement are superposed for broadcasting after probabilistic modulation. Extensive experiments are conducted for two-receiver image semantic broadcasting with coarse and fine classification as hierarchical semantic tasks. DeepSCM outperforms the benchmarking coded-modulation scheme without a superposition structure as well as the classic separate source-channel coding baselines, especially with large channel disparity and high order modulation. It also approaches the performance upperbound as if there were only one receiver.

Terahertz (THz) integrated sensing and communication (ISAC) enables simultaneous data transmission with Terabit-per-second (Tbps) rate and millimeter-level accurate sensing. To realize such a blueprint, ultra-massive antenna arrays with directional beamforming are used to compensate for severe path loss in the THz band. In this paper, the time-frequency-space transmit design is investigated for THz ISAC to generate time-varying scanning sensing beams and stable communication beams. Specifically, with the dynamic array-of-subarray (DAoSA) hybrid beamforming architecture and multi-carrier modulation, two ISAC hybrid precoding algorithms are proposed, namely, a vectorization (VEC) based algorithm that outperforms existing ISAC hybrid precoding methods and a low-complexity sensing codebook assisted (SCA) approach. Meanwhile, coupled with the transmit design, sensing algorithms are proposed to realize high-accuracy sensing, including a target discovery method, a wideband DAoSA MUSIC method for angle estimation and a sum-DFT-GSS approach for range and velocity estimation. Furthermore, to overcome the cyclic prefix limitation and Doppler effects, an inter-symbol interference- and inter-carrier interference-tackled sensing algorithm is developed. Numerical results indicate that the proposed sensing algorithms can realize centi-degree-level angle estimation accuracy and millimeter-level range estimation accuracy, which are one or two orders of magnitudes higher than existing methods in the millimeter-wave band.

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

The immersive nature of the metaverse presents significant challenges for wireless multi-user interactive virtual reality (VR), such as ultra-low latency, high throughput and intensive computing, which place substantial demands on the wireless bandwidth and rendering resources of mobile edge computing (MEC). In this paper, we propose a wireless multi-user interactive VR with edge-device collaborative computing framework to overcome the motion-to-photon (MTP) threshold bottleneck. Specifically, we model the serial-parallel task execution in queues within a foreground and background separation architecture. The rendering indices of background tiles within the prediction window are determined, and both the foreground and selected background tiles are loaded into respective processing queues based on the rendering locations. To minimize the age of sensor information and the power consumption of mobile devices, we optimize rendering decisions and MEC resource allocation subject to the MTP constraint. To address this optimization problem, we design a safe reinforcement learning (RL) algorithm, active queue management-constrained updated projection (AQM-CUP). AQM-CUP constructs an environment suitable for queues, incorporating expired tiles actively discarded in processing buffers into its state and reward system. Experimental results demonstrate that the proposed framework significantly enhances user immersion while reducing device power consumption, and the superiority of the proposed AQM-CUP algorithm over conventional methods in terms of the training convergence and performance metrics.

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless networks can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, through server-device collaboration at the wireless network edge. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at the edge server of the base station (BS), while distributing the expert networks across the mobile devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

This paper considers an integrated sensing and communication system where a multi-antenna base station transmits a common signal for joint multi-user communication and extend target (ET) sensing. We first propose a second-order truncated Fourier series surface model for an arbitrarily-shaped three-dimensional (3D) ET, characterized by center range, center elevation, center azimuth, orientation, and surface coefficients. Based on this model, we consider the backscattering effects of the visible elements along the ET surface, and derive novel closed-form Cramér-Rao bounds (CRBs) for the ET characteristic parameter estimation. Further, we formulate a CRB minimization problem by optimizing the transmit beamformers, under the constraints of transmit power budget, communication-specific signal-to-interference-plus-noise requirements, and ET-specific beam coverage requirement. The non-convex optimization problem can be efficiently solved by the semidefinite relaxation technique. Numerical results demonstrate that the proposed beamforming design is superior to existing baselines with significantly lower CRBs and a more appropriate beampattern for sensing a 3D ET.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). In particular, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.

Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog fashion. In this work, we propose a joint coding-modulation (JCM) framework for digital semantic communications by using variational autoencoder (VAE). Our approach learns the transition probability from source data to discrete constellation symbols, thereby avoiding the non-differentiability problem of digital modulation. Meanwhile, by jointly designing the coding and modulation process together, we can match the obtained modulation strategy with the operating channel condition. We also derive a matching loss function with information-theoretic meaning for end-to-end training. Experiments on image semantic communication validate the superiority of our proposed JCM framework over the state-of-the-art quantization-based digital semantic coding-modulation methods across a wide range of channel conditions, transmission rates, and modulation orders. Furthermore, its performance gap to analog semantic communication reduces as the modulation order increases while enjoying the hardware implementation convenience.

Decentralized learning is widely employed for collaboratively training models using distributed data over wireless networks. Existing decentralized learning methods primarily focus on training single-modal networks. For the decentralized multi-modal learning (DMML), the modality heterogeneity and the non-independent and non-identically distributed (non-IID) data across devices make it difficult for the training model to capture the correlated features across different modalities. Moreover, modality competition can result in training imbalance among different modalities, which can significantly impact the performance of DMML. To improve the training performance in the presence of non-IID data and modality heterogeneity, we propose a novel DMML with knowledge distillation (DMMLKD) framework, which decomposes the extracted feature into the modality-common and the modality-specific components. In the proposed DMML-KD, a generator is applied to learn the global conditional distribution of the modality-common features, thereby guiding the modality-common features of different devices towards the same distribution. Meanwhile, we propose to decrease the number of local iterations for the modalities with fast training speed in DMML-KD to address the imbalanced training. We design a balance metric based on the parameter variation to evaluate the training speed of different modalities in DMML-KD. Using this metric, we optimize the number of local iterations for different modalities on each device under the constraint of remaining energy on devices. Experimental results demonstrate that the proposed DMML-KD with training balance can effectively improve the training performance of DMML.

Integrated sensing and communication (ISAC) has attracted growing interests for enabling the future 6G wireless networks, due to its capability of sharing spectrum and hardware resources between communication and sensing systems. However, existing works on ISAC usually need to modify the communication protocol to cater for the new sensing performance requirement, which may be difficult to implement in practice. In this paper, we study a semi-passive intelligent reflecting surface (IRS) aided millimeter-wave (mmWave) ISAC system by exploiting the established beam scanning operation for simultaneous mmWave communications and sensing. First, we propose a two-phase ISAC protocol, consisting of beam scanning and data transmission. Specifically, in the beam scanning phase, the semi-passive IRS finds the optimal beam for reflecting signals from the base station to a communication user via its passive elements and, meanwhile, directly estimates the angle of a nearby target based on echo signals from the target using its active sensing elements. In the data transmission phase, the sensing accuracy is further improved by leveraging the data signals via possible IRS beam splitting. Next, we derive the achievable rate of the communication user as well as the Cramér-Rao bound and the approximate mean square error of the target angle estimation. Finally, extensive simulation results are provided to verify our analysis as well as the effectiveness of the proposed scheme.

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.

This paper studies an integrated sensing and communication (ISAC) system, where a multi-antenna base station transmits beamformed signals for joint downlink multi-user communication and radar sensing of an extended target (ET). By considering echo signals as reflections from valid elements on the ET contour, a set of novel Cramér-Rao bounds (CRBs) is derived for parameter estimation of the ET, including central range, direction, and orientation. The ISAC transmit beamforming design is then formulated as an optimization problem, aiming to minimize the CRB associated with radar sensing, while satisfying a minimum signal-to-interference-pulse-noise ratio requirement for each communication user, along with a 3-dB beam coverage constraint tailored for the ET. To solve this non-convex problem, we utilize semidefinite relaxation (SDR) and propose a rank-one solution extraction scheme for non-tight relaxation circumstances. To reduce the computation complexity, we further employ an efficient zero-forcing (ZF) based beamforming design, where the sensing task is performed in the null space of communication channels. Numerical results validate the effectiveness of the obtained CRB, revealing the diverse features of CRB for differently shaped ETs. The proposed SDR beamforming design outperforms benchmark designs with lower estimation error and CRB, while the ZF beamforming design greatly improves computation efficiency with minor sensing performance loss.

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models and theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.

Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss. To tackle the challenging problem, we propose a novel deep learning-based hierarchical beam alignment method for both multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems, which learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine search manner. Specifically, a hierarchical beam alignment network (HBAN) is developed for MISO systems, which first performs coarse channel measurement using a tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The propounded HBAN is trained in two steps: the tier-1 PC and the tier-2 PC selector are first trained jointly, followed by the joint training of all the tier-2 PCs and beam predictors. Furthermore, an HBAN for MIMO systems is proposed to directly predict the optimal beam pair without performing beam alignment individually at the transmitter and receiver. Numerical results demonstrate that the proposed HBANs are superior to the state-of-the-art methods in both alignment accuracy and signaling overhead reduction.

In this paper, we consider a cooperative communication network where multiple low-Earth-orbit (LEO) satellites provide services to multiple ground users (GUs) cooperatively at the same time and on the same frequency. The multi-satellite cooperation has great potential in extending communication coverage and increasing spectral efficiency. Considering that the on-board radio-frequency circuit resources and computation resources on each satellite are restricted, we aim to propose a low-complexity yet efficient multi-satellite cooperative transmission framework. Specifically, we first propose a hybrid beamforming method consisting of analog beamforming for beam alignment and digital beamforming for interference mitigation. Then, to establish appropriate connections between the satellites and GUs, we propose a heuristic user scheduling algorithm which determines the connections according to the total spectral efficiency increment of the multi-satellite cooperative network. Next, considering the intrinsic connection between beamforming and user scheduling, a joint hybrid beamforming and user scheduling (JHU) scheme is proposed to dramatically improve the performance of the multi-satellite cooperative network. In addition to the single-connection scenario, we also consider the multi-connection case using the JHU scheme. Extensive simulations conducted over different LEO satellite constellations and across various GU locations demonstrate the superiority of the proposed schemes in both overall and per-user spectral efficiencies.

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

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

This paper investigates digital semantic communications over broadcast channels, where the transmitter has a common observable source to transmit, but the receivers desire to recover different levels of semantic information in adaptation to their respective channel conditions. Inspired by the capacity-achieving superposition coding structure for degraded broadcast channel, we propose a superposition coded modulation (SCM) scheme for semantic information transmission over a two-receiver Gaussian broadcast channel. In specific, the transmitter consists of two separate neural network (NN)-based semantic encoders, one NN-based linear minimum mean square error (LMMSE) decorrelator, and one superposition-based digital modulator. The SCM scheme enables the receiver with lower signal-to-noise ratio (SNR) to decode basic semantic information while the receiver with larger SNR can decode enhanced semantic information. Experiments are conducted on image transmission with coarse and fine classifications as semantics. Results show that our proposed SCM scheme outperforms the basic coded-modulation scheme without a superposition structure for both receivers, especially when channel conditions of the two receivers differ significantly. It also approaches the performance upperbound as if there were only one receiver.

Diffusion models (DM) can gradually learn to re-move noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for wireless communications in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We design corresponding training and sampling algorithms for the forward diffusion process and the reverse sampling process of CDDM. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC). Experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.

Degraded broadcast channels (DBC) are a typical multiuser communication scenario. There exist classic transmission methods, such as superposition coding with successive interference cancellation, to achieve the DBC capacity region. However, semantic communication method over DBC remains lack of in-depth research. To address this, we design a semantic communications system for wireless image transmission over DBC in this paper. The proposed architecture supports a transmitter extracting semantic features for two users separately, and learns to dynamically fuse these semantic features into a joint latent representation for broadcasting. The key here is to design a flexible image semantic fusion (FISF) module to fuse the semantic features of two users, and to use a multi-layer perceptron (MLP) based neural network to adjust the weights of different user semantic features for flexible adaptability to different users channels. Experiments present the semantic performance region based on the peak signal-to-noise ratio (PSNR) of both users, and show that the proposed system dominates the traditional methods.

The immersive nature of the metaverse poses higher requirements on virtual reality (VR). In multi-user interactive VR, achieving real-time rendering of heterogeneous foreground objects with ultra-low latency is a challenge. In this paper, we propose an edge-device collaborative rendering framework based on the real-time computer graphics (CG) execution. We design a multi-object real-time rendering workflow and obtain the corresponding motion-to-photon (MTP) latency. We joint optimize the rendering positions of foreground objects and the bandwidth resource to minimize the MTP latency. A smooth maximum joint rendering scheme is proposed to address the non-differentiable objective function and convert the non-convex problem into convex subproblems. Numerical results illustrate that the proposed scheme significantly improves the frame rate by compensating the bottleneck term in the MTP latency.

In this paper, we consider a cooperative communication network where multiple satellites provide services for ground users (GUs) (at the same time and on the same frequency). The communication and computational resources on satellites are usually restricted and the satellite-GU link determination affects the communication performance significantly when multiple satellites provide services for multiple GUs in a collaborative manner. Therefore, considering the limitation of the on-board radio-frequency chains, we first propose a hybrid beamforming method consisting of analog beamforming for beam alignment and digital beamforming for interference mitigation. Then, to establish appropriate connections between satellites and GUs, we propose a heuristic user scheduling algorithm which determines the connections according to the total spectral efficiency (SE) increment of the multi-satellite cooperative network. Next, a joint hybrid beamforming and user scheduling scheme is proposed to dramatically improve the performance of the multi-satellite cooperative network. Moreover, simulations are conducted to compare the proposed schemes with representative baselines and analyze the key factors influencing the performance of the multi-satellite cooperative network. It is shown that the proposed joint beamforming and user scheduling approach can provide 47.2% SE improvement on average as compared with its non-joint counterpart.

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

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resource of fog access points (F-APs) and also reduce the communication overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The proposed quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communication overhead effectively. Simulation results show that the performance of our proposed policy outperforms the considered baseline policies.

Coded distributed computing can alleviate the communication load by leveraging the redundant storage and computation resources with coding techniques in distributed computing. In this paper, we study a MapReduce-type distributed computing framework over star topological network, where all the workers exchange information through a common access point. The optimal tradeoff among the normalized number of stored files (storage load), computed intermediate values (computation load), and transmitted bits in the uplink and downlink (communication loads) is characterized. A coded computing scheme is proposed to achieve the Pareto-optimal tradeoff surface, in which the access point only needs to perform simple chain coding between the signals it receives, and information- theoretical bound matching the surface is also provided.

As an edge intelligence algorithm for multi-device collaborative training, federated learning (FL) can protect data privacy but increase the computing load of wireless devices. In contrast, split learning (SL) can reduce the computing load of devices by model splitting and assignment. To take advantage of FL and SL, we propose a hybrid federated split learning (HFSL) framework for wireless networks in this paper, which combines the multi-worker collaborative training of FL and the flexible splitting of SL. To reduce the computational idleness in model splitting, we design a parallel computing scheme for model splitting without label sharing and conduct a theoretical analysis of the impact of the delayed gradient on the convergence. Aiming to obtain the trade-off between the training time and energy consumption, we model the joint optimization problem of splitting decisions, the bandwidth, and computing resources as a multi-objective problem. As such, we propose a predictive generative adversarial network (GAN)-powered multi-objective optimization algorithm to obtain the Pareto front of the problem, which utilizes the discriminator to guide the training of the generator to predict promising solutions. Experimental results demonstrate that the proposed algorithm outperforms the considered baselines in finding Pareto optimal solutions, and the solutions obtained from the proposed HFSL framework can dominate the solution of FL.

With the rapid development of mobile Internet, the demand for multicast is growing rapidly, such as content pushing and video streaming. The multicast service is usually offered to users without interrupting their on-going unicast transmission, and thus the multicast and unicast beamformers needs to be jointly designed, which generally requires perfect channel state information (CSI). However, perfect CSI is usually unavailable due to the channel estimation error. In this paper, we propose a learning based approach to jointly design the multicast and unicast beamformers with imperfect CSI. To learn the beamforming strategy, a new graph neural network (GNN) based architecture named unicast-multicast GNN (UMGNN) is proposed, which only requires the estimated channel. UMGNN can guarantee the permutation invariance/equivalence and model the special property in the multicast transmission, i.e., the multicast rate is determined by the worst user. Moreover, by sharing the parameters across different users, UMGNN exhibits a pretty good scalability to different number of users. Numerical results show that UMGNN outperforms a fully connected neural network and a widely used sampling-based algorithm. To highlight its performance in the multicast transmission, we also show that UMGNN can find the correct worst user that determines the multicast rate.

One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Several model compression methods, such as gradient quantization and sparsification, have been proposed to improve the communication efficiency of model aggregation. However, the information-theoretic minimum communication cost for a given distortion of gradient estimators is still unknown. In this paper, we study the fundamental limit of communication cost of model aggregation in distributed learning from a rate-distortion perspective. By formulating the model aggregation as a vector Gaussian CEO problem, we derive the rate region bound and sum-rate-distortion function for the model aggregation problem, which reveals the minimum communication rate at a particular gradient distortion upper bound. We also analyze the communication cost at each iteration and total communication cost based on the sum-rate-distortion function with the gradient statistics of real-world datasets. It is found that the communication gain by exploiting the correlation between worker nodes is significant for SignSGD, and a high distortion of gradient estimator can achieve low total communication cost in gradient compression.

This paper studies the user activity detection and channel estimation problem in a temporally-correlated massive access system where a very large number of users communicate with a base station sporadically and each user once activated can transmit with a large probability over multiple consecutive frames. We formulate the problem as a dynamic compressed sensing (DCS) problem to exploit both the sparsity and the temporal correlation of user activity. By leveraging the hybrid generalized approximate message passing (HyGAMP) framework, we design a computationally efficient algorithm, HyGAMP-DCS, to solve this problem. In contrast to only exploiting the historical estimations, the proposed algorithm performs bidirectional message passing between the neighboring frames for activity likelihood update to fully exploit the temporally-correlated user activities. Furthermore, we develop an expectation maximization HyGAMP-DCS (EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the estimation procedure when the system statistics are unknown. In particular, we propose to utilize the analysis tool of state evolution to find the appropriate hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate that our proposed algorithms can significantly improve the user activity detection accuracy and reduce the channel estimation error.

Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is capable of generating personalized models for heterogenous clients. Combined with with a meta-learning mechanism, PFL can further improve the convergence performance with few-shot training. However, meta-learning based PFL has two stages of gradient descent in each local training round, therefore posing a more serious challenge in information leakage. In this paper, we propose a differential privacy (DP) based PFL (DP-PFL) framework and analyze its convergence performance. Specifically, we first design a privacy budget allocation scheme for inner and outer update stages based on the Rényi DP composition theory. Then, we develop two convergence bounds for the proposed DP-PFL framework under convex and non-convex loss function assumptions, respectively. Our developed convergence bounds reveal that 1) there is an optimal size of the DP-PFL model that can achieve the best convergence performance for a given privacy level, and 2) there is an optimal tradeoff among the number of communication rounds, convergence performance and privacy budget. Evaluations on various real-life datasets demonstrate that our theoretical results are consistent with experimental results. The derived theoretical results can guide the design of various DP-PFL algorithms with configurable tradeoff requirements on the convergence performance and privacy levels.

Integrated ultra-massive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for 6G and beyond Terahertz (0.1-10 THz) communications, to effectively bypass the barriers of limited coverage and line-of-sight blockage. However, excessive dimensions of UM-MIMO and IRS enlarge the near-field region, while strong THz channel sparsity in the far-field is detrimental to spatial multiplexing. Moreover, channel estimation (CE) requires recovering the large-scale channel from severely compressed observations due to limited RF-chains. To tackle these challenges, a hybrid spherical- and planar-wave channel model (HSPM) is introduced for the cascaded channel of the integrated system. The spatial multiplexing gains under near-field and far-field regions are analyzed, which are found to be limited by the segmented channel with a lower rank. Furthermore, a compressive sensing-based CE framework is developed, including a sparse channel representation method, a separate-side estimation (SSE) and a dictionary-shrinkage estimation (DSE) algorithms. Numerical results verify the effectiveness of the HSPM, the capacity of which is only$5\times 10^{-4}$bits/s/Hz deviated from that obtained by the ground-truth spherical-wave-model, with 256 elements. While the SSE achieves improved accuracy for CE than benchmark algorithms, the DSE is more attractive in noisy environments, with 1 dB lower normalized-mean-square error than SSE.

Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. Previous works on achievable rate analysis of RIS-assisted communication systems have mainly focused on the rich-scattering environment where Rayleigh and Rician channel models can be applied. This work studies the ergodic achievable rate of RIS-assisted multiple-input multiple-output communication systems in the millimeter-wave band with limited scattering under the Saleh-Valenzuela channel model. Firstly, we derive an upper bound of the ergodic achievable rate by means of majorization theory and Jensen’s inequality. The upper bound shows that the ergodic achievable rate increases logarithmically with the number of antennas at the base station (BS) and user, the number of the reflection units at the RIS, and the eigenvalues of the steering matrices associated with the BS, user and RIS. Then, we aim to maximize the ergodic achievable rate by jointly optimizing the transmit covariance matrix at the BS and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemannian conjugate gradient algorithm. Simulation results validate the effectiveness of the proposed optimization algorithms.

Reconfigurable intelligent surface (RIS) has attracted extensive attention in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of millimeter-wave (mmWave) (including sub-Terahertz) spectrum is a major trend in next-generation wireless communications, and statistical CSI is more practical to obtain in realistic systems. Thus, we consider the ergodic achievable rate of RIS-assisted mmWave multiple-input multiple-output orthogonal frequency division multiplexing communication systems. The widely used Saleh-Valenzuela channel model is adopted to characterize the mmWave channels and only the statistical CSI is available. We first derive the approximations of the ergodic achievable rate by means of the majorization theory and Jensen’s inequality. Then, an alternating optimization based algorithm is proposed to maximize the ergodic achievable rate by jointly designing the transmit covariance matrix at the base station and the reflection coefficients at the RIS. Specifically, the design of the transmit covariance matrix is transformed into a power allocation problem and solved by spatial-frequency water-filling. The reflection coefficients are optimized by the Riemannian conjugate gradient algorithm. Simulation results corroborate the effectiveness of the proposed algorithms.

With the booming development of Internet of things (IoT) devices and machine learning (ML) technique, edge machine learning is emerging to process the enormous sampled data for realizing intelligent applications at the network edge. With limited edge resources, a well-structured neural network and numerous training data are the two main factors that affect the performance of edge machine learning. In this paper, we cooperatively optimize the data collection and the neural architecture to minimize the energy consumption of devices and the error on a specific task. We derive the Rademacher complexity bounds theoretically to evaluate the generalization error of the neural architectures in the search space and then formulate the optimization problem accordingly. Then we develop a scheme to solve the problem that dynamically performs the data collection based on policy gradient reinforcement learning and the parameter-sharing neural architecture search (NAS) algorithm. By this way, the transmission power of each device can be adjusted based on the data quality assessed by the NAS result in each round to effectively collect data. And with the growing high-quality data, the NAS algorithm can gradually find the optimal architecture for the task. Experimental results show that the neural architectures found by the proposed algorithm outperform the existing architectures while saving energy in the device.

We treat the problem of power and rate adaptation for a point-to-point ultra-reliable low latency communication (URLLC) system over parallel fading channels. The model includes a stochastic traffic arrival process and the transmissions are conducted in the finite blocklength (FBL) regime. The problem is formulated as a long term total power minimization problem under reliability, latency, and peak power constraints. We first establish a proactive outdated data dropping queueing model and transform the reliability constraint into a queuing status constraint. Then we train a deep reinforcement learning (DRL) agent to employ Deep Deterministic Policy Gradient (DDPG) in order to allocate the transmit power on each sub-channel and control the decoding error probability to meet the URLLC constraints. Simulation results show that the proposed DDPG-based algorithm can reduce the transmit power consumption by 13%-26% compared to a baseline approach based on effective capacity. Furthermore, the trained network is scalable and robust towards different traffic arrival models, as well as variations of the average arrival rate.

Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.

Terahertz (0.1-10 THz) communications are envisioned as a key technology for 6G ultra-high-speed wireless systems, by offering an ultra-broad bandwidth. Integrated ultramassive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for THz communications, to effectively bypass the barrier of limited coverage and line-of-sight blockage. Two challenges arise. First, the huge dimension of the antenna array in UM-MIMO and IRS in contrast with the sub-millimeter wavelength enlarge the nearfield region. Second, strong channel sparsity in THz channels could be detrimental to spatial multiplexing gain and thereby capacity. In this work, a hybrid spherical- and planar-wave channel model (HSPM) is developed for the cascaded channel of the THz integrated UM-MIMO and IRS system. Furthermore, the spatial multiplexing gain under near-field and far-field cases are analyzed, which is proved to be limited by the segment of the cascaded channel with a lower rank, and can be improved based on the widely-space architecture design of UM-MIMO and IRS. Performance evaluation reveals that the proposed HSPM can accurately capture the propagation features of the THz integrated UM-MIMO and IRS system. Numerically, when the array size is 256, the capacity based on the HSPM is only 3 × 10−4bits/s/Hz less than that obtained by the ground-truth spherical-wave model.

Federated multi-task learning (FMTL) is a promising edge learning framework to fit the data with non-independent and non-identical distribution (non-i.i.d.) by exploiting the correlations of personalized models. In many practical systems, the sensory data distribution in wireless systems is not only heterogeneous but also non-stationary due to the mobility of terminals and the randomness of link connections. The non-stationary heterogeneous data may lead to model divergence and staleness in the training stage and poor accuracy in the inference stage. In this paper, we design an adaptive FMTL framework, which can work in a non-stationary environment. We propose to optimize the model update scheme and cluster splitting scheme in the training stage to accelerate model convergencse when the training data are non-stationary. We further design a low-complexity model selection scheme in both the training and the inference stages to choose the best model for fitting the current data. The proposed framework is validated in two scenarios, linear regression and graph neural network (GNN)-based power control in wireless device-to-device (D2D) networks. Both sets of numerical results demonstrate that the proposed framework can accelerate the model training convergence and reduce the computation complexity while ensuring model accuracy.

Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max–min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV’s trajectory. We develop a concave–convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max–min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement.

Mobile services are becoming more diverse, making them have different demands on communications, computing, and caching (3C) resources in mobile systems. Unlike the traditional work that considers only one type of service, this paper designs a unified framework to characterize the different kinds of services, and jointly optimizes the 3C resources of the base station (BS) and mobile devices to provide differentiated quality of service (QoS) for diverse services. In the proposed framework, we model the task required by the mobile device to be generated at the BS, the mobile device, or both of them, which means the requested tasks are served through different paths, consuming different bandwidth, computing and caching resources. Since diverse services have different QoS, we formulate a multi-objective programming (MOP) to optimize the allocation of the 3C resources for minimizing the total delay while maximizing the number of executed tasks requested by the mobile devices. We transform the MOP problem as a multi-objective Markov decision process (MO-MDP) and design a multi-objective proximal policy optimization (MO-PPO) algorithm to solve the MO-MDP. The proposed MO-PPO first trains two sub-policies separately for the two objectives, and then combines them to search for Pareto dominating solutions. By alternately perform the separate training and the combination, we can finally obtain a set of Pareto optimal solutions and the corresponding Pareto front. Simulation results show that the proposed MO-PPO outperforms traditional methods in finding a higher-quality set of Pareto optimal solutions and can more appropriately allocate 3C resources to different types of services.

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction.

In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs’ energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

Terahertz (0.1-10 THz) wireless communications are expected to meet 100+ Gbps data rates for 6G communications. Being able to combat the distance limitation with reduced hardware complexity, ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybrid dynamic array-of-subarrays (DAoSA) beamforming are a promising technology for THz wireless communications. However, fundamental challenges in THz DAoSA systems include millidegree-level three-dimensional direction-of-arrival (DoA) estimation and millisecond-level beam tracking with reduced pilot overhead. To address these challenges, an off-grid subspace-based DAoSA-MUSIC and a deep convolutional neural network (DCNN) methods are proposed for DoA estimation. Furthermore, by exploiting the temporal correlations of the channel variation, an augmented DAoSA-MUSIC-T and a convolutional long short-term memory (ConvLSTM) solutions are further developed to realize DoA tracking. Extensive simulations and comparisons on the proposed subspace- and deep-learning-based algorithms are conducted. Results show that both DAoSA-MUSIC and DCNN achieve super-resolution DoA estimation and outperform existing solutions, while DCNN performs better than DAoSA-MUSIC at a high signal-to-noise ratio. Moreover, DAoSA-MUSIC-T and ConvLSTM can capture fleeting DoA variation with an accuracy of 0.1° within milliseconds, and reduce 50% pilot overhead. Compared to DAoSA-MUSIC-T, ConvLSTM can tolerate large angle variation and remain robust over a long duration.

We consider a class of non-convex complex modulus constrained problems (CMCPs), which has many important applications in signal processing for wireless communications, including multicast beamforming and multi-input multi-output detection. Due to the non-convex constraints, traditional optimization-based algorithms either obtain sub-optimal solutions or take exponential complexity to reach the optimum. In this paper, we propose a learning based branch-and-bound (LBB) algorithm for solving the considered CMCPs. LBB regards the search procedure of the global optimal argument-cut based branch-and-bound (AC-BB) algorithm as a sequential decision problem in a binary tree and learns the optimal pruning policy via supervised learning. We first propose to apply ensemble learning to train multiple classifiers and then combine them to achieve better performance. To tackle the imbalanced issue, we propose an undersampling-supervised learning method to sample several balanced subsets and train a classifier on each of them. We also show that the computational complexity of LBB is determined by the depth of the binary tree and give its expression in the worst case. Finally, we validate the proposed LBB with two classic problems in wireless communications, i.e., multicast beamforming and MIMO detection. Numerical results show that LBB runs significantly faster than AC-BB while achieving nearly the same optimal performance.

Consider the single-group multicast beamforming problem in wireless systems, where a multi-antenna transmitter has a common message intended to a group of users. The problem is known be non-convex and NP-hard except in very few special cases. Traditional optimization-based algorithms either obtain sub-optimal solutions or take exponential complexity to reach the optimum. In this paper, we propose a learning based branch-and-bound (LBB) algorithm to find a near-optimal solution of the quality-of-service (QoS)-constrained multicast beamforming problem with affordable computational complexity. LBB regards the search procedure of the argument-cut based branch-and-bound (AC-BB) algorithm as a sequential decision problem in a binary tree and learns the optimal pruning policy via supervised learning. We propose to apply ensemble learning to train multiple classifiers and then combine them to achieve better performance. To tackle the imbalanced issue, we propose an undersampling-supervised learning method to sample several balanced subsets and train a classifier on each of them. We also show that the computational complexity of LBB is determined by the depth of the binary tree and give its expression in the worst case. Numerical results show that LBB runs significantly faster than AC-BB while achieving nearly the same optimal performance.

This paper studies the temporal-correlated massive access system where a large number of devices communicate with the base station sporadically and continue transmitting data in the adjacent frames in high probability when being active. By exploiting the sparsity and the temporal correlations of the user activities, the joint user activity detection and channel estimation (JUADCE) problem in multiple consecutive frames can be formulated as a dynamic compressed sensing (DCS) problem. Specifically, we formulate a probabilistic model that accounts the statistics of channels and characterizes the evolutions of the user activities by a steady Markov chain. The hybrid generalized approximate message passing (HyGAMP) framework is leveraged to develop a computationally efficient algorithm named HyGAMP-DCS to solve the JUADCE problem. The HyGAMP-DCS algorithm performs channel estimation in the GAMP part and soft user activity information update in the MP part, then exchanges intrinsic information between these two parts for performance enhancement. Simulation results demonstrate that the proposed algorithm can significantly outperform the conventional DCS-based algorithms and the GAMP algorithm which ignores the temporal correlations.

One of the main focus in federated learning (FL) is the communication efficiency since a large number of participating edge devices send their updates to the edge server at each round of the model training. Existing works reconstruct each model update from edge devices and implicitly assume that the local model updates are independent over edge devices. In FL, however, the model update is an indirect multi-terminal source coding problem, also called as the CEO problem where each edge device cannot observe directly the gradient that is to be reconstructed at the decoder, but is rather provided only with a noisy version. The existing works do not leverage the redundancy in the information transmitted by different edges. This paper studies the rate region for the indirect multiterminal source coding problem in FL. The goal is to obtain the minimum achievable rate at a particular upper bound of gradient variance. We obtain the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derive an explicit formula of the sum-rate-distortion function in the special case where gradient are identical over edge device and dimension. Finally, we analyse communication efficiency of convex Mini-batched SGD and non-convex Minibatched SGD based on the sum-rate-distortion function, respectively.

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under signal-to-interference-plus-noise ratio (SINR) constraints. The problem is highly challenging due to the non-convex SINR constraints as well as the non-convex unit-modulus constraints for both the phase shifts at the RIS and the analog beamforming at the BS. A penalty-based algorithm in conjunction with the manifold optimization technique is proposed to handle the problem, followed by an individual optimization method with much lower complexity. Simulation results show that the proposed algorithm outperforms the state-of-art algorithm. Results also show that the joint optimization of RIS response matrix and BS hybrid beamforming is much superior to individual optimization.

Reconfigurable intelligent surfaces (RISs) are able to provide passive beamforming gain via low-cost reflecting elements and hence improve wireless link quality. This work considers two-way passive beamforming design in RIS-aided frequency division duplexing (FDD) systems where the RIS reflection coefficients are the same for downlink and uplink and should be optimized for both directions simultaneously. We formulate a joint optimization of the transmit/receive beamformers at the base station (BS) and the RIS reflection coefficients. The objective is to maximize the weighted sum of the downlink and uplink rates, where the weighting parameter is adjustable to obtain different achievable downlink-uplink rate pairs. We develop an efficient manifold optimization algorithm to obtain a stationary solution. For comparison, we also introduce two heuristic designs based on one-way optimization, namely, time-sharing and phase-averaging. Simulation results show that the proposed manifold-based two-way optimization design significantly enlarges the achievable downlink-uplink rate region compared with the two heuristic designs. It is also shown that phase-averaging is superior to timesharing when the number of RIS elements is large.

Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Task offloading may suffer long delay since servers at some ENs may be straggling due to random computation time, and wireless channels may experience severe fading and interference. This paper aims to investigate the interplay among upload, computation, and download latencies during the offloading process in the high signal-to-noise ratio regime from an information-theoretic perspective. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for a sufficiently large upload time. By investing more time in uplink transmission, the policy creates data redundancy at the ENs, which can reduce the computation time, by enabling the use of coded computing, as well as the download time via transmitter cooperation. Moreover, the policy allows computation time to be traded for download time. Numerical examples demonstrate that the proposed policy can improve over existing schemes by significantly reducing the end-to-end execution time.

Coded distributed computing (CDC) can alleviate the communication load in distributed computing systems by leveraging coding opportunities via redundant computation. While the optimal computation-communication tradeoff has been well studied for homogeneous systems, it remains largely unknown for heterogeneous systems where workers have different computation capabilities. This paper characterizes the upper and lower bounds of the optimal communication load as two linear programming problems for a general heterogeneous CDC system using the MapReduce framework. Our achievable scheme first designs a parametric data shuffling strategy for any given mapping strategy, and then jointly optimizes the mapping strategy and the data shuffling strategy to obtain the upper bound. The parametric data shuffling strategy allows adjusting the size of the multicast message intended for each worker set, so that it can largely decrease the number of unicast messages and hence increase the communication efficiency. Numerical results show that our achievable communication load is lower than those achieved in existing works. Our lower bound is established by unifying an improved cut-set bound and a peeling method. The obtained upper and lower bounds degenerate to the existing result in homogeneous systems, and coincide with each other when the system is approximately homogeneous or grouped homogeneous.

This paper investigates online decentralized cache strategy design in multi-cell networks without the knowledge of user preference. The goal is to minimize the cumulative transmission delay of multi-cell networks within a finite time interval. Each small base station (SBS) aims to decide on its own cache action autonomously based on its past observations and limited information transmitted from other SBSs without a central controller. To coordinate the cache actions of different SBSs in a decentralized manner, we first propose an ϵ-calibration learning algorithm for each SBS to predict the cache strategy of other SBSs in real-time, which can progressively improve the accuracy in cache action prediction. Then a decentralized multi-agent multi-armed bandit (MAMAB) algorithm is developed for each SBS to decide its own cache strategy based jointly on its past observations and estimated upcoming cache action of other SBSs. This decentralized MAMAB algorithm with ϵ-calibration enables multiple SBSs to converge to a reasonable joint cache action and realize a cooperative cache decision making in a decentralized manner with limited information exchange. Simulation results demonstrate that our proposed decentralized caching algorithm outperforms other decentralized caching algorithms and can rapidly approach towards the centralized caching solutions.

Unmanned aerial vehicle (UAV) has been widely applied in internet-of-things (IoT) scenarios while the security for UAV communications remains a challenging problem due to the broadcast nature of the line-of-sight (LoS) wireless channels. This article investigates the security problems for dual UAV-assisted mobile edge computing (MEC) systems, where one UAV is invoked to help the ground terminal devices (TDs) to compute the offloaded tasks and the other one acts as a jammer to suppress the vicious eavesdroppers. In our framework, minimum secure computing capacity maximization problems are proposed for both the time division multiple access (TDMA) scheme and non-orthogonal multiple access (NOMA) scheme by jointly optimizing the communication resources, computation resources, and UAVs' trajectories. The formulated problems are non-trivial and challenging to be solved due to the highly coupled variables. To tackle these problems, we first transform them into more tractable ones then a block coordinate descent based algorithm and a penalized block coordinate descent based algorithm are proposed to solve the problems for TDMA and NOMA schemes, respectively. Finally, numerical results show that the security computing capacity performance of the systems is enhanced by the proposed algorithms as compared with the benchmarks. Meanwhile, the NOMA scheme is superior to the TDMA scheme for security improvement.

Content popularity prediction plays an important role on proactive content caching. Different to most of the existing works which focus on improving the popularity prediction accuracy, in this article, we consider the content caching oriented popularity prediction through a weighted clustering approach in order to improve the caching performance. We formulate the loss of the cache hit ratio as the system regret to indicate the caching performance, and construct a clustering-based popularity prediction framework for overcoming the user request sparsity with considering the similarity of popularity evolution trends. For depicting the explicit relationship between the caching performance and the popularity prediction accuracy, we derive the popularity prediction error distribution of each content, and design the caching threshold. By extracting the insights in the relationship between the popularity prediction accuracy and the user clustering strategy, we develop a weighted clustering-based popularity prediction algorithm, which takes the caching regret probability of files as the weights. Based on two real-world datasets, the simulation results demonstrate that the proposed popularity prediction scheme achieves better caching performance than the state-of-the-art schemes.

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for computational tasks, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth under the quality of service guarantee. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether a mobile device stores the data before computing or the data after computing, which tasks to be coded cached and which tasks to be computed locally. The optimization problem is shown to be 0–1 non-convex non-smooth programming, and can be decomposed into a computation offloading programming and a coded caching programming. For a computation offloading subproblem, we proposed an algorithm which applies the convergence of the alternating direction method of multipliers (ADMM) under a non-convex programming and the wide application of the concave-convex procedure (CCCP) for difference of convex (DC) programming to obtain a stationary point, and numerical results verify the convergence of the proposed algorithm and its suboptimality. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.

This paper investigates a novel unmanned aerial vehicles (UAVs) secure communication system with the assistance of reconfigurable intelligent surfaces (RISs), where a UAV and a ground user communicate with each other, while an eavesdropper tends to wiretap their information. Due to the limited capacity of UAVs, an RIS is applied to further improve the quality of the secure communication. The time division multiple access (TDMA) protocol is applied for the communications between the UAV and the ground user, namely, the downlink (DL) and the uplink (UL) communications. In particular, the channel state information (CSI) of the eavesdropping channels is assumed to be imperfect. We aim to maximize the average worst-case secrecy rate by the robust joint design of the UAV’s trajectory, RIS’s passive beamforming, and transmit power of the legitimate transmitters. However, it is challenging to solve the joint UL/DL optimization problem due to its non-convexity. Therefore, we develop an efficient algorithm based on the alternating optimization (AO) technique. Specifically, the formulated problem is divided into three sub-problems, and the successive convex approximation (SCA),$\mathcal {S}$-Procedure, and semidefinite relaxation (SDR) are applied to tackle these non-convex sub-problems. Numerical results demonstrate that the proposed algorithm can considerably improve the average secrecy rate compared with the benchmark algorithms, and also confirm the robustness of the proposed algorithm.

With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV’s mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV’s trajectory, based on the Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

Coded caching is effective in leveraging the accumulated storage size in wireless networks by distributing different coded segments of each file in multiple cache nodes. This paper aims to find a wireless coded caching policy to minimize the total discounted network cost, which involves both transmission delay and cache replacement cost, using tools from deep learning. The problem is known to be challenging due to the unknown, time-variant content popularity as well as the continuous, high-dimensional action space. We first propose a clustering based long short-term memory (C-LTSM) approach to predict the number of content requests using historical request information. This approach exploits the correlation of the historical request information between different files through clustering. Based on the predicted results, we then propose a supervised deep deterministic policy gradient (SDDPG) approach. This approach, on one hand, can learn the caching policy in continuous action space by using the actor-critic architecture. On the other hand, it accelerates the learning process by pre-training the actor network based on the solution of an approximate problem that minimizes the per-slot cost. Real-world trace-based numerical results show that the proposed prediction and caching policy using deep learning outperform the considered existing methods.

Federated learning (FL) is a promising technique that enables many edge devices to train a machine learning model collaboratively in wireless networks. By exploiting the superposition nature of wireless waveforms, over-the-air computation (AirComp) can accelerate model aggregation and hence facilitate communication-efficient FL. Due to channel fading, power control is crucial in AirComp. Prior works assume that the signals to be aggregated from each device, i.e., local gradients have identical statistics. In FL, however, gradient statistics vary over both training iterations and feature dimensions, and are unknown in advance. This paper studies the power control problem for over-the-air FL by taking gradient statistics into account. The goal is to minimize the aggregation error by optimizing the transmit power at each device subject to average power constraints. We obtain the optimal policy in closed form when gradient statistics are given. Notably, we show that the optimal transmit power is continuous and monotonically decreases with the squared multivariate coefficient of variation (SMCV) of gradient vectors. We then propose a method to estimate gradient statistics with negligible communication cost. Experimental results demonstrate that the proposed gradient-statistics-aware power control achieves higher test accuracy than the existing schemes for a wide range of scenarios.

This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users.

Federated learning (FL) is a decentralized algorithm that can train a globally shared model without the requirement to send the raw data to a centralized server by user equipments (UEs). Consider the UEs with non-independently and identically distributed (non-IID) data, heterogeneous computational capabilities and wireless channel conditions, FL becomes unproductive over a wireless network. In this paper, we jointly optimize the user scheduling policy and resource allocation to achieve a tradeoff among the fairness of user scheduling, the accuracy of FL, training time, and energy consumption of UEs. The optimization problem is formulated as a Markov Decision Process considering the potential impact of current scheduling on subsequent training and available resources. To solve the problem, a policy network is trained based on an actor-critic deep reinforcement learning framework. Simulation results show that the proposed user scheduling and resource allocation policy reduces the time and energy cost of the training process while improving the freshness of local update and performance on the 20% worst UEs compared with random user selection and resource allocation policy.

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a coded caching with device computing scheme for content computation, e.g., VR rendering, to minimize the average transmission bandwidth. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether the mobile devices cache the input data or the output data, which tasks to be coded cached and which tasks to compute locally. The optimization problem is shown to be 0-1 nonconvex nonsmooth programming which can be decomposed into the computation programming and the coded caching programming. Computation programming can be solved by utilizing the alternating direction method of multipliers (ADMM), and then a low complexity algorithm is proposed to obtain the acceptable solution for the coded cache programming. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.

This paper considers massive connectivity in asynchronous systems, where a large number of devices sporadically send data to the base station (BS) with imperfect synchronization. Grant-free random access is considered and each device is assigned with a unique but not necessarily orthogonal pilot sequence for identification and channel estimation. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. We first adopt a transmission model where a guard interval is inserted between pilot and data in order to eliminate the potential cross pilot-data interference between asynchronous devices. By exploiting the feature of the asynchronous massive connectivity, we formulate a sparse signal recovery problem with hierarchical sparsity on the user activity and the time delays. We propose the Learned approximate message passing (LAMP) network that combines deep learning in the AMP framework to solve the problem. This neural network benefits from parameter learning ability of deep learning and low computation complexity of the AMP algorithm. Simulation results demonstrate that the LAMP network can perform much better than the AMP algorithm with no prior knowledge of the system statistics. Its performance is also insensitive to the maximal delay spread of the asynchronous users.

A multi-cell mobile edge computing network is studied, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix through data uploading, distributed edge computing, and output downloading. Assuming randomly straggling edge servers, this paper investigates the interplay among upload, compute, and download times in high signal-to-noise ratio regimes. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for sufficiently large upload times. By investing more time in uplink transmission, the policy creates data redundancy at the edge nodes to reduce both computation times by coded computing, and download times via transmitter cooperation. Moreover, it allows computing times to be traded for download times.

The freshness of information is an important indicator for critical message exchange in vehicle-to-vehicle (V2V) communications. In this paper, we study a resource allocation problem to minimize the long-term power consumption under age of information (AoI) constraints in the finite blocklength (FBL) regime. Due to high reliability requirement, we consider the AoI violation probability which consists of decoding error probability and queue length violation probability. To ensure a short tail of the AoI distribution, we impose statistical constraints to the queue length utilizing extreme value theory (EVT). Applying Lyapunov optimization technique, the long-term problem is transformed into the drift-plus-penalty problem, which can be solved in each slot via a two-step method. In addition, in order to achieve the optimal power control and decoding error probability, we propose an efficient iterative algorithm and show the convexity of the optimization problem in each step. Simulation results show that our scheme achieves high reliability and short AoI tail compared to the baseline in the FBL regime.

This work investigates the degrees of freedom (DoF) of a downlink cache-aided cellular network where the locations of base stations (BSs) are modeled as a grid topology and users within a grid cell can only communicate with four nearby BSs. We adopt a cache placement method with uncoded prefetching tailored for the network with partial connectivity. According to the overlapped degree of cached contents among BSs, we propose transmission schemes with no BS cooperation, partial BS cooperation, and full BS cooperation, respectively, for different cache sizes. In specific, the common cached contents among BSs are utilized to cancel some undesired signals by interference neutralization while interference alignment is used to coordinate signals of distinct cached contents. Our achievable results indicate that the reciprocal of per-user DoF of the cellular network decreases piecewise linearly with the normalized cache size μ at each BS, and the gain of BS caching is more significant for the small cache region. Under the given cache placement scheme, we also provide an upper bound of per-user DoF and show that our achievable DoF is optimal when μ ∈ [1/2,1], and within an additive gap of 4/39 to the optimum when μ ∈ [1/4, 1/2).

We consider the uplink channel estimation problem in a millimeter wave (mmWave) system with large-scale antenna arrays. Unlike many existing works which estimate the channel assuming that the number of channel paths is known a priori, we address the problem of channel estimation with an unknown number of channel paths. The spatial channel is transformed into the beamspace channel by the discrete Fourier transform (DFT). Based on the sparsity property of the beamspace channel, we propose three algorithms to estimate the number of paths, direction of arrivals (DoAs) and path gains. The first one is the Spectrum Weighted Identification of Signal Sources (SWISS) for the case when the channel statistics are unknown, which introduces a weight vector to amplify the desired signal and suppress the noise. The second one is the Neyman-Pearson criterion based-Detector (NPD) based on the Rician channel model, which adopts the Neyman-Pearson criterion to decide whether there exists a path on each DFT point. In practice, the DoAs are continuously distributed, leading to the power leakage problem. We solve this leakage problem by proposing the combined algorithm with leakage (CAL). Simulation results show that the proposed algorithms perform better than the conventional spatial smoothing.

This paper studies caching in (K + L - 1) × K partially connected wireless linear networks, where each of the K receivers locally communicates with L out of the K +L-1 transmitters, and caches are at all nodes. The goal is to design caching and delivery schemes to reduce the transmission latency, by using normalized delivery time (NDT) as the performance metric. For small transmitter cache size (any L transmitters can collectively store the database just once), we propose a cyclic caching strategy so that each of every L consecutive transmitters caches a distinct part of each file; the delivery strategy exploits coded multicasting and interference alignment by introducing virtual receivers. The obtained NDT is within a multiplicative gap of 2 to the optimum in the entire cache size region, and optimal in certain region. For large transmitter cache size (any L transmitters can collectively store the database for multiple copies), we propose a modified caching strategy so that every bit is repeatedly cached at consecutive transmitters; the delivery strategy exploits self-interference cancellation and interference neutralization. By combining these schemes, the NDT is optimal in a larger region. We also extend our results to linear networks with heterogeneous receiver connectivity and partially connected circular networks.

In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the central and the edge networks, we aim to minimize the system energy consumption used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices, which is a mixed-integer and non-convex optimization problem. Based on the methods such as decomposition approach and successive pseudoconvex approach, a tractable solution is proposed via an iterative algorithm. The simulation results show that our proposed solution can achieve great performance gain over conventional schemes using edge or central cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.

Existing works on task offloading in mobile edge computing (MEC) networks often assume a task is executed once at a single edge node (EN). Downloading the computed result from the EN back to the mobile user may suffer long delay if the downlink channel experiences strong interference or deep fading. This paper exploits the idea of computation replication in MEC networks to speed up the downloading phase. Computation replication allows each user to offload its task to multiple ENs for repetitive execution so as to create multiple copies of the computed result at different ENs which can then enable transmission cooperation and hence reduce the communication latency for result downloading. Yet, computation replication may also increase the communication latency for task uploading, despite the obvious increase in computation load. The main contribution of this work is to characterize asymptotically an order-optimal upload-download communication latency pair for a given computation load in a multi-user multi-server MEC network. Analysis shows when the computation load increases within a certain range, the downloading time decreases in an inversely proportional way if it is binary offloading or decreases linearly if it is partial offloading, both at the expense of linear increase in the uploading time.

In this paper, we present a novel mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing-enabled mobile devices via a shared wireless link. Each task request can be served from local output caching, local computing with input caching, local computing without local caching or MEC downloading, each of which incurs a unique bandwidth requirement of the multicast link. Aiming to minimize the transmission bandwidth, we optimize the joint caching and computing policy at mobile devices subject to latency, caching, power and multicast transmission constraints. The joint policy optimization problem is shown to be NP-hard. To tackle the problem of intractability of priori knowledge of users' request popularity, we approximate the expectation via sampling. When all the output data size is smaller than the input data size, we reformulate the problem as minimization of a monotone submodular function over matroid constraints and obtain the optimal solution via a strongly polynomial algorithm of Schrijver. Otherwise, by leveraging concave convex procedure together with the alternating direction method of multipliers, we propose a low-complexity high-performance algorithm and prove it converges to a local minimum. Furthermore, in homogeneous case, we theoretically reveal how much bandwidth gain can be achieved from computing and caching resources at mobile devices or the multicast transmission. Our results indicate that exploiting the computing and caching resources at mobile devices as well as multicast transmission can provide significant bandwidth savings.

This paper investigates learning-based caching in small-cell networks (SCNs) when user preference is unknown. The goal is to optimize the cache placement in each small base station (SBS) for minimizing the system long-term transmission delay. We model this sequential multi-agent decision making problem in a multi-agent multi-armed bandit (MAMAB) perspective. Rather than estimating user preference first and then optimizing the cache strategy, we propose several MAMAB-based algorithms to directly learn the cache strategy online in both stationary and non-stationary environment. In the stationary environment, we first propose two high-complexity agent-based collaborative MAMAB algorithms with performance guarantee. Then we propose a low-complexity distributed MAMAB which ignores the SBS coordination. To achieve a better balance between SBS coordination gain and computational complexity, we develop an edge-based collaborative MAMAB with the coordination graph edge-based reward assignment method. In the non-stationary environment, we modify the MAMAB-based algorithms proposed in the stationary environment by proposing a practical initialization method and designing new perturbed terms to adapt to the dynamic environment. Simulation results are provided to validate the effectiveness of our proposed algorithms. The effects of different parameters on caching performance are also discussed.

Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size.

In this paper, the synergy of combining the edge and central cloud computing is studied in heterogeneous cellular networks (HetNets). Multi-antenna small base stations (SBSs) equipped with edge cloud servers offer computing services for user equipment (UEs) proximally, whereas a macro base station (MBS) provides central cloud computing services for UEs via wireless multiple-input multiple-output (MIMO) backhaul allocated to their associated SBSs. With task processing latency constraints for UEs, the network energy consumption is minimized through jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices. A mixed integer and non-convex optimization problem is formulated, and a decomposition algorithm is proposed to obtain a tractable solution iteratively. The simulation results confirm that great performance improvement can be achieved compared with the traditional scheme with central cloud computing only.

This paper studies the computation-communication tradeoff in a heterogeneous MapReduce computing system where each distributed node is equipped with different computation capability. We first obtain an achievable communication load for any given computation load and any given function assignment at each node. The proposed file allocation strategy has two steps: first, the input files are partitioned into disjoint batches, each with possibly different size and computed by a distinct node; then, each node computes additional files from its non-computed files according to its redundant computation capability. In the Shuffle phase, coded multicasting opportunities are exploited thanks to the repetitive file allocation among different nodes. Based on this scheme, we further propose the computation-aware and the shuffle-aware function assignments. We prove that, by using proper function assignments, our achievable communication load for any given computation load is within a constant multiplicative gap to the optimum in an equivalent homogeneous system with the same average computation load. Numerical results show that our scheme with shuffle-aware function assignment achieves better computation- communication tradeoff than existing works in some cases.

In this paper, we propose a novel wireless virtual reality scheme by caching resource on head-mounted displays (HMD) and exploiting head movement prediction to improve user experience. We render images for the future request based on the user's head movement prediction at the server-end to reduce the motion-to- photon latency and cache those rendered images on the HMD to form an image pool. We then develop a corresponding algorithm to select and warp the exact image from the image pool for display. Furthermore, a performance metric - warping distance is defined and used to evaluate the image quality of the proposed scheme. Finally, real dataset-driven results show that the proposed scheme is able to provide higher image quality as well as experience consistency compared with existed schemes.

In this paper, we exploit computation replication to reduce the communication time for task offloading in mobile edge computing networks, by introducing a tradeoff between computation load and communication latency defined as a pair of the Normalized Uploading Time (NUlT) and Normalized Downloading Time (NDlT). The key idea of replication is to allow mobile users to offload their tasks to multiple edge nodes for repetitive execution so as to enable the transmission cooperation in computed results downloading, and consequently interference mitigation across users. We develop an achievable communication latency pair at a given computation load, where the NUlT is optimal and the NDlT is within a multiplicative gap of 2 to an information theoretic lower bound. We show that in a certain interval, the NDlT can be traded by the computation load in an inversely proportional function.

This work investigates the degrees of freedom (DoF) of a downlink cache-aided cellular network where the locations of base stations (BSs) are modeled as a regular grid topology and users within a grid cell can only communicate with four nearby BSs. We adopt a new cache placement method with uncoded prefetching tailored for network with partial connectivity. Transmission schemes with no BS cooperation, partial BS cooperation, and full BS cooperation are proposed, respectively, for different cache sizes. In particular, for no BS cooperation, interference alignment is used to coordinate signals of distinct cached contents among BSs. For partial BS cooperation, we utilize overlapping of cached contents to cancel some of the interference and align the rest by interference alignment. For full BS cooperation, zero-forcing precoding is applied to neutralize all the interference. Our results reveal that the reciprocal of per-user DoF of the cellular network decreases piecewise linearly with the cache size and the gain of BS caches is more significant for the small cache region.

Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user’s message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.

Virtual reality (VR) over wireless is emerging as an important use case of 5G networks. Fully-immersive VR experience requires the wireless delivery of huge data at ultra-low latency, thus leading to ultra-high transmission rate requirement for wireless communications. This challenge can be largely addressed by the recent network architecture known as mobile edge computing (MEC) network, which enables caching and computing capabilities at the edge of wireless networks. This paper presents a novel MEC-based mobile VR delivery framework that is able to cache parts of the field of views (FOVs) in advance and compute certain post-processing procedures on demand at the mobile VR device. To minimize the average required transmission rate, we formulate the joint caching and computing optimization problem to determine which FOVs to cache, whether to cache them in 2D or 3D as well as which FOVs to compute at the mobile device under cache size, average power consumption as well as latency constraints. When FOVs are homogeneous, we obtain a closed-form expression for the optimal joint policy which reveals interesting communications-caching-computing tradeoffs. When FOVs are heterogeneous, we obtain a local optima of the problem by transforming it into a linearly constrained indefinite quadratic problem and then applying concave convex procedure. Numerical results demonstrate the proposed mobile VR delivery framework can significantly reduce communication bandwidth while meeting low latency requirement.

Today's mobile data traffic is dominated by content-oriented traffic. Caching popular contents at the network edge can alleviate network congestion and reduce content delivery latency. This paper provides a comprehensive and unified study of caching and delivery techniques in wireless radio access networks (RANs) with caches at all edge nodes (ENs) and user equipments (UEs). Three cache-aided RAN architectures are considered: RANs without fronthaul, with dedicated fronthaul, and with wireless fronthaul. It first reviews in a tutorial nature how caching facilitates interference management in these networks by enabling interference cancelation (IC), zero-forcing (ZF), and interference alignment (IA). Then, two new delivery schemes are presented. One is for RANs with dedicated fronthaul, which considers centralized cache placement at the ENs but both centralized and decentralized placement at the UEs. This scheme combines IA, ZF, and IC together with soft-transfer fronthauling. The other is for RANs with wireless fronthaul, which considers decentralized cache placement at all nodes. It leverages the broadcast nature of wireless fronthaul to fetch not only uncached but also cached contents to boost transmission cooperation among the ENs. The numerical results show that both schemes outperform existing results for a wide range of system parameters, thanks to the various caching gains obtained opportunistically.

Existing polar coding schemes for the two-user interference channel follow the original idea of Han and Kobayashi, in which component messages are encoded independently and then mapped by some deterministic functions (i.e., homogeneous superposition coding). In this paper, we propose a new polar coding scheme for the interference channel based on the heterogeneous superposition coding approach of Chong, Motani, and Garg. We prove that fully joint decoding (the receivers simultaneously decode both senders' common messages and the intended sender's private message) in the Han-Kobayashi strategy can be simplified to two types of partial-joint decoding, which are friendly to polar coding with practical decoding algorithms. The proposed coding scheme requires less auxiliary random variables and no deterministic functions and can be efficiently constructed. Furthermore, we extend this result to interference networks and show that partial-joint decoding is a general method for designing heterogeneous superposition polar coding schemes in interference networks.

Coded caching can create coded multicasting and, thus, significantly accelerates content delivery in broadcast channels with receiver caches. While the original delivery scheme in coded caching multicasts each coded message sequentially, it is not optimal for multiple-input multiple-output (MIMO) broadcast channels. This paper aims to investigate the full spatial multiplexing gain in multi-antenna coded caching by concurrently transmitting all coded messages. In specific, we propose to treat the content delivery as the transmission problem with general message sets where all possible messages are present, each with different length and intended for different user set. We first obtain inner and outer bounds of the degrees of freedom (DoF) region of a K-user (M, N) broadcast channel with general message sets, with M and N being the number of transmit and receive antennas, respectively. Then for any given set of coded messages, we find its minimum normalized delivery time (NDT) by searching the optimal DoF tuple in the DoF regions. The obtained minimum NDT is optimal at antenna configuration M/N ∈ (0, 1]U[K, ∞) and is within a multiplicative gap of M/N to optimum at M/N ∈ (1, K). Our NDT results can be evaluated for any user demand with both centralized and decentralized cache placements.

In traditional cache-enabled small-cell networks (SCNs), a user can suffer strong interference due to content-centric base station association. This may degenerate the advantage of collaborative content caching among multiple small base stations (SBSs), including probabilistic caching and coded caching. In this work, we tackle this issue by deploying multiple antennas at each SBS for interference management. Two types of beamforming are considered. One is matched-filter (MF) to strengthen the effective channel gain of the desired signal, and the other is zero-forcing (ZF) to cancel interference within a selected SBS cooperation group. We apply these two beamforming techniques in both probabilistic caching and coded caching, and conduct performance analysis using stochastic geometry. We obtain exact and approximate compact integral expressions of system performances measured by average fractional offloaded traffic (AFOT) and average ergodic spectral efficiency (AESE). Based on these expressions, we then optimize the caching parameters for AFOT or AESE maximization. For probabilistic caching, optimal caching solutions are obtained. For coded caching, an efficient greedy-based algorithm is proposed. Numerical results show that multiple antennas can boost the advantage of probabilistic caching and coded caching over the traditional most popular caching with the proper use of beamforming.

In-band full-duplex (IBFD) self-backhauling is promising to provide cost-effective and flexible backhaul connectivity in ultra-dense wireless networks. In this paper, we propose a user-centric joint access-backhaul transmission framework for performance optimization in IBFD-enabled self- backhauled cooperative wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by a cluster of small base stations (SBSs) via joint beamforming. In the backhaul link, user-centric multicast transmission is proposed so that the macro base station (MBS) treats each user's data as a multicast message and sends it to its serving SBS cluster. We formulate an optimization problem to maximize the end-to-end weighted sum rate of all users under power constraints through joint design of multicast beamforming in the backhaul link as well as SBS clustering and beamforming in the access link. We first tackle the joint access- backhaul beamforming problem under given SBS clustering by transforming it into a manifold optimization problem, for which a stationary solution is obtained using the Riemannian optimization technique. We then develop a heuristic algorithm to determine the SBS clustering. Simulation results demonstrate the effectiveness of the proposed algorithms.

In mobile edge computing (MEC) systems, mobile devices can offload their computation-intensive tasks to edge servers to save energy and shorten latency. However, the extra latency for downloading the computed results back may degrade the benefits of computation offloading if the downlink channel suffers severe fading and interference. In this work, we exploit computation replication in task offloading to reduce the download latency in multi-user multi-server MEC networks. The main idea is to partition the task generated by each user into multiple subtasks, and offload each subtask to a set of MEC servers via the uplink channel for repeated execution. The duplication of computation results on multiple servers thus enables data-sharing based transmission cooperation to send the results back to users. Next, we adopt an asymptotic total latency that accounts the uploading, computing and results downloading phases as the performance metric to capture the tradeoff between the increased computation load and the reduced communication time. We formulate a linear programming problem to optimize the task partition ratios for minimizing the total latency. We show that there exists an optimal degrees of replication (the number of MEC servers to compute the same subtask) and an associated task partition strategy for optimal latency performance. Our finding reveals great advantage of computation replication for latency reduction in multi-server MEC networks where the output data size of each computation task is non-negligible.

This paper considers a new mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing- enabled mobile devices via a shared wireless link. Each mobile device can pre-store the input or output data of a task and also execute a task locally. We aim to investigate the impact of local caching and computing at mobile devices as well as content-centric multicast transmission on the saving of required bandwidth on the wireless link. To this end, we first formulate a joint caching and computing decision optimization problem to minimize the required transmission bandwidth subject to latency, caching and energy constraints at each mobile device in the general case. The joint policy optimization problem is shown to be NP-hard. Based on equivalent transformation and exact penalization of the problem, a stationary point is obtained via concave convex procedure. In the special case where all the computation tasks are symmetric and user requests are uniform, we obtain the closed- form expressions for the local caching gain, local computing gain, and multicasting gain. Our results indicate that exploiting the computing and caching resources at mobile devices can provide significant bandwidth savings.

Previous works on learning-based caching problems often only focus on a single base station (BS) scenario. For works considering multiple BSs, the coordination among BSs only exists in the cache placement phase after the learning phase that each BS estimates the file popularity independently. In this work, we investigate the cache strategy design problem in small cell networks (SCNs) with multiple small base stations (SBSs) when user preferences are unknown. We model this multi-agent decision making problem in a multi-armed bandit (MAB) perspective. We first tackle this problem with the centralized MAB algorithm and distributed multi-agent MAB (MAMAB) algorithm. For the centralized algorithm, it considers the coordinations among SBSs but the computational complexity grows exponentially with the number of SBSs. For the distributed one, the computational complexity grows linearly with the number of SBSs but the coordinations among SBSs are totally ignored. To take both coordination among SBSs and computational complexity into account, we propose a collaborative MAMAB algorithm to learn the cache strategy directly, rather than learning the user preferences. The simulation results show that the collaborative MAMAB approaches the performance of the greedy algorithm when the user preferences are perfectly known.

This paper investigates the transmission design in fog radio access networks where each edge node (EN) has a local cache and can pre-store contents based on partition-based caching. In the considered partition- based caching, each file is partitioned into multiple subfiles and cached at different ENs. Upon user requests, the ENs can jointly transmit all the subfiles to the target users simultaneously. Each user adopts a successive interference cancellation receiver to decode each desired subfile with certain order. To improve the network performance, we propose a novel cache-aware scheme to determine the decoding order. Furthermore, we formulate a joint beamforming and dynamic EN clustering optimization problem to minimize the weighted sum of transmission power cost and fronthaul cost under the quality-of-service constraint for each user. An efficient algorithm by adopting smooth function approximation and convex-concave procedure is proposed to solve this problem. To exploit the advantages of partition-based caching, we further design a user-aware caching strategy. Numerical results show that the proposed transmission scheme together with the proposed caching strategy can strike a better balance between power consumption and fronthaul consumption than existing schemes.

Caching in multi-cell networks faces a well-known dilemma, i.e., to cache same contents among multiple edge nodes (ENs) to enable transmission cooperation/diversity for higher transmission efficiency, or to cache different contents to enable content diversity for higher cache hit rate. In this work, we introduce a partition-based caching to exploit the tradeoff between transmission diversity and content diversity in a multi-cell edge caching networks with single user only. The performance is characterized by the system average outage probability, which can be viewed as the sum of the cache hit outage probability and cache miss probability. We show that (i) In the low signal-to-noise ratio(SNR) region, the ENs are encouraged to cache more fractions of the most popular files so as to better exploit the transmission diversity for the most popular content; (ii) In the high SNR region, the ENs are encouraged to cache more files with less fractions of each so as to better exploit the content diversity.

Mobile virtual reality (VR) delivery is gaining increasing attention from both industry and academia due to its ability to provide an immersive experience. However, achieving mobile VR delivery requires ultra-high transmission rate, deemed as a first killer application for 5G wireless networks. In this paper, in order to alleviate the traffic burden over wireless networks, we develop an implementation framework for mobile VR delivery by utilizing caching and computing capabilities of mobile VR device. We then jointly optimize the caching and computation offloading policy for minimizing the required average transmission rate under the latency and local average energy consumption constraints. In a symmetric scenario, we obtain the optimal joint policy and the closed-form expression of the minimum average transmission rate. Accordingly, we analyze the tradeoff among communication, computing and caching, and then reveal analytically the fact that the communication overhead can be traded by the computing and caching capabilities of mobile VR device, and also what conditions must be met for it to happen. Finally, we discuss the optimization problem in a heterogeneous scenario, and propose an efficient suboptimal algorithm with low computation complexity, which is shown to achieve good performance in the numerical results.

This paper aims to characterize the synergy of distributed caching and wireless fronthaul in a fog radio access network (Fog-RAN) where all edge nodes (ENs) and user equipments (UEs) have a local cache and store contents independently at random. The network operates in two phases, a file-splitting based decentralized cache placement phase and a fronthaul-aided content delivery phase. We adopt normalized delivery time (NDT) to characterize the asymptotic latency performance with respect to cache size and fronthaul capacity. Both an achievable upper bound and a theoretical lower bound of NDT are obtained, and their multiplicative gap is within 12. In the proposed delivery scheme, we utilize the fronthaul link, by exploiting coded multicasting, to fetch both non-cached and cached contents to boost EN cooperation in the access link. In particular, to fetch contents already cached at ENs, an additional layer of coded multicasting is added on the coded messages desired by UEs in the fronthaul link. Our analysis shows that the proposed delivery scheme can balance the delivery latency between the fronthaul link and access link, and is approximately optimum under decentralized caching.

This paper considers the channel estimation problem in millimeter-wave (mmWave) systems where a single-antenna user communicates with a massive multiple-input multiple-output (MIMO) base station (BS) in the uplink. Unlike many existing works which estimate the channel gain under the assumption that the number of channel paths is given a priori, we address first the problem of path-number identification. By taking the weighted discrete Fourier transform (WDFT) of the received noisy signal, we formulate an optimization problem to determine the optimum combination of DFT components in this weighted spectrum that leads to a time-domain reconstructed signal (the channel vector) that is at the minimum Euclidean distance from the received signal. Our algorithm, called SWISS (Spectrum Weighted Identification of Signal Sources), is an accurate and computationally efficient means for identifying the paths in the channel vector, providing the information needed for BS beamforming. Once the paths are identified, their individual directions-of-arrival (DoAs) and complex fading gains can be obtained easily. Simulation results for the case of no power leakage in the DFT are presented to demonstrate the effectiveness of SWISS.

This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this special issue. We analyze the challenges that caching needs to address today, also considering an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique.

Welcome to the second part of the IEEE JSAC special issue on Caching for Communication Systems and Networks. The goal of this special issue is to present the multiple facets of caching, from information theory to networking and services, and explore the role of memory in communications. This is a very timely topic due to recent technological and theoretical advances summarized in the tutorial paper that appears in the first part of the issue[1].

In this paper, we study the degrees of freedom (DoF) region for the multiple-input multiple-output two-way X relay channel with asymmetric antenna setting. In this channel model, there are two groups of source nodes, each group contains two source nodes, every source node in one group exchanges independent messages with every source node in another group via a common relay node, and each node has a different number of antennas. First, we derive an outer bound of the DoF region by using the cut-set theorem and the genie-message approach. Then, we obtain an inner bound of the DoF region by proposing a new transmission scheme that collectively utilizes antenna deactivation, pairwise signal alignment, cyclic signal alignment, and generalized signal alignment techniques. In the case of symmetric data exchange, our inner bound coincides with the outer bound, and thus, our proposed transmission strategy is optimal. We also obtain the optimal sum DoF for the special case, where the source nodes within a same group are equipped with the same number of antennas. This paper provides new insights for the study of more complicated relay networks.

The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this paper, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and unicast services concurrently in cooperative multi-cell cellular networks with a limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast message first, subtracts it, and then decodes its dedicated unicast message. We formulate a joint multicast and unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-unicast rate region.

In this paper, we study the probabilistic caching for an N-tier wireless heterogeneous network (HetNet) using stochastic geometry. A general and tractable expression of the successful delivery probability (SDP) is first derived. We then optimize the caching probabilities for maximizing the SDP in the high signal-to-noise ratio regime. The problem is proved to be convex and solved efficiently. We next establish an interesting connection between N-tier HetNets and single-tier networks. Unlike the single-tier network where the optimal performance only depends on the cache size, the optimal performance of N-tier HetNets depends also on the base station (BS) densities. The performance upper bound is, however, determined by an equivalent single-tier network. We further show that with uniform caching probabilities regardless of content popularities, to achieve a target SDP, the BS density of a tier can be reduced by increasing the cache size of the tier when the cache size is larger than a threshold; otherwise, the BS density and BS cache size can be increased simultaneously. It is also found analytically that the BS density of a tier is inverse to the BS cache size of the same tier and is linear to BS cache sizes of other tiers.

Caching at the wireless edge is an effective way to trade the scarce communication bandwidth with the more sustainable storage size through traffic time shifting. Multiple-input multiple-output (MIMO), on the other hand, can bring spatial multiplexing gain in wireless channels. This work is to investigate the combined effect of MIMO and caching in a three- user cache-aided MIMO broadcast channel, where the base station is equipped with M antennas and each user is equipped with N antennas. Decentralized coded caching is adopted in this paper, which converts the content delivery phase to a transmission problem in a broadcast channel with general message sets. We first derive an achievable degrees of freedom (DoF) region of the three-user MIMO broadcast channel with general message sets. Based on this region, we then obtain an achievable normalized delivery time (NDT), which illustrates the spatial multiplexing gain of MIMO through simultaneous transmission of multiple multicast messages intended to different user sets. It is shown that the achievable NDT is optimal at certain cases and is within a multiplicative gap of 3 from the optimum at other cases.

The demand for providing multicast services in emerging cellular networks is increasing. This paper proposes a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to incorporate multicast and unicast services into cellular networks with limited- capacity backhaul. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for unicast services, and the two layers are superposed with different beamformers and decoded by each user receiver using successive interference cancellation. To optimize the non- orthogonal transmission, we formulate a joint multicast and unicast beamforming design problem with adaptive base station (BS) clustering that aims to maximize the weighted sum of multicast rate and unicast rate under peak power constraints and peak backhaul constraints on each BS. The problem is a sparse optimization problem and NP- hard. By means of smoothed l0-norm approximation and novel algebraic operations, we transform it into a difference of convex (DC) programming, which is solved using convex-concave procedure (CCP) with guaranteed convergence. Simulation results demonstrate the superiority of the proposed algorithm, especially when the backhaul constraint is not too stringent. Results also show that the proposed LDM-based non-orthogonal scheme can achieve a much larger multicast-unicast rate region than orthogonal schemes.

Cache-aided communication is emerging as a new topic in wireless networks. Previous works have shown that caching in interference networks can change the interference topology by changing the information flow and hence facilitate advanced interference management. This paper studies the gain of caching in partially connected interference networks where each receiver can only communicate with a subset of transmitters. The performance is characterized by an information- theoretic metric, normalized delivery time (NDT). We obtain an order-optimal NDT for the (K+L-1)×K partially connected linear interference network with any number of receivers K, any receiver connectivity L≤K, and with caches equipped at all transmitters and receivers. The cache placement phase adopts a file splitting strategy tailor- made for the partial receiver connectivity. Via the aid of virtual receivers, the proposed delivery strategy exploits coded multicasting gain by XOR combining and transmitter coordination gain by interference alignment. In the special case when L is a divisor of K, our NDT results are directly applicable to K×K partially connected circular interference networks.

Previous works on cache-enabled small-cell networks (SCNs) with probabilistic caching often assume that each user is connected to the nearest small base station (SBS) among all that have cached its desired content. The user may, however, suffer strong interference from other SBSs which do not cache the desired content but are geographically closer. In this work, we investigate this issue by deploying multiple antennas at each SBS. We first propose a user-centric SBS clustering model where each user chooses its serving SBS only from a cluster of nearest SBSs with being a fixed cluster size. Two beamforming schemes are considered. One is coordinated beamforming, where each SBS uses zero-forcing (ZF) beamformer to null out the interference within the coordination cluster. The other is uncoordinated beamforming, where each SBS simply applies matched-filter (MF) beamformer. Using tools from stochastic geometry, we obtain tractable expressions for the successful transmission probability (STP) of a typical user for both cases in the high signal-to-noise ratio (SNR) region. Tight approximations in closed-form expressions are also obtained. We then formulate and solve the optimal probabilistic caching problem to maximize the STP. Numerical results reveal interesting insights on the choices of ZF and MF beamforming in multi-antenna cache-enabled SCNs.

Multi-group multicast beamforming in wireless systems with large antenna arrays and massive audience is investigated in this paper. Multicast beamforming design is a well-known non-convex quadratically constrained quadratic programming (QCQP) problem. A recent attempt is to apply convex-concave procedure (CCP) to find a stationary solution, whose complexity, however, increases dramatically as the problem size increases. In this paper, we propose a low-complexity highperformance algorithm for multi-group multicast beamforming design in large-scale wireless systems by utilizing the alternating direction method of multipliers (ADMM) together with CCP. In specific, the original non-convex QCQP problem is first approximated by a sequence of convex subproblems via CCP. Each convex subproblem is then reformulated as a novel ADMM form. Our ADMM reformulation enables that each updating step is performed by solving multiple small-size subproblems with closed-form solutions in parallel. Numerical results show that our fast algorithm maintains the same favorable performance as state-of-the-art algorithms but reduces the complexity by orders of magnitude.

This paper studies multi-group multicast beamforming with a hybrid large-scale antenna array in millimeter wave (mmWave) communication systems. A low-complexity hybrid structure is adopted, where each RF chain is only connected to part of the antenna elements. We formulate a hybrid analog and digital beamforming design problem for multi-group multicast transmission with the objective of minimizing the total transmit power at the base station, subject to an individual signal-to-interference-plus-noise ratio constraint for each multicast group. The problem is very challenging and its global optimal solution is difficult to obtain. We first adopt alternating minimization method to design the analog and digital beamformer alternatively. Then we solve each of the analog and digital subproblems through solving a sequence of convex problems via concave-convex procedure (CCP). Each convex CCP subproblem is reformulated as a novel alternating direction method of multipliers (ADMM) form. Our ADMM reformulation enables that each updating step can be decomposed into multiple subproblems with much smaller size, which can be solved optimally in parallel with closed-form expressions. Simulation results show that our algorithm can achieve favorable performance with very low complexity compared with the state-of-art methods.

Coded caching is able to exploit accumulated cache size in a wireless network by storing fractions of files in each node. In this work, we model, analyze, and optimize coded caching in cache-enabled small-cell networks (SCNs). We first propose a content delivery framework for coded caching, where multiple small base stations (SBSs) that cache different coded packets of a desired file transmit simultaneously upon a user request and the packets are decoded using successive interference cancellation (SIC)-based receiver. A closed-form expression for the successful transmission probability with SIC receiver in the high signal-to-noise ratio (SNR) region is derived by using stochastic geometry. We next optimize the cache placement to maximize the average fraction of offloaded traffic by the cache-enabled SBSs. The coded cache placement problem is formulated as a multiple-choice knapsack problem (MCKP). By exploiting the analytic properties of the average traffic offload fraction, we propose a greedy but optimal algorithm. Numerical results show our proposed coded caching scheme facilitated by simultaneous transmission and SIC-based receiving significantly outperforms uncoded caching. In addition, the coded caching only needs to split each file into a small number of fragments to approach an ideal performance.

In this paper, we investigate cooperative caching for spectrum access in cognitive radio networks. By cooperative caching, we mean that the unlicensed secondary base station (SBS) can cache certain primary contents to serve primary users, in exchange for the opportunities to access the licensed spectrum. We consider the joint optimization of caching and scheduling of the SBS to maximize the weighted average number of satisfied secondary requests under the average available time constraint and the cache capacity constraint. This problem is a mixed-integer bilinear programming, which is challenging in general. By exploring the special structure of the problem, we first show that the optimal caching satisfies a cache-split structure and the optimal scheduling satisfies a rate-ratio structure. Then, based on these optimality properties, we transform the original problem into a simplified joint cache splitting and SU partitioning optimization problem, and propose an efficient algorithm to solve it optimally. Moreover, we investigate the impacts of the primary and secondary content popularity distributions on the system performance. Numerical results verify the theoretical analysis and provide some counter-intuitive insights.

Millimeter-wave (mmWave) communication is the rising technology for next-generation wireless transmission. Benefited by its abundant bandwidth and short wavelength, mmWave is advanced in multi-gigabit transmittability and beamforming. In contrast, the short wavelength also makes mmWave easily blocked by obstacles. In order to bypass these obstacles, relays are widely needed in mmWave communications. Unmanned autonomous vehicles (UAVs), such as drones and self-driving robots, enable the mobile relays in real applications. Nevertheless, it is challenging for a UAV to find its optimal relay location automatically. On the one hand, it is difficult to find the location accurately due to the complex and dynamic wireless environment; on the other hand, most applications require the relay to forward data immediately, so the autonomous process should be fast. To tackle this challenge, we propose a novel method AutoRelay specialized for mmWave communications. In AutoRelay, the UAV samples the link qualities of mmWave beams while moving. Based on the real-time sampling, the UAV gradually adjusts its path to approach the optimal location by leveraging compressive sensing theory to estimate the link qualities in candidate space, which increases the accuracy and save the time. Performance results demonstrate that AutoRelay outperforms existing methods in achieving an accurate and efficient relay strategy.

Multi-group multicast beamforming in wireless systems with large antenna arrays and massive audience is investigated in this paper. Multicast beamforming design is a well-known non-convex quadratically constrained quadratic programming (QCQP) problem. A conventional method to tackle this problem is to approximate it as a semi-definite programming problem via semi-definite relaxation, whose performance, however, deteriorates considerably as the number of per-group users goes large. A recent attempt is to apply convex-concave procedure (CCP) to find a stationary solution by treating it as a difference of convex programming problem, whose complexity, however, increases dramatically as the problem size increases. In this paper, we propose a low-complexity high-performance algorithm for multi-group multicast beamforming design in large-scale wireless systems by leveraging the alternating direction method of multipliers (ADMM) together with CCP. In specific, the original non-convex QCQP problem is first approximated as a sequence of convex subproblems via CCP. Each convex subproblem is then reformulated as a novel ADMM form. Our ADMM reformulation enables that each updating step is performed by solving multiple small-size subproblems with closed-form solutions in parallel. Numerical results show that our fast algorithm maintains the same favorable performance as state-of-the-art algorithms but reduces the complexity by orders of magnitude.

Coded caching is able to exploit accumulated cache size and hence superior to uncoded caching by distributing different fractions of a file in different nodes. This paper investigates coded caching in a large-scale small-cell network (SCN) where the locations of small base stations (SBSs) are modeled by stochastic geometry. We first propose a content delivery framework, where multiple SBSs that cache different coded packets of a desired file transmit concurrently upon a user request and the user decodes the signals using successive interference cancellation (SIC). We characterize the performance of coded caching by two performance metrics, average fractional offloaded traffic (AFOT) and average ergodic rate (AER), for which a closed-form expression and a tractable expression are derived, respectively, in the high signal-to-noise ratio region. We then formulate the coded cache placement problem for AFOT maximization as a multiple-choice knapsack problem (MCKP). By utilizing the analytical properties of AFOT, a greedy but optimal algorithm is proposed. We also consider the coded cache placement problem for AER maximization. By converting this problem into a standard MCKP, a heuristic algorithm is proposed. Analytical and numerical results reveal several design and performance insights of coded caching in conjunction with SIC receiver in interference-limited SCNs.

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not fully exploit the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power, and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP).By usingrelative value iterationand special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. Motivated by the switch structures of the optimal policy, we propose a low-complexity suboptimal policy, which exhibits similar switch structures to the optimal policy, and design a low-complexity algorithm to compute this policy.

This paper studies the fundamental tradeoff between storage and latency in a general wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by an information-theoretic metric, normalized delivery time (NDT), which is the worst case delivery time of the actual traffic load at a transmission rate specified by degrees of freedom of a given channel. We obtain both an achievable upper bound and a theoretical lower bound of the minimum NDT for any number of transmitters, any number of receivers, and any feasible cache size tuple. We show that the achievable NDT is exactly optimal in certain cache size regions, and is within a bounded multiplicative gap to the theoretical lower bound in other regions. In the achievability analysis, we first propose a novel cooperative transmitter/receiver coded caching strategy. It offers the freedom to adjust file splitting ratios for NDT minimization. We then propose a delivery strategy that transforms the considered interference network into a new class of cooperative X-multicast channels. It leverages local caching gain, coded multicasting gain, and transmitter cooperation gain (via interference alignment and interference neutralization) opportunistically. Finally, the achievable NDT is obtained by solving a linear programming problem. This paper reveals that with caching at both transmitter and receiver sides, the network can benefit simultaneously from traffic load reduction and transmission rate enhancement, thereby effectively reducing the content delivery latency.

We consider a generic problem of multicast beamforming design in multicell networks where each base station (BS) has multiple independent messages to multicast and each user intends to decode an arbitrary subset of messages sent from all BSs using successive group decoding (SGD). We first formulate the total transmit power minimization problem subject to the constraints that a target rate vector is achievable by the SGDs at all receivers. This problem is a non-convex quadratically constrained quadratic program and NP-hard. We propose a new method based on solving a sequence of linearly regularized semi-definite programming (SDP) relaxation of the original problem that yields feasible and near-optimal solutions with high probability. Moreover, we propose a decentralized algorithm based on the alternating direction method of multipliers to solve each linearly regularized SDP, which consists of solving a quadratic program at the central controller, and closed-form analytic computations at each BS. Finally, we propose an iterative procedure for joint beamformer and rate optimization under the SGD framework. Numerical results confirm the superiority of the proposed beamformer design in both performance and complexity. It is also demonstrated that, compared with the traditional linear receivers, the SGD receivers achieve both significant rate improvement and energy savings.

Caching is an effective technique to improve user perceived experience for content delivery in wireless networks. Wireless caching differs from traditional web caching in that it can exploit the broadcast nature of wireless medium and hence, opportunistically change the network topologies. This paper studies a cache-aided MIMO interference network with three transmitters each equipped with M antennas and three receivers each with N antennas. With caching at both the transmitter and receiver sides, the network is changed to hybrid forms of MIMO broadcast channel, MIMO X channel, and MIMO multicast channels. We analyze the degrees of freedom (DoF) of these new channel models using practical interference management schemes. Based on the collective use of these DoF results, we then obtain an achievable normalized delivery time (NDT) of the network, an information-theoretic metric that evaluates the worst-case delivery time at given cache sizes. The obtained NDT is for arbitrary M, N, and any feasible cache sizes. It is shown to be optimal in certain cases and within a multiplicative gap of 3 from the optimum in other cases. The extension to the network with arbitrary number of transmitters and receivers is also discussed.

Recently, content-aware-enabled distributed caching relying on local small-cell base stations (SBSs), namely, smallcell caching, has been intensively studied for reducing transmission latency as well as alleviating the traffic load over backhaul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several content providers (CPs), and multiple mobile users (MUs). The NSP, as a network facility monopolist in charge of the SBSs, leases its resources to the CPs for gaining profits. At the same time, the CPs are intended to rent the SBSs for providing better downloading services to the MUs. We focus on solving the profit maximization problem for the NSP within the framework of contract theory. To be specific, we first formulate the utility functions of the NSP and the CPs by modeling the MUs and SBSs as two independent Poisson point processes. Then, we develop the optimal contract problem for an information asymmetric scenario, where the NSP only knows the distribution of CPs' popularity among the MUs. Also, we derive the necessary and sufficient conditions of feasible contracts. Lastly, the optimal contract solutions are proposed with different CPs' popularity parameter γ. Numerical results are provided to show the optimal quality and the optimal price designed for each CP. In addition, we find that the proposed contract-based mechanism is superior to the benchmarks from the perspective of maximizing the NSP's profit.

Caching is an effective technique to improve user perceived experience for massive content delivery in wireless networks. An essential problem in cache-aided wireless networks is to find what and how much gain can be achieved by caching. This paper provides a study of the fundamental storage-latency tradeoff for a cache-aided MIMO interference network with 3 transmitters and 3 receivers and each node equipped with antennas. By using a newly proposed novel file splitting and caching strategy, the network topology during the content delivery phase is turned opportunistically to MIMO X channel, MIMO broadcast channel, MIMO multicast channel, or a hybrid form of these channels. Linear-precoding based interference management schemes such as interference alignment and neutralization over finite symbol extension are designed for these channels. We characterize the storage-latency tradeoff by fractional delivery time (FDT), a metric to evaluate the worst-case delivery time of the actual traffic load at a rate specified by the degrees of freedom (DoF) of the considered channel. The achievable FDT of our proposed scheme decreases piecewise linearly with the normalized cache sizes and is inversely proportional to the number of antennas. It is also shown that the achievable FDT is optimal at certain cache size regions and is within a multiplicative gap of 2 from the optimum at other regions.

In this paper, we study the optimal degrees of freedom (DoF) region for the two-pair MIMO two-way relay channel (TWRC) with asymmetric antenna setting, where two pairs of users exchange information with the help of a common relay. First, we derive an outer bound of the DoF region by using the cut-set theorem and the genie-message approach. Then, we propose a new transmission scheme to achieve the outer bound of the DoF region. Due to the asymmetric data exchange, where the two users in each pair can communicate a different number of data streams, we not only need to form the network-coded symbols but also need to process the additional asymmetric data streams at the relay. This is realized through the joint design of relay compression matrix and source precoding matrices. From the optimal DoF region of this channel, we show that in the asymmetric antenna setting, some antennas at certain source nodes are redundant and cannot contribute to enlarge the DoF region.

Caching in wireless device-to-device (D2D) networks can be utilized to offload data traffic during peak times. However, the design of incentive mechanisms is challenging due to the heterogeneous preference and selfish nature of user terminals (UTs). In this paper, we propose an incentive mechanism in which the base station (BS) rewards those UTs that share contents with others using D2D communication. We study the cost minimization problem for the BS and the utility maximization problem for each UT. In particular, the BS determines the rewarding policy to minimize his total cost, while each UT aims to maximize his utility by choosing his caching policy. We formulate the conflict among UTs and the tension between the BS and the UTs as a Stackelberg game. We show the existence of the equilibrium and propose an iterative gradient algorithm (IGA) to obtain the Stackelberg Equilibrium. Extensive simulations are carried out to evaluate the performance of the proposed caching scheme and comparisons are drawn with several baseline caching schemes with no incentives. Numerical results show that the caching scheme under our incentive mechanism outperforms other schemes in terms of the BS serving cost and the utilities of the UTs.

This paper investigates the linear precoder design for cognitive multiple-access wiretap channel (CMAC-WT), where two secondary-user transmitters (STs) communicate with one secondary-user receiver (SR) in the presence of an eavesdropper and subject to interference threshold constraints at primary-user receivers (PRs). It designs linear precoders to maximize the ergodic secrecy sum rate for multiple-input multiple-output (MIMO) CMAC-WT under finite-alphabet inputs and statistical channel state information (CSI). For this non-convex problem, a two-layer algorithm is proposed by embedding the convex-concave procedure into an outer approximation framework. The key idea of this algorithm is to reformulate the approximated ergodic secrecy sum rate as a difference of convex (DC) functions, and then generate a sequence of simpler relaxed sets to approach the non-convex feasible set. In this way, near optimal precoding matrices are obtained by maximizing the approximated ergodic secrecy sum rate over a sequence of relaxed sets. Numerical results show that the proposed precoder design provides a significant performance gain over the Gaussian precoding method in the medium and high SNR regimes.

This work studies the sum degrees of freedom (DoF) of the asymmetric four-user MIMO Y channel, where each user i, for 1 ≤ i ≤ 4, is equipped with Mi antennas, and the relay is equipped with N antennas. By using the genie message approach, we derive that the sum DoF is upper bounded by min {Σi=14Mi, 2 Σi=24Mi, 2N, 2/7 Σ∀i,∀j>imax{N, Mi+ Mj} }, where M1≥ M2≥ M3≥ M4. Then, we show that the bound is tight under certain antenna constraints using signal alignment, generalized signal alignment, and antenna deactivation techniques.

This paper studies the storage-latency tradeoff in the 3 × 3 wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by the so-called fractional delivery time (FDT) at given normalized transmitter and receiver cache sizes. We first propose a generic cooperative transmitter/receiver caching strategy with adjustable file splitting ratios. Based on this caching strategy, we then design the delivery phase carefully to turn the considered interference channel opportunistically into broadcast channel, multicast channel, X channel, or a hybrid form of these channels. After that, we obtain an achievable upper bound of the minimum FDT by solving a linear programming problem of the file splitting ratios. The achievable FDT is a convex and piece-wise linear decreasing function of the cache sizes. Receiver local caching gain, coded multicasting gain, and transmitter cooperation gain (interference alignment and interference neutralization) are leveraged in different cache size regions.

This paper presents a content-centric transmission design in a cloud radio access network by incorporating multicasting and caching. Users requesting the same content form a multicast group and are served by a same cluster of base stations (BSs) cooperatively. Each BS has a local cache, and it acquires the requested contents either from its local cache or from the central processor via backhaul links. We investigate the dynamic content-centric BS clustering and multicast beamforming with respect to both channel condition and caching status. We first formulate a mixed-integer nonlinear programming problem of minimizing the weighted sum of backhaul cost and transmit power under the quality-of-service constraint for each multicast group. Theoretical analysis reveals that all the BSs caching a requested content can be included in the BS cluster of this content, regardless of the channel conditions. Then, we reformulate an equivalent sparse multicast beamforming (SBF) problem. By adopting smoothed ℓ0-norm approximation and other techniques, the SBF problem is transformed into the difference of convex programs and effectively solved using the convex-concave procedure algorithms. Simulation results demonstrate significant advantage of the proposed content-centric transmission. The effects of heuristic caching strategies are also evaluated.

Due to its time-varying nature, oscillator phase noise can significantly degrade the performance of the channel estimation, carrier recovery, and data detection blocks in high-speed wireless communication systems. In this paper, we propose a new data-aided joint channel, carrier frequency offset (CFO) and phase noise estimator for orthogonal frequency division multiplexing (OFDM) relay systems. For the data transmission phase, we propose a new iterative receiver that tracks phase noise and detects the transmitted symbols. Additionally, we derive the hybrid Cramér-Rao lower bound for evaluating the performance of channel estimation and carrier recovery algorithms in OFDM relay networks. Extensive simulations demonstrate that the application of the proposed estimation and receiver blocks significantly improves the performance of OFDM relay networks in the presence of phase noise and CFO.

Most of the existing works on energy-efficient wireless communications only consider the transmitter (Tx) or the receiver (Rx) side power consumption, but not both. Moreover, the circuit power consumption is often assumed to be constant regardless of the transmission rate or the bandwidth. In this paper, we investigate the system-level energy-efficient transmission in multi-radio access networks by considering joint Tx and Rx power consumption and adopting link-dependent dynamic circuit power model. A combinatorial-type optimization problem for user scheduling, radio-link activation, and power control is formulated with the objective of maximizing joint Tx and Rx energy efficiency (EE). We tackle this problem using a divide-and-conquer approach. Specifically, the concepts of link EE and user EE are first introduced, which have structures similar to the system EE. Then, we explore their hierarchical relationships and propose an optimal algorithm whose complexity is linear in the product of the total number of users and radio links. Furthermore, we investigate the EE maximization problem with minimum user data rate constraints. The divide-and-conquer approach is also applied to find a sub-optimal but efficient solution. Finally, comprehensive numerical results are provided to validate the theoretical findings and demonstrate the effectiveness of the proposed algorithms.

This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the nonconvexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting nonconvex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.

Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by considering the inherent multicast capability of wireless medium. We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using relative value iteration and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP. Based on these properties, we show that the optimal multicast scheduling policy is of threshold type. Then, we propose a structure-aware optimal algorithm to obtain the optimal policy. We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy.

Multicast transmission and wireless caching are effective ways of reducing air and backhaul traffic load in wireless networks. This paper proposes to incorporate these two key ideas for content-centric transmission in a cloud radio access network (RAN) where multiple base stations (BSs) are connected to a central processor (CP) via finite-capacity backhaul links. Each BS has a cache with finite storage size and is equipped with multiple antennas. The BSs cooperatively transmit contents, either stored in the local cache or fetched from the CP, to multiple users in the network. Users requesting a same content form a multicast group and are served by a same cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache placement, this paper investigates the joint design of multicast beamforming and content-centric BS clustering by formulating an optimization problem of minimizing the total network cost under the quality-of-service (QoS) constraints for each multicast group. The network cost involves both the transmission power and the backhaul cost. We model the backhaul cost using the mixed ℓ0/ℓ2-norm of beamforming vectors. To solve this non-convex problem, we first approximate it using the semidefinite relaxation (SDR) method and concave smooth functions. We then propose a difference of convex functions (DC) programming algorithm to obtain suboptimal solutions and show the connection of three smooth functions. Simulation results validate the advantage of multicasting and show the effects of different cache size and caching policies in cloud RAN.

This work is to study the degrees of freedom (DoF) of the K-user MIMO Y channel. Previously, two transmission frameworks, in addition to the original signal alignment introduced by Lee et al., have been proposed for the DoF analysis at different antenna configurations. One is signal group based alignment proposed by Hua et al. and the other is a signal pattern approach proposed by Wang et al. Yet, the maximum achievable ( DoF at antenna configuration N/E ∈ (2K2-2K/K2-K+2, K2-3K+k/K-1) still remains unknown, where M and N denote the number of antennas at each source node and the relay node, respectively. In this work, we first derive a new upper bound of the DoF using the genie-aided approach. Then, we propose a more general transmission framework, generalized signal alignment (GSA), to approach the new bound. With GSA, we prove that the new DoF upper bound is tight when N/M ∈ (0, 2 + 4/K(K-1)] ∪ [K - 2, + ∞).

Orthogonal frequency-division multiplexing (OFDM) has been widely adopted for broadband wireless communications due to its high spectral efficiency. However, it is sensitive to the time selectivity caused by the high-mobility, which largely degrades the accurate of estimating the channel state information (CSI). Therefore, the channel estimation in high-mobility OFDM systems has been a long-standing challenge. Recently, numerous experimental studies have shown that high-mobility broadband wireless channels tend to have some inherent sparsity. In this paper, we introduce the compressed sensing (CS) to utilize the inherent channel sparsity and estimate the high-mobility channel. Based on the CS minimization criterion, we propose two off-line pilot design algorithms to improve the estimation performance. One is to design the pilot symbol only and the other is to jointly design the pilot symbol and the pilot pattern. Simulation results show that the proposed methods achieve better estimation performances than conventional linear methods in high-mobility environments.

Most of the existing works on energy-efficient wireless communication systems only consider the transmitter (Tx) or the receiver (Rx) side power consumption but not both. Moreover, they often assume the static circuit power consumption. To be more practical, this paper considers the joint Tx and Rx power consumption in multiple-access radio networks, where the power model takes both the transmission power and the dynamic circuit power into account. We formulate the joint Tx and Rx energy efficiency (EE) maximization problem which is a combinatorial-type one due to the indicator function for scheduling users and activating radio links. The link EE and the user EE are then introduced which have the similar structure as the system EE. Their hierarchical relationships are exploited to tackle the problem using a divide-and-conquer approach, which is only of linear complexity. We further reveal that the static receiving power plays a critical role in the user scheduling. Finally, comprehensive numerical results are provided to validate our theoretical findings and demonstrate the effectiveness of the proposed algorithm for improving the system EE.

This paper considers wireless powered communication networks (WPCN). Our goal is to investigate the maximum network energy efficiency (EE) by joint time allocation and power control while taking account the initial battery energy level of each user. It is shown that the EE maximization problem for the WPCN can be cast into the EE maximization problems for two independent networks, i.e., purely wireless powered communication networks (PWPCN) or initial energy limited communication networks (IELCN). For the PWPCN, we find that: 1) in the wireless energy transfer (WET) stage, the power station always transmits with its maximum power; 2) it is not necessary for all users to transmit signals in the wireless information transmission (WIT) stage, but all scheduled users will deplete all of their energy; 3) the maximum system EE can always be achieved by exhausting all the available time. Based on these observations, we derive a closed-form expression for the system EE based on the user EE, which transforms the original problem into a user scheduling problem that can be solved efficiently. While for the IELCN, we reveal that the most energy-efficient transmission strategy is to only schedule the user who has the highest user EE. Simulation results validate our theoretical findings and demonstrate the effectiveness of the proposed scheme.

In this paper we consider the multicast transmission in massive MIMO systems where the base station (BS) serves multiple multicast groups simultaneously. Each single-antenna user takes interest in multiple groups but can be only assigned to one interested group during each transmission interval. We study the joint optimization of beamformer design and user grouping under the max-min fairness objective. First, when the BS has the perfect channel state information (CSI) of all users, we show that the asymptotic signal to interference plus noise ratio (SINR) is independent of user grouping and derive the optimal beamformers in closed-form. Second, we consider the imperfect CSI case in time-division duplex (TDD) mode where the BS obtains the downlink channel as the reciprocity of the estimated uplink channel. Instead of estimating every individual user channels, the BS estimates the composite channel of each multicast group. With pilot power control and beamformer power allocation, we propose an optimal user grouping algorithm and a low-complexity suboptimal user grouping algorithm. Numerical and simulation results are presented to demonstrate the performances of proposed algorithms.

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not make full use of the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). It is well-known to be a difficult problem and there generally only exist numerical solutions. By using relative value iteration algorithm and the special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. The optimality properties obtained in this paper can provide design insights for practical networks.

Environment reconstruction is to rebuild the physical environment in the cyberspace using the sensory data collected by sensor networks, which is a fundamental method for human to understand the physical world in depth. A lot of basic scientific work such as nature discovery and organic evolution heavily relies on the environment reconstruction. However, gathering large amount of environmental data costs huge energy and storage space. The shortage of energy and storage resources has become a major problem in sensor networks for environment reconstruction applications. Motivated by exploiting the inherent feature of environmental data, in this paper, we design a novel data gathering protocol based on compressive sensing theory and time series analysis to further improve the resource efficiency. This protocol adapts the duty cycle and sensing probability of every sensor node according to the dynamic environment, which cannot only guarantee the reconstruction accuracy, but also save energy and storage resources. We implement the proposed protocol on a 51-node testbed and conduct the simulations based on three real datasets from Intel Indoor, GreenOrbs and Ocean Sense projects. Both the experiment and simulation performances demonstrate that our method significantly outperforms the conventional methods in terms of resource efficiency and reconstruction accuracy.

In this paper, we consider a symmetric multiple-input-multiple-output (MIMO) multiway relay channel (mRC) with L clusters and K users per cluster operating in clustered pairwise data exchange. Each user is equipped with M antennas, and the relay is equipped with N antennas. The degrees of freedom (DoF) of the MIMO mRC has recently attracted much research interest. The DoF results under certain configurations of (L,K,M,N) have been reported. However, the DoF capacity of the MIMO mRC with an arbitrary network configuration is, in general, far from being well understood. In this regard, the main contribution of this paper is to propose a systematic signal alignment approach to jointly design the beamforming matrices at the users and the relay. Based on that, an achievable DoF is derived for the MIMO mRC with an arbitrary network configuration of (L,K,M,N). Our analysis revealed that the derived achievable DoF is piecewise linear in M and N alternately. Moreover, we showed that the DoF capacity can be achieved for M/N ∈ [1/LK(K-1) + 1/2, ∞) and M/N ∈ (0, Lq/LK(Lq-1)], where q = 2 ⌈K/2⌉.We further derived the asymptotic DoF as K or L → ∞. The DoF analysis in this paper can provide insights on the practical design of efficient communication mechanisms over multiterminal MIMO relay networks.

In this paper, we propose a cross-layer design framework for transmitting Markov modulated Poisson process (MMPP) traffic over Nakagami-m fading channel with delay demands. The adaptive modulation and coding (AMC) technique is used at the physical layer. The energy efficiency is described as the average throughput over the average transmission power, where both of throughput and transmit power have full consideration of the queuing system. We first derive the closed-form expressions of the delay and the energy efficiency with the stationary distribution of the system. We then derive the energy efficient thresholds to choose the AMC transmission modes. At last, we derive the transmission policy to maximize the energy efficiency with delay constraints. Numerical results are provided to support the theoretical development.

This paper investigates the joint transmitter and receiver optimization for the energy efficiency (EE) in orthogonal frequency-division multiple-access (OFDMA) systems. We first establish a holistic power dissipation model for OFDMA systems, including the transmission power, signal processing power, and circuit power from both the transmitter and receiver sides, while existing works only consider the one side power consumption and also fail to capture the impact of subcarriers and users on the system EE. The EE maximization problem is formulated as a combinatorial fractional problem that is NP-hard. To make it tractable, we transform the problem of fractional form into a subtractive-form one by using the Dinkelbach transformation and then propose a joint optimization method, which leads to the asymptotically optimal solution. To reduce the computational complexity, we decompose the joint optimization into two consecutive steps, where the key idea lies in exploring the inherent fractional structure of the introduced individual EE and the system EE. In addition, we provide a sufficient condition under which our proposed two-step method is optimal. Numerical results demonstrate the effectiveness of proposed methods, and the effect of imperfect channel state information is also characterized.

This paper studies the achievable degrees of freedom (DoF) for multi-user multiple-input multiple-output (MIMO) two-way relay channels, where there are K source nodes, each equipped with M antennas, one relay node, equipped with N antennas, and each source node exchanges independent messages with an arbitrary set of other source nodes via the relay. By allowing an arbitrary information exchange pattern, the considered channel model is a unified one. It includes several existing channel models as special cases: 1) K-user MIMO Y channel; 2) multi-pair MIMO two-way relay channel; 3) generalized MIMO two-way X relay channel; and 4) L-cluster MIMO multiway relay channel. Previous studies mainly considered the achievability of the DoF cut-set bound 2N at the antenna configuration N2]∪[K-2, +∞) for the generalized MIMO two-way X relay channel. We also provide the antenna configuration regions for the general multi-user MIMO two-way relay channel to achieve the total DoF KM.

In this paper, while considering the impact of the antenna correlation and the interference from neighboring users, we analyze channel estimation and training sequence design for multi-input multi-output (MIMO) two-way relay systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) phase and the broadcast (BC) phase. By considering the Kronecker-structured channel model, we derive the optimal linear minimum mean-square-error (LMMSE) channel estimators. The corresponding training designs for the MAC phase and the BC phase are then formulated and solved to improve channel estimation accuracy. For the general scenario of the training sequence design for both phases, two iterative training design algorithms are proposed that are verified to produce training sequences achieving near optimal channel estimation performance. Furthermore, for specific practical scenarios, where the covariance matrices of the channel or disturbances are of particular structures, the optimal training sequence design guidelines are obtained. The minimum required training lengths for channel estimation in both the MAC phase and the BC phase are also analyzed. Comprehensive simulations are carried out to demonstrate the effectiveness of the proposed training designs.

In this paper, we study the achievable degrees of freedom (DoF) for an arbitrary MIMO two-way relay channel, where there are K source nodes, each equipped with Miantennas, for i = 1, 2, ⋯, K, and one relay node, equipped with N antennas. Each source node can exchange independent messages with an arbitrary set of other source nodes assisted by the relay. We extend our newly-proposed transmission scheme, generalized signal alignment (GSA) in [1], to the arbitrary MIMO two-way relay channel with antenna configuration satisfying N ≥ Mi+ Mj, ∀i ≠ j. The notion of GSA is to form network-coded symbols by aligning every pair of signals to be exchanged in a projected subspace at the relay. This is realized by jointly designing the precoding matrices at all source nodes and the processing matrix at the relay node. Moreover, the aligned subspaces are orthogonal to each other. Applying the GSA, we show that the DoF upper bound min {Σi=1KMi, 2Σi=2KMi, 2N} is tight under the antenna configuration N ≥ max{Σi=1KMi-Ms-Mt+ds, t| ∀s, t}. Here, ds, tdenotes the DoF of the message exchanged between nodes s and t. In the special case when the arbitrary MIMO two-way relay channel reduces to the K-user MIMO Y channel, we show that our achievable region of DoF upper bound with GSA is larger than the existing result.

In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation in orthogonal frequency division multiplexing (OFDM) relaying networks. To achieve this goal, a detailed transmission framework involving both training and data symbols is first presented. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise parameters based on the maximum a posteriori criterion is proposed. Additionally, to evaluate the performance of the proposed channel estimation and carrier recovery algorithms, we analyze the ambiguities among the estimated parameters. Based on this analysis, a new Hybrid Cramér-Rao Lower Bound (HCRLB) is derived, which can effectively avoid such ambiguities. The simulation results show that the proposed estimation algorithm can achieve a performance close to the derived HCRLB.

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we study the problem of channel estimation and training sequence design in multi-input multi-output (MIMO) two-way relaying (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) and the broadcast (BC) phases. By deriving the optimal linear minimum mean-square-error estimators, the corresponding training design problems for the MAC and BC phases are formulated and solved. Subsequently, algorithms and, in some special cases, closed-form solutions for obtaining the optimal training sequences for channel estimation in TWR systems are derived. Moreover, to further reduce channel estimation overhead, the minimum required length of the training sequences are determined. Simulation results verify the effectiveness of the proposed training designs in improving channel estimation performance in TWR systems.

This paper investigates the optimal energy-efficient transmission policy of multi-channels in energy harvesting systems. We configure the transmitter with the active mode in which the energy cost includes the basic operation cost and transmission cost and signal processing cost, while with the sleep mode only counting the basic operation cost. Then the energy efficiency maximization problem of joint transmission time and power allocation is formulated and studied in the offline manner. Based on the fractional optimization theory, we transform the original fractional optimization problem into a series of subtractive-form optimization problems which are then further transformed into convex optimization problems. Then characteristics of the optimal solution are described based on the analysis of transmission time and power allocation. Finally, a special case without considering the basic operation cost as previous works assumed is studied. We find that the optimal policy results a best subchannel scheduling which can be viewed as the peaky transmission. Through this, the energy cost of the sleep mode in previous case can be interpreted as the switching operation cost.

Optimal queueing control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider a two-hop half-duplex relaying system with random channel connectivity. The relay is equipped with a finite buffer. We focus on stochastic link selection and transmission rate control to maximize the average system throughput subject to a half-duplex constraint. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP), which is well-known to be a difficult problem. By using sample-path analysis and exploiting the specific problem structure, we first obtain an equivalent Bellman equation with reduced state and action spaces. By using relative value iteration algorithm, we analyze the properties of the value function of the MDP. Then, we show that the optimal policy has a threshold-based structure by characterizing the supermodularity in the optimal control. Based the threshold-based structure and Markov chain theory, we further simplify the original complex stochastic optimization problem to a static optimization problem over a small discrete feasible set and propose a simple algorithm to solve the static optimization problem. Furthermore, we obtain the closed-form optimal threshold for the symmetric case. The analytical results obtained in this paper also provide design insights for two-hop relaying systems with multiple relays equipped with finite relay buffers.

We study the degrees of freedom (DoF) of MIMO two-way X relay channels. Previous work studied the case N2M where the performance is limited by the number of antennas at each source node and conventional SA is not feasible. We propose a generalized signal alignment (GSA) based transmission scheme. The key is to let the signals to be exchanged between every source node align in a transformed subspace, rather than the direct subspace, at the relay so as to form network-coded signals. This is realized by jointly designing the precoding matrices at all source nodes and the processing matrix at the relay. Moreover, the aligned subspaces are orthogonal to each other. By applying the GSA, we show that the DoF upper bound 4M is achievable when M ≤ ⌊ 2N/5 ⌋ (M is even) or M ≤ ⌊ 2N-1 /5 ⌋ (M is odd). Numerical results also demonstrate that our proposed transmission scheme is feasible and effective.

We study the massive MIMO (multiple-input multiple-output) multicast transmission in multicell networks, where each base station (BS) is equipped with a large-scale antenna array and transmits a common message using a single beamformer to multiple mobile users. We first consider the case when each BS knows the perfect channel state information (CSI) of all its served users. We show that the asymptotically optimal beamformer structure at each BS is a linear combination of the channel vectors of its multicast users. The optimal combination coefficients are also obtained in closed form. Then we consider the imperfect CSI scenario where each BS obtains the CSI through uplink channel estimation. We propose a novel pilot scheme that estimates the compound channel rather than the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The optimal power control of pilot transmission is also derived. Numerical results show that the performance of the proposed pilot scheme with pilot power control is close to that of the perfect CSI case.

We study physical layer multicasting in cellular networks where each base station (BS) is equipped with a very large number of antennas and transmits a common message using a single beamformer to multiple mobile users. The messages sent by different BSs are independent, and the BSs do not cooperate. We first show that when each BS knows the perfect channel state information (CSI) of its own served users, the asymptotically optimal beamformer at each BS is a linear combination of the channel vectors of its multicast users. Moreover, the optimal and explicit combining coefficients are obtained. Then we consider the imperfect CSI scenario where the CSI is obtained through uplink channel estimation in time-division duplex systems. We propose a new pilot scheme that estimates the composite channel, which is a linear combination of the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The pilot power control for optimizing the multicast beamformer at each BS is also derived. Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI. Simulation also verifies the effectiveness of the proposed scheme with finite number of antennas at each BS.

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system operating with high mobility is very challenging. This is mainly due to the significant Doppler spread, inherent in a time-frequency doubly-selective (DS) channel. Consequently, a large number of channel coefficients must be estimated, forcing the need for allocating a large number of pilot subcarriers. To address this problem, we propose a novel channel estimation method based on basis expansion models (BEMs) and distributed compressive sensing (DCS) theory. To be specific, we develop a two-stage sparse BEM coefficients estimation method, which can effectively combat the Doppler spread and enable accurate channel estimation with dramatically reduced number of pilot subcarriers. The numerical results reveal that, in a typical LTE system configuration, the proposed scheme can increase the spectral efficiency by 40% and achieve a 6 dB gain in terms of normalized mean square error (NMSE), both compared to the conventional scheme.

In this paper, we study the cross layer design and optimization for delay quality-of-service (QoS) provisioning in spectrum sharing femtocell networks. Our goal is to find the optimal resource allocation policy to maximize the throughput for each femtocell user, addressing the co-channel interference problem in the physical layer and the individual statistical delay-QoS guarantee problem from the upper layers. The statistical delay-QoS requirement is characterized by the QoS exponent. By integrating the concept of effective capacity, the cross-layer optimization problem is formulated as an effective capacity maximization game. With partial dual decomposition, this game is solved through a hierarchical structure. Specifically, we derive the optimal power allocation policy for femtocell users and design a distributed algorithm to obtain the Nash Equilibrium (N.E.). Numerical results show that the proposed policy can efficiently improve the performance of the networks.

Recently, a multiple-input multiple-output (MIMO) two-way X relay channel, where two groups of source nodes each having 2 nodes exchange independent messages via a common relay node, was studied in [1]. In this paper, we extend it to the generalized MIMO two-way X relay channel, where m ≥ 2 and n ≥ 2 source nodes are contained in two groups, respectively. Based on signal space alignment, a new beamforming scheme is proposed to maximize the minimum effective signal to interference plus noise ratios (SINRs) among all data streams. The beamforming vectors are designed by an iterative algorithm in which a closed-form solution is obtained in each step. Moreover, we show that the power allocation problem given the shape of the beamformers can be transformed as a linear programming problem. Simulation results show that the proposed beamforming scheme can achieve significantly better error performance than random beamforming schemes subject to signal space alignment only.

With the popularity of smart devices, content based internet services are growing rapidly. Different from real-time services, the end-to-end delay requirements of the contents are much less stringent. In this paper, we consider the delay-tolerant content-aware delivery in two-tier heterogeneous wireless networks. Taking transmit power consumption into account, we propose a new transmission framework by pushing users from macrocells to small cells at the cost of tolerable delay. We model the tolerable delay of content delivery in a stochastic way by considering users' interests. In this model, we adopt a progressively decreasing interest function which treats the delay as its argument. A modified Black-Scholes model is adopted to model the interest function. Thus, there exists a tradeoff between users' experience and power consumption. We consider power efficiency under a certain level of quality of experience (QoE) to handle this tradeoff. A content-aware transmission scheme with the optimal power efficiency is proposed. Simulation results show that under the maximal power efficiency criterion, about 18% of power consumption is reduced while about 90% of users' interests is preserved.

From security perspective, a friendly relay may help to keep the confidential messages from being eavesdropped, while an untrusted relay may intentionally eavesdrop the messages when relaying. This paper studies the secure beamforming for multiple-input multiple-output (MIMO) two-way communications, where two source nodes exchange information with the help of an untrusted relay node. The relay adopts amplify-and-forward (AF) strategy and acts as both an essential helper and a potential eavesdropper. Our goal is to maximize the secrecy sum rate of the bidirectional links by jointly optimizing the source and relay beamformers. For the two-phase two-way relay scheme, we first derive the optimal structure of the relay beamformer and then propose an iterative algorithm to jointly optimize the source and relay beamformers. Then, a comprehensive study on the asymptotical performance is conducted by letting the source and relay powers approach zero or infinity. In particular, we show that when all powers approach infinity, the two-way relay scheme achieves the maximum secrecy rate if the transceiver beamformers are designed such that the received signals at the relay can be aligned to be parallel.

As an effective solution for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. From the perspective of spectral efficiency, the macrocell base station (MBS) and femtocell base stations (FBSs) are usually deployed in the same spectrum. Then the interference problem has become a key obstruction that limits the network performance. In this paper, we study the spectrum reuse in the two-tier LTE femtocell network. In order to improve the network performance, the FBSs are encouraged to provide services to nearby macrocell users, and the MBS releases a fractional spectrum to the FBSs for avoiding cross-tier interference in return. We model this problem as a Stackelberg game where the MBS acts as a leader and the FBSs as the followers. We define the utilities for the MBS and FBSs as the average throughput and the distortion-rate function, respectively. It is worth noting that in our Stackelberg game model, there is no monetary price for the interaction between the leader and followers, which is the significant distinction from previous works. The optimal strategies of spectrum reuse for both MBS and FBSs are proposed by analyzing the Stackelberg game model. The simulation results show that the proposed spectrum reuse scheme can significantly improve the network performance.

Radio interferometric positioning systems (RIPS) are recently proposed for low-complexity and high-accuracy localization. However, the original RIPS involves four nodes (two transmitters and two receivers) for a ranging session, and requires stringent time synchronization upon two receivers. In this paper, an asynchronous radio interferometric positioning system (ARIPS) is developed with larger positioning ranges. In ARIPS, two anchors (nodes with known positions) transmit two slightly different dual-tone signals. The differences of the two dual-tone signals create two low-frequency differential signals at the target receiver. The phase differences of the differential signals bear the time-difference-of-arrival (TDOA) information, i.e., the distance information. We develop two new methods to estimate the TDOA with and without accurate knowledge of the frequencies of the differential signals, respectively. By switching the pairs of the anchor nodes, several TDOAs can be obtained and thus the location of the target node can be estimated. The proposed ARIPS is robust to carrier frequency offsets (CFOs) and random phases due to asynchronous oscillators, and increases the resolving range limit due to the well-known integer ambiguity issue. Simulation results illustrate the performance of the proposed ARIPS.

Most existing works about scheduling and resource allocation for orthogonal frequency division multiplexing (OFDM) based two-way relay networks have focused on immediate relay forwarding. In this paper, we consider relay buffering in delay-tolerant networks. The relay node is aided by two buffers and one for each user, so that it can adaptively decide when to buffer the received packets or to forward them according to the instantaneous channel and queue conditions. We formulate the joint optimization of subcarrier assignment, transmission mode selection (direct or relay mode), and relay strategy selection (buffering or forwarding), for maximizing the long-term average throughput. An efficient dual-based algorithm is proposed to characterize the optimal policy. Simulation results show that relay buffering can significantly enhance the long-term throughput in OFDM bidirectional transmission systems.

Two-way relaying promises considerable improvements on spectral efficiency in wireless relay networks. While most existing works focus on physical layer approaches to exploit its capacity gain, the benefits of two-way relaying on upper layers are much less investigated. In this paper, we study the cross-layer design and optimization for delay quality-of-service (QoS) provisioning in two-way relay systems. Our goal is to find the optimal transmission policy to maximize the weighted sum throughput of the two users in the physical layer while guaranteeing the individual statistical delay-QoS requirement for each user in the datalink layer. This statistical delay-QoS requirement is characterized by the QoS exponent. By integrating the concept of effective capacity, the cross-layer optimization problem is equivalent to a weighted sum effective capacity maximization problem. We derive the jointly optimal power and rate adaptation policies for both three-phase and two-phase two-way relay protocols. Numerical results show that the proposed adaptive transmission policies can efficiently provide QoS guarantees and improve the performance. In addition, the throughput gain obtained by the considered three-phase and two-phase protocols over direct transmission is significant when the delay-QoS requirements are loose, but the gain diminishes at tight delay requirements. It is also found that, in the two-phase protocol, the relay node should be placed closer to the source with more stringent delay requirement.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of pilot subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of pilot subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

Two-way relaying can considerably improve spectral efficiency in relay-assisted bidirectional communications. However, the benefits and flexible structure of orthogonal frequency division multiplexing (OFDM)-based two-way relay systems is much less exploited. Moreover, most of existing works have not considered quality-of-service (QoS) provisioning for two-way relaying. In this paper, we consider the OFDM-based bidirectional transmission where a pair of users exchange information via the assistance of a decode-and-forward (DF) relay. Each user can communicate with the other via three transmission modes: direct transmission, one-way relaying, and two-way relaying. We jointly optimize the transmission policies, including power allocation, transmission mode selection, and subcarrier assignment in order to maximize the weighted sum rates of the two users with diverse QoS guarantees. This is formulated as a mixed integer programming problem. By using the dual method, we efficiently solve the problem in an asymptotically optimal manner. Simulation results show that the proposed resource allocation scheme can substantially improve system performance compared with conventional schemes. A number of interesting insights are also obtained via comprehensive simulations.

This paper studies a wireless network where multiple users cooperate with each other to improve the overall network performance. Our goal is to design an optimal distributed power allocation algorithm that enables user cooperation, in particular, to guide each user on the decision of transmission mode selection and relay selection. Our algorithm has the nice interpretation of an auction mechanism with multiple auctioneers and multiple bidders. Specifically, in our proposed framework, each user acts as both an auctioneer (seller) and a bidder (buyer). Each auctioneer determines its trading price and allocates power to bidders, and each bidder chooses the demand from each auctioneer. By following the proposed distributed algorithm, each user determines how much power to reserve for its own transmission, how much power to purchase from other users, and how much power to contribute for relaying the signals of others. We derive the optimal bidding and pricing strategies that maximize the weighted sum rates of the users. Extensive simulations are carried out to verify our proposed approach.

In wireless cooperative orthogonal frequency-division multiple-access (OFDMA) networks, it is important to adapt the transmission strategies for each user according to the network channel dynamics in order to optimize the overall system performance. The adaption involves transmission mode selection (a user can choose from direct or cooperative transmission), subcarrier assignment, subcarrier pairing (the incoming and outgoing subcarriers at the relay for cooperative transmission need to be matched), relay selection, as well as power allocation and hence is highly challenging. Many previous works only consider a subset of the adaptation. In this paper, we tackle the joint optimization problem using a network flow approach. Specifically, we first show that for given power allocation, the combinatorial optimization problem of transmission mode selection, subcarrier assignment, relay selection and subcarrier pairing for the system total throughput maximization can be transformed into a minimum cost network flow (MCNF) problem with integer solutions. The linear optimal distribution (LOD) algorithm is applied to find the optimal solution in polynomial time. We then solve the mixed integer programming problem of the joint assignment and power allocation in an asymptotically optimal way in the dual domain. Simulation results show that the proposed algorithms can significantly enhance the overall system throughput.

We study physical layer multicasting in multicell networks where each base station, equipped with multiple antennas, transmits a common message using a single beamformer to multiple users in the same cell. We investigate two coordinated beamforming designs: the quality-of-service (QoS) beamforming and the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The goal of the QoS beamforming is to minimize the total power consumption while guaranteeing that received SINR at each user is above a predetermined threshold. We present a necessary condition for the optimization problem to be feasible. Then, based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. The max-min SINR (MMS) beamforming is to maximize the minimum received SINR among all users under per-base station power constraints. We show that the MMS problem and a weighted peak-power minimization (WPPM) problem are inverse problems. Based on this inversion relationship, we then propose an efficient algorithm to solve the MMS problem in an approximate manner. Simulation results demonstrate significant advantages of the proposed multicast beamforming algorithms over conventional multicasting schemes.

In this paper, we consider the orthogonal frequency division multiplexing (OFDM)-based bidirectional transmission where a pair of users exchange information with the assistance of a decode-and-forward (DF) relay. Each user can communicate with the other via three transmission modes: direct transmission, one-way relaying, and two-way relaying. We jointly optimize the transmission policies, including power allocation, transmission mode selection, and subcarrier-node assignment for maximizing the weighted sum rates of the two users with quality-of-service (QoS) guarantees. We formulate the joint optimization problem as a mixed integer programming problem. By using the dual method, we solve the problem efficiently in an asymptotically optimal manner. Particularly, we derive the capacity region of two-way DF relaying in parallel relay channels. Simulation results show that the proposed resource-allocation scheme can substantially improve system performance compared with the conventional schemes.

Hash functions are often used to protect the integrity of information. In general, the design of hash functions should satisfy three standards: pre-image resistance, second pre-image resistance and collision resistance. The design of hash functions in the literature assumes that the messages to be transmitted are equally probable. In this paper, we focus on the pre-image resistance and investigate the problem of mapping design for hash function utilizing the unequal occurrence probabilities of the messages. We first present a necessary condition for the optimal mapping and then introduce a heuristic algorithm. Simulation experiments are carried out to evaluate the performance of the proposed new design. It is shown that the probability of successful attack can be significantly reduced compared with the conventional design. Our algorithm can be useful in scenarios where the attacker has limited ability or time to estimate the probability distribution of the messages. To our best knowledge, this work is the first attempt of making use of the message distribution in designing hash functions for information security.

We study precoding design for cognitive two-way relay networks (C-TWRNs). In C-TWRN, the multi-antenna secondary transmitter not only transmits its own signal to the secondary receiver, it also acts as relay to help forwarding signals of two primary users via two-way relaying in the licensed frequency band. Our objective is to design linear relay transceiver or precoder such that the achievable rate of the secondary user is maximized while maintaining rate requirements of the primary users. To achieve this goal, different relay strategies, i.e., amplify-and-forward (AF), bit level XOR based decode-and-forward (DF-XOR) and symbol level superposition coding based decode-and-forward (DF-SUP), are considered. By transforming these non-convex design problems into suitable forms, efficient optimization tools are used to find the optimal solutions of all the schemes. Closed-form solutions are also obtained under certain conditions.

In this paper, we study the degrees of freedom of a multiple-input multiple-output (MIMO) two-way X relay channel, i.e., a system with two groups of source nodes and one relay node, where each of the two source nodes in one group wants to exchange independent messages with both the two source nodes in the other group via the relay node. We only consider the symmetric case where each source node is equipped with M antennas while the relay is equipped with N antennas. We first show that the upper bound of the degrees of freedom is 2N when N ≤ 2M. Then by applying physical layer network coding and joint interference cancellation, we propose a novel transmission scheme for the considered network. We show that this scheme can always achieve this upper bound when N ≤ ⌊4M/3⌋.

In this paper, we study the physical layer multicasting in multicell networks, where each base station equipped with multiple antennas transmits a common message using a single beamformer to multiple users equipped with a single antenna in the same cell. We consider the quality-of-service (QoS) beamforming for minimizing the total power consumption while guaranteeing that the signal-to-interference-plus-noise ratio (SINR) at each user is above a predetermined threshold. Based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. Simulation results demonstrate significant advantages of the proposed coordinated beamforming over conventional beamforming.

In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. CS enables the recovery of high-dimensional sparse signals from much fewer samples than usually required. Further, quite a few recent channel measurement experiments show that many wireless channels also tend to exhibit sparsity. In this case, CS theory can be applicable to sparse channel estimation and its effectiveness has been validated in point-to-point (P2P) communication. In this work, we study sparse channel estimation for two-way relay networks (TWRN). Unlike P2P systems, applying CS theory to sparse channel estimation in TWRN is much more challenging. One issue is that the equivalent channels (terminal-relay-terminal) may be no longer sparse due to the linear convolutional operation. On this basis, novel schemes are proposed to solve this problem and effectively improve the accuracy of TWRN channel estimation when using CS theory. Extensive numerical results are provided to corroborate the proposed studies.

In this paper, we consider the cross-layer design for delay quality-of-service (QoS) provisioning in two-way relay systems. We aim to find the optimal resource allocation policy to maximize the weighted sum-rate while guaranteeing the statistical delay-QoS requirements for both users. The delay requirement is characterized as the QoS exponent. With the integration of the concept of effective capacity, the cross-layer optimization problem is equivalent to a weighted sum effective capacity maximization problem. We derive the optimal joint power and rate adaptation policy for the two-phase two-way relaying. Numerical results show that the proposed policy can efficiently support diverse QoS requirements and significantly improve the performance compared with both the fixed power scheme and the weight-based method.

This paper considers a relay-assisted cooperative network where multiple relays assist the communication of multiple users using orthogonal frequency-division multiple-access (OFDMA). Our goal is to improve system performance by exploring full potential of the network in various dimensions, including user, relay, channel, and transmission mode. We formulate the joint optimization of subcarrier pairing, subcarrier assignment, relay selection, and transmission mode selection. We show that this combinatorial optimization problem can be transformed into a minimum cost network flow (MCNF) problem with integer solutions in graph theory. Then the linear optimal distribution (LOD) algorithm is applied to find the optimal solution in polynomial time. Simulations show that the proposed algorithm can significantly enhance the overall system throughput.

This paper presents an analytical study on the outage performance of amplify-and-forward (AF) two-way relay system with multi-antenna relay node (RN). Two major bidirectional protocols, i.e., two time slots multiple access broadcast (MABC) protocol and three time slots time division broadcast (TDBC) protocol, are considered. For both considerations, we first assume that instantaneous channel-state-information (CSI) is unavailable at RN, thus RN just simply uses the fixed relay gain derived from statistical CSI to scale the received signals before forwarding. We then consider the scenario where RN can obtain the instantaneous CSI to perform the zero-forcing (ZF) relay precoding. The closed-form expressions of outage probability are derived for all cases. Based on these expressions, the diversity-multiplexing tradeoff (DMT) is further obtained for the MABC protocol. The analytical results show that, for non-precoding MABC scheme, the diversity order is only 1, which is independent to the relay antenna number M. While for the ZF-precoding case, the diversity order of M - 1 can be obtained.

Cooperative jamming as a physical layer security enhancement has recently drawn considerable attention. While most existing works focus on communication systems with a small number of nodes, we investigate the use of cooperative jamming for providing secrecy in large-scale decentralized networks consisting of randomly distributed legitimate users and eavesdroppers. A modified slotted ALOHA protocol, named CJ-ALOHA, is considered where each legitimate transmitter either sends its message signal or acts as a helping jammer according to a message transmission probability p. We derive the secrecy transmission capacity to characterize the network throughput and show how the throughput is affected by the CJ-ALOHA protocol. Both analytical and numerical insights are provided on the design of the CJ-ALOHA protocol for optimal throughput performance.

Efficient utilization of radio resources in wireless networks is crucial and has been investigated extensively. This letter considers a wireless relay network where multiple user pairs conduct bidirectional communications via multiple relays based on orthogonal frequency-division multiplexing (OFDM) transmission. The joint optimization of channel and relay assignment, including subcarrier pairing, subcarrier allocation as well as relay selection, for total throughput maximization is formulated as a combinatorial optimization problem. Using a graph theoretical approach, we solve the problem optimally in polynomial time by transforming it into a maximum weighted bipartite matching (MWBM) problem. Simulation studies are carried out to evaluate the network total throughput versus transmit power per node and the number of relay nodes.

Partial decoding has the potential to achieve a larger capacity region than full decoding in two-way relay (TWR) channels. Existing partial decoding realizations are however designed for Gaussian channels and with a static physical layer network coding (PLNC). In this paper, we propose a new solution for joint network coding and channel decoding at the relay, called pairwise check decoding (PCD), for low-density parity-check (LDPC) coded TWR system over block fading channels. The main idea is to form a check relationship table (check-relation-tab) for the superimposed LDPC coded packet pair in the multiple access (MA) phase in conjunction with an adaptive PLNC mapping in the broadcast (BC) phase. Using PCD, we then present a partial decoding method, two-stage closest-neighbor clustering with PCD (TS-CNC-PCD), with the aim of minimizing the worst pairwise error probability. Moreover, we propose the minimum correlation optimization (MCO) for selecting the better check-relation-tabs. Simulation results confirm that the proposed TS-CNC-PCD offers a sizable gain over the conventional XOR with belief propagation (BP) in fading channels.

Two-way relaying can improve spectral efficiency in two-user cooperative communications. It also has great potential in multiuser systems. A major problem of designing a multiuser two-way relay system (MU-TWRS) is transceiver or precoding design to suppress co-channel interference. This paper aims to study linear precoding designs for a cellular MU-TWRS where a multi-antenna base station (BS) conducts bi-directional communications with multiple mobile stations (MSs) via a multi-antenna relay station (RS) with amplify-and-forward relay strategy. The design goal is to optimize uplink performance, including total mean-square error (Total-MSE) and sum rate, while maintaining individual signal-to-interference-plus-noise ratio (SINR) requirement for downlink signals. We show that the BS precoding design with the RS precoder fixed can be converted to a standard second order cone programming (SOCP) and the optimal solution is obtained efficiently. The RS precoding design with the BS precoder fixed, on the other hand, is non-convex and we present an iterative algorithm to find a local optimal solution. Then, the joint BS-RS precoding is obtained by solving the BS precoding and the RS precoding alternately. Comprehensive simulation is conducted to demonstrate the effectiveness of the proposed precoding designs.

In this paper, we consider a non-regenerative MIMO two-way relay system with imperfect channel state information (CSI). We employ a stochastic approach to model the channel uncertainties and address the robust joint source and relay optimization problem based on the minimum mean squared error (MMSE) criterion. With imperfect CSI, the self-interference (SI) cannot be completely canceled at destination nodes. Hence, both channel uncertainties and residual self-interference should be considered. We develop an optimization framework that unifies both frequency-division duplex (FDD) and time-division duplex (TDD) systems despite their different channel statistical properties. Two robust algorithms are proposed to minimize the sum mean squared error (MSE) averaged over channel uncertainties. The first algorithm adopts alternating optimization to update the source precoders, relay precoder and destination receivers iteratively with guaranteed convergence. In the second algorithm, only the relay precoder with certain structure is considered. Then the relay precoder design is reduced to the simple power allocation problem. Simulation results show that the proposed algorithms provide robustness against channel uncertainties, especially when the signal-to-noise (SNR) ratio is high.

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We investigate the linear precoding designs for multiuser two-way relay system (MU-TWRS) where a multi-antenna base-station (BS) communicates with multiple single-antenna mobile stations (MSs) via a multi-antenna relay station (RS). The amplify- and-forward (AF) relay protocol is employed. The design goal is to optimize the precodings at BS, RS or both so as to minimize the total mean-square error (MSE) of the uplink messages while maintaining the individual signal-to-interference-plus-noise ratio (SINR) requirement for each downlink signal. We show that the BS precoding design problem can be converted to a standard second order cone programming (SOCP), while the RS precoding is non- convex for which a local optimal solution is obtained using an iterative algorithm. A joint BS-RS precoding is also obtained by alternating optimization of BS precoding and RS precoding with guaranteed convergence. Numerical results show that RS-precoding is superior to BS-precoding. Furthermore, the joint BS-RS precoding can significantly outperform the two individual precoding schemes. The implementation issues including complexity and feedback overhead are also discussed.

We present a novel adaptive physical layer network coding (PLNC) at the relay, called pseudo exclusive-or (PXOR), for LDPC coded two-way relay (TWR) block fading channels. Based on the pairwise check decoding (PCD), the check relationship table generated by the proposed PXOR mapping obtains the same Hamming distances of the PLNC mapped codewords as that of conventional XOR mapping. In the meantime, the PXOR mapping optimizes the Euclidean distances by adjusting the symbol distances dynamically as far as possible in order to compensate the amplitude fading and phase deviation of the TWR block fading channels. For the system end-to-end error probability, simulation results show that the proposed coded PXOR considerably outperforms the coded conventional XOR and achieves the same performance as the complicated coded CNC for considered two TWR block fading channels.

We investigate the source and relay precoding based on the minimum mean-square-error (MMSE) criterion for amplify-and-forward (AF) MIMO two-way relay systems. This joint design problem is shown to be a highly nonconvex optimization problem. In this work, we present two efficient design algorithms. The first one aims to minimize the total MSE of two users by alternatively solving three trackable sub-problems and it is iterative in nature. Since the optimal solution for each sub-problem can be obtained, the convergence is thus ensured. The second design aims to compromise computational complexity and system performance and possesses a certain precoder structure. This structure is able to parallelize channels in the Multiple Access (MAC) and Broadcast (BC) phases of the two-way relaying. Based on such structure, the joint precoding design is simply reduced to the joint source and relay power allocation problem. The efficiency of both proposed algorithms is verified through simulation.

Providing physical-layer security for mobile users in future broadband wireless networks is of both theoretical and practical importance. In this paper, we formulate an analytical framework for resource allocation in a downlink OFDMA-based broadband network with coexistence of secure users (SU) and normal users (NU). The problem is formulated as joint power and subcarrier allocation with the objective of maximizing average aggregate information rate of all NU's while maintaining an average secrecy rate for each individual SU under a total transmit power constraint for the base station. We solve this problem in an asymptotically optimal manner using dual decomposition. Our analysis shows that an SU becomes a candidate competing for a subcarrier only if its channel gain on this subcarrier is the largest among all and exceeds the second largest by a certain threshold. Furthermore, while the power allocation for NU's follows the conventional water-filling principle, the power allocation for SU's depends on both its own channel gain and the largest channel gain among others. We also design a suboptimal algorithm to reduce the computational cost. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of the achievable pair of information rate for NU's and secrecy rate for SU at different power consumptions.

As a bridge of connecting orthogonal frequency division multiplexing (OFDM) with single-carrier frequency domain equalization (SC-FDE) techniques, Vector OFDM (V-OFDM) provides significant flexibility in system design. This paper presents an analytical study of V-OFDM over multi-path fading channels. Our goal is to investigate the diversity gain and coding gain of each vector block (VB) in V-OFDM so as to ultimately reveal its performance limits over fading channel. By using algebraic number theory tools, we rigorously prove for the first time that a majority of VBs in V-OFDM can surely realize the diversity gain of min {M,G} , where M is the length of each VB, and G is the total number of channel taps. Furthermore, some specific VBs, whose length equals the total number of channel taps, can not only harvest the maximum diversity gain but also achieve the maximum coding gain. It is further demonstrated that, even though VBs fail to benefit from additional diversity gain when M exceeds G, they can enjoy significantly increased coding gains. Our analysis concludes that it is preferable to choose the length of VBs to be equal to the number of channel taps in consideration of both overall system performance and computational complexity.

Providing physical-layer security for mobile users in future broadband wireless networks is of both theoretical and practical importance. In this paper, we formulate an analytical framework for resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA)-based broadband network with coexistence of secure users (SUs) and normal users (NUs). The SUs require secure data transmission at the physical layer while the NUs are served with conventional best-effort data traffic. The problem is formulated as joint power and subcarrier allocation with the objective of maximizing average aggregate information rate of all NUs while maintaining an average secrecy rate for each individual SU under a total transmit power constraint for the base station. We solve this problem in an asymptotically optimal manner using dual decomposition. Our analysis shows that an SU becomes a candidate competing for a subcarrier only if its channel gain on this subcarrier is the largest among all and exceeds the second largest by a certain threshold. Furthermore, while the power allocation for NUs follows the conventional water-filling principle, the power allocation for SUs depends on both its own channel gain and the largest channel gain among others. We also develop a suboptimal algorithm to reduce the computational cost. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of the achievable pair of information rate for NU and secrecy rate for SU at different power consumptions.

In this paper, we study the diversity-multiplexing tradeoff (DMT) of two-way relay channels (TWRC) with multi-antenna relay at finite signal-to-noise ratio (SNR). A two-phase decode-and-forward (DF) relay protocol and Rayleigh fading environment are considered. We first derive upper and lower bounds on the outage probability. These bounds are very tight for all practical SNR regions. Based on these bounds, the estimates of finite-SNR DMT (f-DMT) are then obtained. Our analysis shows that the multiplexing gain approaches one when SNR decreases to zero. On the other hand, the diversity gain approaches the number of relay antennas when SNR increases to infinity. Furthermore, the impact of time sharing, rate allocation and relay location on the outage and f-DMT is also discussed through numerical examples.

This paper considers a relay-assisted bidirectional cellular network where the base station (BS) communicates with each mobile station (MS) using orthogonal frequency-division multiple-access (OFDMA) for both uplink and downlink. We first introduce a novel three-time-slot time-division duplexing (TDD) transmission protocol. This protocol unifies the direct transmission, one-way relaying and network-coded two-way relaying between the BS and each MS. Using the proposed TDD protocol, we then propose an optimization framework for resource allocation to achieve the following gains: cooperative diversity gain (via relay selection), network coding gain (via bidirectional transmission mode selection), and multiuser diversity gain (via subcarrier assignment). We formulate the problem as an integer programming problem. By establishing its equivalence with a maximum weighted clique problem (MWCP) in graph theory, we show that the problem can be solved using an ant colony optimization (ACO) based metaheuristic algorithm in polynomial time. Simulation results demonstrate that the proposed protocol together with the ACO algorithm significantly enhances the system total throughput compared with conventional schemes.

We study the transmission strategy for a system consisting of two pairs of two-way communication links. The information exchange between the two nodes in each pair can only occur with the help of the nodes in the other pair. A novel transmission protocol, named Rotating Decode-and-Forward (RDF) is proposed. In this protocol, the two pairs take turns to forward the signal for each other while having their own information embedded in the signal-to-forward by applying physical layer network coding. The achievable rate region of this protocol is derived. Numerical examples show that proposed RDF protocol can provide significantly higher spectral efficiency than conventional protocols where the transmissions of information signal and signal-to-forward are carried out separately.

Information theoretic criteria (ITC) based spectrum sensing is a promising blind method which can reliably detect the primary users while requiring little prior information in cognitive radio networks. In this paper, we provide an intensive treatment on the ITC sensing. We first introduce a new over-determined channel model constructed by applying multiple antennas in order to make the ITC applicable. Then, a simplified ITC sensing algorithm is introduced, which needs to compute and compare only two decision values. Compared with the original ITC (OITC) sensing algorithm, the simplified algorithm significantly reduces the computational complexity without losing any performance. Furthermore, applying the recent advances in random matrix theory, we derive closed-form expressions to tightly approximate both the probability of false alarm and probability of detection. Finally, comprehensive simulations are carried out to evaluate the performance of the proposed ITC sensing algorithms. Results show that they considerably outperform existing blind spectrum sensing methods in certain cases.

This paper studies the performance limits of two-way relay channel (TWRC) at finite signal-to-noise ratio (SNR) in Rayleigh fading environment. A two-phase decode-and-forward (DF) protocol is considered. We first derive closed-form expressions for both outage probability and diversity-multiplexing tradeoff (DMT). Our results are general and suitable for any time sharing and any rate allocation in the two-way relay protocol. It is found that DF outperforms amplify-and-forward (AF) when either multiplexing gain or SNR is small enough, otherwise, DF is inferior to AF in term of outage probability. Meanwhile, finite-SNR DMT of DF is always lower than that of AF regardless of SNR due to the additional sum-rate constraint imposed on the relay node for full decoding. Furthermore, the optimum relay location for any given combination of time sharing and rate allocation is presented.

We present a novel partial decoding method at the relay, called pairwise check decoding (PCD), for two-way relay fading channels. The proposed PCD method forms a so-called check-relationship table for the superimposed Low-Density Parity-Check (LDPC)-coded packet pair during the multiple access phase. Meanwhile, it incorporates adaptive network coding by using closest-neighbor clustering mapping (CNCM) to compensate the phase deviation of the fading channels. The proposed PCD method is a practical and efficient realization of the promising denoise-and-forward relay strategy with advanced channel coding and non-linear network coding. Simulation results show that under the same LDPC-coded two-way relay system, our proposed PCD considerably outperforms the case where the relay performs only adaptive network coding without channel decoding. It also performs better than the case where the relay adopts the belief propagation decoding along with conventional XOR-based network coding under certain regions.

In this paper, we study the quantized precoding strategy selection for multiple-input multiple-output (MIMO) multiple access channels (MAC) in cognitive radio (CR) networks through a game-theoretic perspective. Since the secondary users in such system are difficult to be coordinated by a centralized authority, they are noncooperative and attempt to maximize their own payoffs selfishly in a distributed method. We propose a noncooperative precoding strategy selection game and find that it is a potential game which possesses at least one pure strategy Nash equilibrium. A decentralized learning algorithm with a small amount of feedback is proposed to obtain Nash equilibrium. We prove that the proposed algorithm can converge to a pure strategy Nash equilibrium. Simulation results are provided to verify our analysis.

In this paper, we propose a new interference alignment (IA) scheme designing jointly the linear transmitter and receiver for the MIMO interference channel system, using minimum total mean square error criterion, subject to individual transmit power constraints. We show that transmitter and receiver under such criterion could be realized through a joint iterative algorithm. The convergence of the proposed algorithm is discussed. We also proposed a robust MMSE-based iterative design with imperfect channel state information (CSI). The proposed robust MMSE-based iterative interference alignment scheme is shown to be less sensitive to channel estimation errors. Simulation results show that the proposed schemes outperform the existing IA schemes with fast convergence.

This paper is concerned with decentralized selection of multimode precoding strategy for multiple-input multiple-output (MIMO) multiple access channels. We formulate it as a discrete noncooperative game. This game is shown to possess at least one pure strategy Nash equilibrium (NE) and the optimal strategy profile which maximizes the sum rate constitutes a pure strategy NE. Then we propose a decentralized algorithm based on learning automata to achieve the NE. A repeated mechanism is introduced to improve the sum rate performance and a mechanism for adapting step size is designed to control the convergence speed. Simulation results show that the proposed algorithm, which only requires limited feedback, can achieve near optimal or optimal sum rate performance.

In this paper, we study the joint allocation of three types of resources, namely, power, subcarriers and relay nodes, in multi-relay assisted dual-hop cooperative OFDM systems. All the relays adopt the amplify-and-forward protocol and assist the transmission from the source to destination simultaneously but on orthogonal subcarriers. The objective is to maximize the system transmission rate subject to individual power constraints on each node or a total network power constraint. We formulate such a problem as a subcarrier-pair based resource allocation that seeks the joint optimization of subcarrier pairing, subcarrier-pair-to-relay assignment, and power allocation. Using a dual approach, we solve this problem efficiently in an asymptotically optimal manner. Specifically, for the optimization problem with individual power constraints, the computational complexity is polynomial in the number of subcarriers and relay nodes, whereas the complexity of the problem with a total power constraint is polynomial in the number of subcarriers.We further propose two suboptimal algorithms for the former to trade off performance for complexity. Simulation studies are conducted to evaluate the average transmission rate and outage probability of the proposed algorithms. The impact of relay location is also discussed.

This paper considers a relay-assisted bidirectional cellular network where the base station (BS) communicates with each mobile station (MS) using orthogonal frequency-division multiple-access (OFDMA) for both uplink and downlink. The goal is to improve the overall system performance by exploring the full potential of the network in various dimensions including user, subcarrier, relay, and bidirectional traffic. In this work, we first introduce a novel three-time-slot time-division duplexing (TDD) transmission protocol. This protocol unifies direct transmission, one-way relaying and network-coded two-way relaying between the BS and each MS. Using the proposed three-time-slot TDD protocol, we then propose an optimization framework for resource allocation to achieve the following gains: cooperative diversity (via relay selection), network coding gain (via bidirectional transmission mode selection), and multiuser diversity (via subcarrier assignment). We formulate the problem as a combinatorial optimization problem, which is NP-complete. To make it more tractable, we adopt a graph-based approach. We first establish the equivalence between the original problem and a maximum weighted clique problem (MWCP) in graph theory. A metaheuristic algorithm based on ant colony optimization (ACO) is then employed to find the solution in polynomial time. Simulation results demonstrate that the proposed protocol together with the ACO algorithm significantly enhances the system total throughput.

Interference alignment (IA) has evolved as a powerful technique in the information theoretic framework for achieving the optimal degrees of freedom of interference channel. In practical systems, the design of specific interference alignment schemes is subject to various criteria and constraints. In this paper, we propose novel transceiver schemes for the MIMO interference channel based on the mean square error (MSE) criterion. Our objective is to optimize the system performance under a given and feasible degree of freedom. Both the total MSE and the maximum per-user MSE are chosen to be the objective functions to minimize. We show that the joint design of transmit precoding matrices and receiving filter matrices with both objectives can be realized through efficient iterative algorithms. The convergence of the proposed algorithms is proven as well. Simulation results show that the proposed schemes outperform the existing IA schemes in terms of BER performance. Considering the imperfection of channel state information (CSI), we also extend the MSE-based transceiver schemes for the MIMO interference channel with CSI estimation error. The robustness of the proposed algorithms is confirmed by simulations.

We study the joint allocation of three types of resources, namely, power, subcarriers and relay nodes, in cooperative two-hop multi-relay OFDM systems. Each relay adopts the amplify-and-forward (AF) protocol. The objective is to maximize the system transmission rate subject to individual power constraints on each node. We formulate such a problem as a subcarrier-pair based resource allocation that seeks the joint optimization of subcarrier pairing, subcarrier-pair-to-relay assignment, and power allocation. Using a dual decomposition method, we solve this problem efficiently in an asymptotically optimal manner. We further propose two suboptimal algorithms to trade off performance for complexity. Simulation results demonstrate that the proposed subcarrier-pair based resource allocation schemes significantly outperform the symbol based benchmark scheme. Moreover, it is shown that subcarrier pairing plays an important role in improving the system performance.

We present a new physical layer network coding (PLNC) scheme, called superimposed XOR, for two-way relay channels. The new scheme specifically takes into account the channel as well as information asymmetry in the broadcast phase of two-way relaying. It is based upon both bitwise XOR and symbol-level superposition coding. We first derive its achievable rate regions when integrated with two known time control protocols over Gaussian channels. We then demonstrate its average maximum sum-rate and service delay performance over fading channels. Compared with the existing bitwise XOR and symbol-level superposition coding, the proposed superimposed XOR scheme achieves larger rate region in asymmetric channels. As a result, it performs much better in terms of averaged maximum sum-rate and service delay over fading channels. Numerical results also show that the proposed practical PLNC closely approaches the capacity bound given by the information-theoretic random binning.

With imperfect channel state information (CSI) acquired by channel estimation at the receiver, the performances of automatic-repeat-request (ARQ) systems are evaluated as a function of the accuracy of channel estimation. A link between network-layer performances and physical-layer parameters is therefore established. We study in particular the good-put and the accepted packet error rate as a function of the channel estimation mean square error (MSE) and the factors which affect the MSE. The results enable us to analyze the optimum allocation of energy for data transmission and energy for pilot channel estimation so as to maximize the good-put.

In most wireless relay networks, the source and relay nodes transmit successively via fixed time division (FTD) and each relay forwards a packet immediately upon receiving. In this paper we enable the buffering capability of relay nodes and propose a framework for joint scheduling and relay selection. The goal is to maximize the system long-term throughput by fully exploiting multi-user diversity in the network. We develop two joint scheduling and relay selection (JSRS) algorithms for unidirectional and bidirectional traffic, respectively. The novel cross-layer relay selection metrics which our algorithms are based upon take into account both instantaneous channel conditions and the queuing status. We also demonstrate that the proposed JSRS can be realized in a distributed way without explicit coordination among the network nodes. Extensive simulation is carried out to evaluate the performance of the proposed JSRS with buffering in comparison with traditional FTD without buffering. Typical throughput enhancements up to 101% and 110% are observed in one-way and two-way relay networks respectively, at low signal-to-noise ratio (0 dB).

Due to the half-duplex property of the underwater acoustic channels, the classic stop-and-wait ARQ (SW-ARQ) and its variants are generally thought to be the only class of ARQ protocols that can be applied in underwater. When combined with the large propagation delay property of the underwater acoustic channels, the use of SW-ARQ and its variants makes the throughput performance of underwater acoustic communication systems very inefficient. In this paper, we propose a transmission scheme that takes advantage of the long propagation delay in underwater to enable the use of continuous ARQ protocols over underwater acoustic channels. Simulation results show that our proposed transmission scheme allows much higher throughput to be achieved than both the classic SW-ARQ and its variants, even when simple continuous ARQ protocols are used.

In this paper, we propose a hybrid relay selection (HRS) scheme in a general cooperative network using differential modulation. In the HRS scheme, when the destination decodes successfully, the relay nodes will remain silent. Otherwise, optimal relay node has to be determined to make an additional transmission. In this process, all the relays are divided into two groups, referred to as an amplify-and-forward (AAF) relay group and a decode-and-forward (DAF) relay group depending on whether they can decode correctly or not. The relay, which has the maximum signal-to-noise ratio (SNR) at the destination, will be selected from both AAF and DAF relay groups. Simulation results show that the proposed relay selection scheme significantly outperforms the conventional AAF selection in terms of both frame error rate (FER) and throughput, and these performance gains considerably grow as the number of relay nodes increases.

Non-identical fading distribution in a multiple-input multiple-output (MIMO) channel, including unequal average channel gains and fade rates, often occurs when antennas are not co-located. In this paper, we present an analytical study of the effects of non-identical Rayleigh fading on the error performance of differential unitary space-time modulation (DUSTM). The fading processes for different transmit-receive antenna pairs are assumed to be independent and time-variant. We find that the maximum-likelihood (ML) differential detector of DUSTM over such channels is involved except for differential cyclic group codes. The conventional detector is proved to be asymptotically optimal in the limit of high signal-to-noise ratio (SNR) over static fading channels. Applying the distribution of quadratic forms of Gaussian vectors, we then derive closed-form expressions for the exact error probabilities of two specific unitary classes, namely, cyclic group codes and orthogonal codes. Simple and useful asymptotic bounds on error probabilities are also obtained. Our analysis leads to the following general findings: (1) equal power allocation is asymptotically optimal, and (2) non-identical channel gain distribution degrades the error performance. Finally, we also introduce a water-filling based power allocation to exploit the transmit non-identical fading statistics.

Multi-hop relaying is an economically efficient architecture for coverage extension and throughput enhancement in future wireless networks. OFDM, on the other hand, is a spectrally efficient physical layer modulation technique for broadband transmission. As a natural consequence of combining OFDM with multi-hop relaying, the allocation of per-hop subcarrier power and per-hop transmission time is crucial in optimizing the network performance. This paper is concerned with the end-to-end information outage in an OFDM based linear relay network. Our goal is to find an optimal power and time adaptation policy to minimize the outage probability under a long-term total power constraint. We solve the problem in two steps. First, for any given channel realization, we derive the minimum short-term power required to meet a target transmission rate. We show that it can be obtained through two nested bisection loops. To reduce computational complexity and signalling overhead, we also propose a sub-optimal algorithm. In the second step, we determine a power threshold to control the transmission on-off so that the long-term total power constraint is satisfied. Numerical examples are provided to illustrate the performance of the proposed power and time adaptation schemes with respect to other resource adaptation schemes.

We analyze the capacity of a multiple-input single- output system over Rayleigh fading channels. The channels are assumed to be independent and non-identically distributed. Simple, explicit and closed-form expressions of ergodic mutual information and outage probability are obtained. Moreover, two suboptimal but efficient analytical power allocation schemes for mutual information maximization and outage minimization are derived, respectively. In specific, for mutual information maximization, more power is assigned to those channels with higher channel variances, while for outage minimization the power allocation scheme follows the water-filling principle.

We present an analytical study on the error performance of differential unitary space-time modulation (DUSTM) over multiple-input multiple-output (MIMO) channels with non- identical fading statistics. The channel for each transmit-receive antenna pair is assumed to be independent, non-identically distributed (i.n.i.d), and time-varying Rayleigh fading. We first show that the maximum-likelihood (ML) differential detector of DUSTM over such channels is involved except for differential cyclic group codes. Applying the distribution of quadratic forms of Gaussian vectors, we derive closed-form expressions for the exact error probabilities of two specific unitary classes, namely, cyclic group codes and orthogonal codes. Simple and useful asymptotic bounds are also obtained. Our analysis leads to several general findings.

This paper is concerned with the end-to-end transmission outage in an OFDM based wireless multi-hop network. The network consists of a source node, a destination node, and a set of serial relay nodes. Transmission outage occurs when the end-to-end transmission rate is less than a target rate. Our goal is to find an optimal power and time control policy to minimize the outage probability at a target end-to-end data rate under a long-term total power constraint. The problem is solved in two steps. First, for any given channel realization, we derive the minimum total power required to meet the target rate by varying the transmission power and time allocated to each hop. This is formulated as a convex optimization problem and is solved efficiently. We then compare the required minimum total power for each channel realization with a power threshold. If the threshold is exceeded, the transmission is turned off. The optimal threshold is chosen to meet the long-term total power constraint. Two suboptimal power and time control policies are also presented and compared with the optimal policy by simulation.

We study the end-to-end resource allocation in an OFDM based multi-hop network consisting of a one-dimensional chain of nodes including a source, a destination, and multiple relays. The problem is to maximize the end-to-end average transmission rate under a long-term total power constraint by adapting the transmission power on each subcarrier over each hop and the transmission time used by each hop in every time frame. The solution to the problem is derived by decomposing it into two subproblems: short-term time and power allocation given an arbitrary total power constraint for each channel realization, and total power distribution over all channel realizations. We show that the optimal solution has the following features: the power allocation on subcarriers over each hop has the water-filling structure and a higher water level is given to the hop with relatively poor channel condition; meanwhile, the fraction of transmission time allocated to each hop is adjusted to keep the instantaneous rates over all hops equal. To tradeoff between performance, computational complexity and signalling overhead, three suboptimal resource allocation algorithms are also proposed. Numerical results are illustrated under different network settings and channel environments.

Most existing work on adaptive allocation of sub- carriers and power in multiuser orthogonal frequency division multiplexing (OFDM) systems has focused on homogeneous traffic consisting solely of either delay-constrained data (guaranteed service) or non-delay-constrained data (best-effort service). In this paper, we investigate the resource allocation problem in a heterogeneous multiuser OFDM system with both delay-constrained (DC) and non-delay-constrained (NDC) traffic. The objective is to maximize the sum-rate of all the users with NDC traffic while maintaining guaranteed rates for the users with DC traffic under a total transmit power constraint. Through our analysis we show that the optimal power allocation over subcarriers follows a multi-level water-filling principle; moreover, the valid candidates competing for each subcarrier include only one NDC user but all DC users. By converting this combinatorial problem with exponential complexity into a convex problem or showing that it can be solved in the dual domain, efficient iterative algorithms are proposed to find the optimal solutions. To further reduce the computational cost, a low-complexity suboptimal algorithm is also developed. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of service outage probability, achievable transmission rate pairs for DC and NDC traffic, and multiuser diversity.

For most wireless services with variable rate transmission, both average rate and rate oscillation are important performance metrics. The traditional performance criterion, utility of average transmission rate, boosts the average rate but also results in high rate oscillations. We introduce a utility function of instantaneous transmission rates. It is capable of facilitating the resource allocation with flexible combinations of average rate and rate oscillation. Based on the new utility, we consider the time and power allocation in a time-shared wireless network. Two adaptation policies are developed, namely, time sharing (TS) and joint time sharing and power control (JTPC). An extension to quantized time sharing with limited channel feedback (QTSL) for practical systems is also discussed. Simulation results show that by controlling the concavity of the utility function, a tradeoff between the average rate and rate oscillation can be easily made.

Average transmission rate and rate oscillation are two important performance metrics for most wireless services. Both are often needed to be optimized in multi-user scheduling and resource management. In this paper we introduce a utility function that increases with average rate but decreases with rate variance. It is capable of facilitating resource allocation with flexible combinations of the two performance metrics. A generalized gradient scheduling algorithm (GGSA) is then developed to maximize the proposed utility. It is shown that the best scheduler should maximize the sum of concave functions of instantaneous transmission rates in order to maximize the utility of average rate and rate oscillation. The scheduler reduces to the traditional gradient scheduling algorithm when the rate variance term in the new utility function is omitted. We analyze the dynamics of average transmission rates and rate variances using ordinary differential equation and show that GGSA is asymptotically optimal under the condition that the transmission rate vector, after an appropriate scaling, converges to a fixed vector as time goes into infinity.

The problem of non-cooperative power control is studied for wireless ad hoc networks supporting data services. We develop a maximum throughput based non-cooperative power control game (MT-NPG), where each transmitter node requires only theeffectiveinterferencemeasurement from its corresponding receiver. An expression for the optimal power control to maximize the average transmission rate for each individual link is derived. Then, we design a distributed iterative power updating algorithm to approximate the optimal solution. The existence and uniqueness of Nash equilibrium for the proposed game is investigated. Simulation results show that the proposed MT-NPG yields a significant improvement in the sum of average achievable rates when compared with existing approaches.

We study transmission schemes for lifetime maximization in wireless sensor networks. Specifically we consider the network where all the sensors observe uncorrelated signals and forward the quantized observations to a common fusion center. We demonstrate how the minimum node lifetime is maximized under individual estimation accuracy constraints through adapting transmission times and powers assigned to different sensor nodes. A partially distributed algorithm is proposed to implement joint time sharing and power control, for which each sensor node only needs local information and little common information. In addition, we obtain a necessary and sufficient condition for convergence of the algorithm. Simulation results show a significant lifetime gain over existing schemes, especially when the sensing environment becomes more heterogeneous and the number of nodes increases.

It is demonstrated that iteratively decoded variable length space time coded modulation (VL-STCM-ID) schemes are capable of simultaneously providing both coding gain as well as multiplexing and diversity gain. The VL-STCM-ID arrangement is a jointly designed iteratively decoded scheme combining source coding, channel coding, modulation as well as spatial diversity/multiplexing. In this contribution, we analyse the iterative decoding convergence of the VL-STCM-ID scheme using symbol-based three-dimensional EXIT charts. The performance of the VL-STCM-ID scheme is shown to be about 14.6 dB better than that of the fixed length STCM (FL-STCM) benchmarker at a source symbol error ratio of 10-4, when communicating over uncorrelated Rayleigh fading channels. The performance of the VL-STCM-ID scheme when communicating over correlated Rayleigh fading channels using imperfect channel state information is also studied.

For most of wireless services with variable rate transmission, both average rate and rate oscillation are important performance metrics. One often needs to decide how much rate oscillation the service can tolerate to obtain a higher average rate. Service satisfaction for each user is quantified by an increasing and concave utility function of instantaneous transmission rate. It is capable of facilitating the resource allocation with flexible combinations of average rate and rate oscillation. Particularly, we are interested in maximizing the time-average aggregate utility by scheduling user transmissions in a time-shared wireless network. A resource allocation policy is developed, namely, time sharing (TS), to exploit the concavity of utility function and the fluctuation of channel gain. This is formulated as a constrained convex optimization problem. Our analysis shows that in the TS policy the optimal scheduler allows multiple users with relatively better channel conditions to share a same time frame in an adaptive time-division manner. In addition, the more concave the utility function is, the higher the probability of time frame sharing is. An extension to quantized time sharing with limited channel feedback (QTSL) for practical systems is all studied. Simulation results show that, two to three bits of channel state information (CSI) are sufficient for the performance of QTSL scheme to approach that of the optimal TS policy when the number of time slots in a time frame is not less than the number of users, especially, in high SNR region.

This paper provides a comprehensive study on the error performance of noncoherent orthogonal M-ary frequency-shift-keying (FSK) signals with various diversity combining schemes over fading channels. The diversity branches are assumed to be independent and non-identically distributed (i.n.d) Rayleigh fading. Closed-form expressions for the symbol error probability with an arbitrary diversity order and any modulation level are obtained for optimal combining (OC), equal gain combining (EGC) and log-likelihood ratio based selection combining (SC-LLR). Our analytical results show that EGC performs closely to OC over slightly unbalanced channels, whereas for highly unbalanced channels SC-LLR performs more closely to OC than EGC.

We study the performance of differential orthogonal space-time block codes (OSTBC) over independent and semi-identically distributed block Rayleigh fading channels. In this semiidentical fading model, the channel gains from different transmit antennas to a common receive antenna are identically distributed, but the gains associated with different receive antennas are nonidentically distributed. Arbitrary fluctuation rates of the fading processes from one transmission block to another are considered. We first derive the optimal symbol-by-symbol differential detector, and show that the conventional differential detector is suboptimal. We then derive expressions of exact bit-error probabilities (BEPs) for both the optimal and suboptimal detectors. The results are applicable for any number of receive antennas, and any number of transmit antennas for which OSTBCs exist. For two transmit antennas, explicit and closed-form BEP expressions are obtained. For an arbitrary number of transmit antennas, a Chernoff bound on the BEP for the optimal detector is also derived. Our results show that the semi-identical channel statistics degrade the error performance of differential OSTBC, compared with the identical case. Also, the proposed optimal detector substantially outperforms the conventional detector when the channel fluctuates rapidly. But in near-static fading channels, the two detectors have similar performances

An iteratively decoded variable length space time coded modulation (VL-STCM-ID) scheme capable of simultaneously providing both coding and iteration gain as well as multiplexing and diversity gain is proposed. Non-binary unity-rate precoders are employed for assisting the iterative decoding of the VL-STCM-ID scheme. The discrete-valued source symbols are first encoded into variable-length codewords that are mapped to the spatial and temporal domains. Then the variable-length codewords are interleaved and fed to the precoder assisted modulator. More explicitly, the proposed VL-STCM-ID arrangement is a jointly designed iteratively decoded scheme combining source coding, channel coding, modulation as well as spatial diversity/multiplexing. As expected, the higher the source correlation, the higher the achievable performance gain of the scheme becomes. Furthermore, the performance of the VL-STCM-ID scheme is about 14.6 dB better than that of the fixed length STCM (FL-STCM) benchmarker at a source symbol error ratio of 10-4

A new space-time block coded transmit antenna selection scheme over spatially correlated fading channels is presented. This scheme allows two or more transmit antennas to simultaneously use one radio frequency frontend. A system with four transmit antennas is considered in particular. The four antennas are selectively grouped into two subsets. Alamouti code is then applied on top of the subsets as if each was a single antenna. This scheme is shown to be more efficient than the conventional transmit antenna selection combined with Alamouti code in correlated channels. Moreover, it lowers the bitrate of the feedback channel.

We study the performance of differential orthogonal space-time block codes over independent and semi-identically distributed Rayleigh fading channels. In this semi-identically distributed fading model, the channel gains from different transmit antennas to a common receive antenna are identically distributed, but the gains associated with different receive antennas are non-identically distributed. Arbitrary fluctuation rates of the fading processes from one transmission block to another are considered. We first derive the optimal symbol-by-symbol differential detector, and show that the conventional differential detector is suboptimal. We then derive expressions of exact bit error probabilities (BEP) for both the optimal and suboptimal detectors. The results are applicable for any number of receive antennas and any number of transmit antennas for which orthogonal space-time block codes exist. For two transmit antennas, explicit and closed-form BEP expressions are obtained. A Chernoff bound on the BEP of optimal detection for any number of transmit antennas is also derived.

Most existing work on adaptive allocation of subcarriers and power in multiuser OFDM systems has focused on homogeneous traffic consisting of delay-constrained data (guaranteed service) or delay-tolerant data (best-effort service) only. In this work, we investigate the resource allocation problem in a heterogeneous multiuser OFDM system with both delay-constrained (DC) and no-delay-constrained (NDC) traffic. The objective is to maximize the sum-rate of all the users with NDC traffic while maintaining guaranteed rates for the DC traffic under a total transmission power constraint. Finding the optimal allocation of subcarriers and power is formulated as a convex programming problem. An iterative algorithm is proposed to compute the optimal solutions numerically. A low-complexity suboptimal allocation algorithm is also presented. Simulation experiments are conducted to evaluate the performance of the proposed algorithms in terms of service outage probability and achievable transmission rate pairs for DC and NDC traffic.

We introduce a new trellis coded differential unitary space-time modulation scheme for multiple-antenna wireless systems. In the new scheme, the constellation expansion is executed using a super unitarity technique so that the number of available unitary matrices is increased but the transmitted symbol alphabet is kept the same. A novel set partitioning strategy is then applied to the expanded matrix set. This scheme avoids the rate-loss problem suffered by traditional code design. We provide some examples using the proposed code construction method. Compared with existing ones, the new codes can not only achieve lower transmitter and receiver complexity, but also provide higher coding gains

We present the architecture of generalized layered space-time codes (GLST) as a combination of Bell Labs layered space-time (BLAST) architecture and space-time coding (STC) in multiple-antenna wireless communication systems. This approach provides both spectral and power efficiency with moderate complexity. The framework is to partition all the available transmit antennas into groups and apply STC on each group as component codes. Based on the mappings from coded symbols to transmit antenna groups, we can construct different GLST systems. Particularly, horizontal mapping and diagonal mapping are introduced and referred to as HGLST and DGLST respectively. The basic decoding of GLST, under quasi-static flat Rayleigh fading environments and assuming perfectly known channel state information (CSI) at the receiver, combines group interference suppression and group interference cancellation techniques. As a result, the individual STC on each group is decoded serially. To improve the overall system performance, we derive the optimal power allocation among all space-time codewords without requiring the knowledge of CSI at the transmitter and suitable for all GLST systems. We also derive the optimal serial decoding order based on the channel realizations at the receiver for HGLST systems without power allocation. Simulation results show that both can provide much improvement. To further enhance the system performance, we propose a low complexity hard-decision iterative decoding method. This method efficiently exploits full receive antenna diversity and, hence, dramatically improves the system performance which is confirmed by simulation.

Coding and modulation for multiple-antenna systems have gained much attention in wireless communications. This paper investigates a noncoherent trellis-coded scheme based on differential unitary space-time modulation when neither the transmitter nor the receiver know the channel. In a time-varying flat Rayleigh fading environment, we derive differentially noncoherent decision metrics and obtain performance measures for systems with either an ideal interleaver or no interleaver. We demonstrate that with an ideal interleaver, the system performance is dominated by the minimum Hamming distance of the trellis code, while without an interleaver, the performance is dominated by the minimum free squared determinant distance (a novel generalization of the Euclidean distance) of the code. For both cases, code construction is described for Ungerboeck-type codes. Several examples that are based on diagonal cyclic group constellations and offer a good tradeoff between the coding advantage and trellis complexity are provided. Simulation results show that, by applying the soft-decision Viterbi decoder, the proposed scheme can achieve very good performance even with few receive antennas. Extensions to trellis-coded differential space-time block codes are also discussed.

This paper considers trellis-coded differential unitary spacetime modulation for multiple-antenna systems when neither the transmitter nor the receiver has access to channel state information. The channel is assumed to be slowly time-varying flat Rayleigh fading with ideal interleaver. We derive the metric for maximum likelihood differential decoding and the Chernoff bound for pairwise error probability. We show that performance is dominated by the minimum Hamming distance which contributes linearly to the order of diversity. Code construction is described for rate m/(m + 1) trellis codes. Several code examples are provided based on diagonal cyclic group constellations for two transmit antennas with data rate 1 and 2 bits/s/Hz. Simulation results show that the proposed trellis-coded differential scheme can achieve high data rate communications by medium signal-to-noise ratio without channel estimation.

We propose a new differential modulation scheme for multiple-antenna systems based on square space-time block codes (STBC) when neither the transmitter nor the receiver knows channel state information. Compared with the known differential unitary space-time modulation (DUSTM), the proposed constellations generally have multiple amplitudes and do not have group properties. This generalization potentially allows the spectral efficiency to be increased by carrying information not only on orientations (or phases) but also on amplitudes. Two non-coherent decoders, optimal differential decoder (DD) and near-optimal DD, are derived for flat Rayleigh fading channels. Particularly, the near-optimal DD inherits the decomposition decoding property retained by STBC with coherent receiver and thus has linear complexity. Compared with the best-known cyclic group constellations designed for DUSTM, our proposed constellations with the near-optimal DD have significantly lower probability of error and lower decoding complexity.

We consider a generalized layered space-time (GLST) architecture with parallel space-time (ST) codes and derive an optimal power allocation (PA) scheme over the space-time encoders to minimize the overall frame error rate. We prove that the optimal power allocation scheme will equate the derivatives of the frame error rate functions of all the ST codes. From simulation, we show that the derived optimal power allocation has a 1-2 dB gain over the equal power scheme. Also, the proposed scheme has a better performance than the power allocation scheme proposed by Tarokh, Naguib, Seshadri and Calderbank (see IEEE Trans. Inform. Theory, vol.45, no.4, p.1121-28, 1999). Since various power allocation schemes have the same receiver structure, the gain can be achieved simply using the optimized parameters with no increase in complexity.

In this paper we refer to the combination of BLAST and space-time coding (STC) for multi-input multi-output systems as generalized layered space-time coding (GLST). Post-ordered decoding algorithm is introduced based on the generalization of the original BLAST ordering detection algorithm. The performance is analyzed through comparison to that of pre-ordered decoding with and without power allocation. Interleavered GLST with a new iterative process is also proposed. It can efficiently exploit full receive antenna diversity and thus significantly improve the system overall performance. Due to hard interference cancellation (IC) and ML decoder in the iterations, the new iterative decoding is much less complex than conventional turbo processing where soft IC and MAP decoder were applied.