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.

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.

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.

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.

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.

The modeling and analysis of large-scale stochastic MEC systems are of great significance in providing useful design guidelines for practical MEC networks. In most prior works, the users generally adopt the same strategy to select appropriate MEC access points (MAPs), where the fact that the available mobile computing services are different among the randomly distributed users is ignored. To this end, this paper proposes a user-centric cooperative offloading scheme to enable more flexible and efficient user task offloading. Specifically, each user can be served by one or two MAPs based on both the communication performance and computing performance. To evaluate the performance gains acquired from the proposed task offloading scheme, we first derive the service mode assignment probability, link distance distribution, and interference intensity to capture the network characteristics. Further, the moment and the meta distribution of the task transmission performance are analyzed. Based on the above results, we focus on the distribution of the computation workload to evaluate the edge computing service capability. From the analytical and simulation results, it is demonstrated that compared with the non-cooperative offloading scheme, the proposed task offloading scheme not only achieves more reliable and fair task offloading but also increases the computing service capacity.

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm. In the pre-training stage, we introduce a multi-modal two-tower model and a corresponding contrastive learning method to integrate the explicit description of the scattering environment and implicit channel state information (CSI) into a universal representation, which encapsulates rich high-level knowledge and can be leveraged for all downstream tasks in different scenarios. Additionally, we present two specially designed model structures to enhance the interaction of communication modalities. In the second stage, based on the frozen pre-trained model, we propose a direct method and a pluggable method for flexible and low-cost task adaptation. Experimental results demonstrate that our proposed approach significantly outperforms benchmarks in both task performance and tuning parameter size for exemplary sub-tasks in unseen scenarios.

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.

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.

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.

Short-packet communications are applied to various scenarios where transmission covertness and reliability are crucial due to the open wireless medium and finite blocklength. Nonetheless, transmission covertness and reliability present a dilemma and significantly limit the throughput for short-packet communications. Intelligent reflection surface (IRS) is utilized in this paper to address this issue by enabling channel reconstruction, thereby releasing the fundamental limit between transmission covertness, reliability, latency (blocklength), and throughput. Specifically, to reveal the benefits brought by IRS, the achievable throughput without IRS is derived first in terms of the transmission covertness requirement, reliability requirement, and blocklength. Subsequently, the achievable throughput with IRS is derived, which releases the fundamental limit among the above-mentioned performance metrics. Besides, considering the phase uncertainty at IRS and limited channel estimation overhead, the cases where the warder/IRS knows instantaneous/statistical channel state information (CSI) of warder-related/legitimate links are all discussed, elucidating the impacts of CSI availability on the throughput. Furthermore, numerical results verify the accuracy of analytical results and demonstrate the benefits provided by IRS. Especially, the achievable covertness and reliability performance can be enhanced while the minimum required blocklength can be reduced by introducing IRS, which is crucial for short-packet communications.

Wireless short-packet communications pose challenges to the reliability and security of the transmission. Besides, the proactive warder compounds these challenges, who detects and interferes with the potential transmission. Thus, the tradeoff among reliability, covertness, latency (blocklength) and transmission rate is crucial, and efficient system design schemes are required for short-packet communications against the proactive warder. To address these issues, we investigate the reliable and covert performance of above systems. Specifically, detection error probabilities and their approximations are derived for cases where the warder knows the instantaneous/statistical channel state information. Besides, the decoding error probability is derived. The asymptotic relationship among the detection/decoding error probability, blocklength and transmission rate is established, revealing the non-trivial tradeoff between reliability and covertness performance. Furthermore, to maximize the effective throughput, an optimization framework is proposed under reliability and covertness constraints. Numerical results verify the tightness of the approximations and the feasibility of the optimization framework. Furthermore, it is shown that the proactive warder severely degrades the throughput compared to the passive one. And longer blocklength is always beneficial to improve the throughput for systems with optimized transmission rates, whereas the optimal blocklength is not necessarily the maximum one when transmission rates are fixed.

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.

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.

Covert communication is crucial to ensure the safety of wireless communications in Internet of Things (IoT) systems. In this article, a multiantenna relay is employed to enhance the communication link and avoid the transmission being detected by the multiantenna warder simultaneously. Considering the dynamic fluctuating fading channels of IoT systems, a novel adaptive coordinated direct and relay transmission (ACDRT) scheme is proposed where the relay switches on/off adaptively to maximize the achievable covert rate. The covertness constraint requirements are derived with instantaneous and statistical warder-related channel state information based on the availability of the channel information in practical systems. Since both the direct and the relay links impact the system performance, the optimization problem is formulated, where the beamforming vectors are coupled. A semidefinite relaxation-based line search method is proposed to address this problem. Besides, the globally optimal solutions can be obtained by the proposed method, which is rigorously proved mathematically. In addition, the conditions for achieving a positive covert rate are analyzed with the multiantenna warder. Simulations demonstrate that the performance of the ACDRT is robust to covertness requirements when the positive rate condition holds, and significant covert rate gains can be obtained by the ACDRT with the multiantenna relay compared with the conventional systems.

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.

The stochastic geometry-based modeling and analysis of large-scale mobile edge computing (MEC) networks are vital for the effective configuration of MEC networks. In this paper, we develop a meta-analytical framework for MEC-enabled heterogeneous networks with the communication-computation-aware (CCA) user association mechanism. Compared with the communication-based user association mechanisms in most existing works, the CCA user association mechanism can capture the impacts of network computation capability on the association process between the user and MEC access point, at the expense of dealing with the more complex coupling of communication and computing. Given the need for interference characterization, we first derive the essential prerequisite quantities (i.e., per-tier association probability, link distance distribution, interferer process intensity, etc.) to represent the computation-dependent interference model. Further, the moment and the meta distribution of the task success offloading probability are derived, based on which we investigate the task execution latency performance, including the communication latency, local computing latency, and edge computing latency. By theoretical analysis and simulation results, it is demonstrated that the proposed analytical framework can provide accurate fine-grained network information for the MEC-enabled HetNets. Moreover, we elaborate on the impacts of the edge computation capability on the network performance and reveal important tradeoffs of the performance metrics.

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.

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.

Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation.

Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes only consider the maximum throughput or fairness of the system and do not consider the freshness of information. In this paper, we propose a novel D2D links scheduling scheme to minimize the average age of information (AoI) of wireless D2D communications. It is motivated by the fact that the more links are activated, the greater the interference with each other, which reduces the probability of successful transmission and in turn increases the AoI. We thus derive the accurate expression of the overall average AoI of the network based on the transmission success probability under the interfering channels. Moreover, a neural network structure is proposed to learn the mapping from the geographic location to the minimum AoI scheduling under a stationary randomized policy, where the scheduling decision can be made without estimating the channel state information. Finally, numerical results reveal that the performance of the deep learning approach is close to that of a local optimal algorithm which has a higher computational complexity.

Edge computing has been regarded as an efficient approach to achieve the multiplayer interactive virtual reality (VR) game over wireless networks, where the game scenes can be rendered at the edge computing server and then the real-time video frames can be transmitted to the players. In order to ensure the fairness of the interacting players, we allocate the edge computing and wireless bandwidth resources for minimizing the average inter-player delay among different players. The proposed programming is under the constraints of the absolute delay requirements, the maximum frame per second (FPS) demands, the total bandwidth and rendering resources limits. The influence of prediction and pre-rendering field of views (FOVs) is also considered in the optimization problem to model the interaction among players. To tackle the non-convex problem efficiently, a sub-optimal algorithm which convert the original problem into several convex subproblems to optimize iteratively is designed. Finally, numerical results verify the proposed algorithm can reduce the average interplayer delay significantly with lower complexity, and it also reveals the impact of the content sizes and the channel state conditions on the edge resource allocation.

A novel bidirectional computation task model has emerged as an important use case of 5G. For example, interactive AR/VR gaming service needs to render the live scene by jointly computing user features such as 3D positions and video data generated from the Internet. In this article, we consider the bidirectional computation task model, where each task is served via three mechanisms, i.e., local computing with local caching, local computing without local caching, and computing at the mobile edge computing server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. In the homogeneous scenario, we derive the optimal policy and analytical expression for the minimum bandwidth. In the heterogeneous scenario, to reduce the computation complexity of the NP-hard problem, we relax some constraints of the original problem and propose a Lagrangian relaxation (LR) suboptimal solution, which may be infeasible. We then reformulate the original problem as an auxiliary problem based on the LR solution and solve this via Concave-Convex Procedure (CCCP), which outputs feasible local optimal solution. Simulation has shown that LR-based algorithms outperform the baselines including greedy and CCCP algorithms in the bandwidth performance and time efficiency.

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.

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.

With the advent of the modern mobile traffic, e.g., online gaming, augmented reality delivery and etc., a novel bidirectional computation task model where the input data of each task consists of two parts, one generated at the mobile device in real-time and the other originated from the Internet proactively, is emerging as an important use case of 5G. In this paper, for ease of analytical analysis, we consider the homogeneous bidirectional computation task model in a mobile edge network which consists of one mobile edge computing (MEC) server and one mobile device, both enabled with computing and caching capabilities. Each task can be served via three mechanisms, i.e., local computing with local caching, local computing without local caching and computing at the MEC server. To minimize the average bandwidth, we formulate the joint caching and computing optimization problem under the latency, cache size and average power constraints. We derive the closed-form expressions for the optimal policy and the minimum bandwidth. The tradeoff among communications, computing and caching is illustrated both analytically and numerically, which provides insightful guideline for the network designers.

We study the sensitivity of multi-user scheduling performance to channel magnitude misreporting in systems with massive antennas. We consider the round-robin scheduler combined with max-min and waterfilling power controls, respectively. We show that user scheduling combined with power allocation, in general, dampens the negative effect of channel misreporting compared to the purely physical layer analysis of channel misreporting without scheduling. We discover several interesting results. First, we observe a periodicity in rate-loss behavior as the number of misreporting users increases. Second, we find that the waterfilling power control is more robust to channel misreporting compared with max-min power control. Third, for homogeneous users with equal average signal-to-noise ratios (SNRs), channel underreporting is harmful but overreporting is beneficial for max-min power control; the opposite impact is found for waterfilling power control. For heterogeneous users with various average SNRs, however, both underreporting and overreporting harm the system for both power control policies, demonstrating the complex interactions across network layers due to channel misreporting.

The mixed analog-to-digital converters (ADC) architecture is a promising solution to the problem of high energy consumption of multiple-input multiple-output (MIMO) systems. In such a scheme, part of the received signals at the base station are quantized by high-resolution ADCs, while the others are quantized by one-bit ADCs. The signals quantized by the mixed-ADC architecture are correlated and non-identically distributed, which is different from the one-bit quantized MIMO systems. Moreover, unlike the previous works focusing on the achievable rate of mixed-ADC MIMO systems under linear detectors, we derive a closed-form expression of the ergodic mutual information between the transmit signals and the quantized outputs of the mixed-ADC architecture over Rayleigh channels, where the statistical property of the equivalent channel is characterized by random matrix theory. Then, we derive the power scaling law, which shows that reducing the transmit power will not sacrifice the transmission efficiency when the number of receive antennas increases. Furthermore, for any given number of antennas and user transmit power, the optimal number of one-bit ADCs for maximizing the energy efficiency is obtained. Moreover, constrained by a given hardware energy consumption, the theoretical mutual information shows obvious gains over the one of unquantized MIMO systems for low user transmit power.

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.

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.

Mobile edge computing (MEC) is an emerging architecture to enable variety of innovative applications and services with ultra low latency at the resource-limited mobile devices. In this paper, we investigate how the communication resources and the computing resources, including mobile users and MEC servers, interact with each other in multi-cell MEC-enabled stochastic wireless networks. To this end, the MEC-enabled network model including mobile users with limited storage capacity and computing capabilities is considered, which is characterized in random node distribution, dynamic traffic, orthogonal frequency division multiple access and task retransmission mechanism. Based on the model, the two-dimensional discrete Markov chain is employed to characterize the task execution process. We derive the stationary distribution of the buffer length and outage probability by combining the queuing theory and stochastic geometry, based on which the radio access network throughput is calculated to measure the network performance. Extensive simulations have been conducted to verify the effectiveness of the proposed offloading strategy and to provide valuable insight.

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.

Multi-User scheduling is challenging due to the channel unbalance problem, which leads to system performance degradation. In this paper, two optimal multi-user scheduling schemes maximizing the system throughput are proposed for the fixed and adaptive power transmission scenarios of the full-duplex (FD) multi-user buffer-aided relay system, respectively. Independent and non-identically distributed (i.ni.d.) model is used to characterize the unbalanced channels of different links. In particular, the optimal weight factor of each pair is designed based on the statistical channel state information in both scenarios. With the weight factors, the proposed schemes are able to balance the throughput gaps between different links. In addition, we propose an optimal power allocation scheme with closed-form expressions under average power constraint for the adaptive power transmission scenario. By combining the optimal weight factor and the power allocation scheme, novel optimal selection function is obtained to facilitate the selection process. Considering the specific i.ni.d. Rayleigh fading, the system throughput is further derived in both cases. Theoretical analysis is verified by the numerical simulations and the results demonstrate the superiority of the proposed schemes.

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.

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.

It is starting to become a big trend in the era of social networking that people produce and upload user-generated contents to the Internet via wireless networks, bringing a significant burden on wireless uplink networks. In this paper, we contribute to designing and theoretical understanding of wireless cache-enabled uplink transmission in a delay-tolerant small cell network to relieve the burden, and then propose the corresponding scheduling policies for the small base station (SBS) with a limited cache size. Specifically, the cache ability introduced by the SBS enables the SBS to eliminate the redundancy among the upload contents from users. This strategy not only alleviates the wireless backhaul traffic congestion from the SBS to a macro base station (MBS) but also improves the transmission efficiency from users to the SBS. We then investigate the scheduling scheme to offload more data traffic under caching size constraint. Finally, numerical results are provided to demonstrate the significant performance gains of the proposed wireless cache-enabled upload network.

The robustness of system throughput with scheduling is a critical issue. In this paper, we analyze the sensitivity of multi-user scheduling performance to channel misreporting in systems with massive antennas. The main result is that for the round-robin scheduler combined with max-min power control, the channel magnitude misreporting is harmful to the scheduling performance and has a different impact from the purely physical layer analysis. Specifically, for the homogeneous users that have equal average signal-to-noise ratios (SNRs), underreporting is harmful, while overreporting is beneficial to others. In under-reporting, the asymptotic rate loss on others is derived, which is tight when the number of antennas is huge. One interesting observation in our research is that the rate loss “periodically” increases and decreases as the number of misreporters grows. For the heterogeneous users that have various SNRs, both underreporting and overreporting can degrade the scheduler performance. We observe that strong misreporting changes the user grouping decision and hence greatly decreases some users' rates regardless of others gaining rate improvements, while with carefully designed weak misreporting, the scheduling decision keeps fixed and the rate loss on others is shown to grow nearly linearly with the number of misreporters.

In this paper, we mainly analyze the inter-cell interference (ICI) and its impacts on system performance for cellular networks with dynamic traffic, where base stations (BSs) do not remain active all the time. This is quite different from the assumption in previous works that base stations (BSs) are full-loaded, which overestimates the interference. To ensure the packet transmission, automatic repeat request (ARQ) protocol is adopted and packets will remain at the head of the buffer until they are successfully transmitted. Retransmissions bring a coupled relationship between the ICI and the buffer states, i.e., the packet transmission is impaired by the ICI, and packets will be blocked at the buffer to be retransmitted, which aggravates the interference in turn. To better reveal this coupled relationship, a queuing model is established to analyze the packet arrival and departure processes. With the aid of the probability generating function (PGF) and the Laplace transform, both the outage probability and the buffer empty probability are derived.

Wireless multicast and client caching are two promising approaches to provide scalable video-on-demand (VoD) services. In this paper, we consider VoD services with client pre-caching under asynchronous requests, where a client request watches the desired video from the beginning to the end. A requesting client can retrieve the data of the requested video from its local cache, other clients it meets via device-to-device (D2D) communication, and the BS multicast transmission. Under this model, we optimize the cache placement to minimize the average traffic rate of BS transmission by the optimal exploitation of BS multicast and cooperative caching. Specifically, we first formulate the cache placement problem into a separable concave minimization problem with convex constraints, which can be solved via convex underestimation and branch bound, where the global optimality is achieved at the cost of a very high computational complexity. To reduce the complexity, we further adopt the difference of convex algorithm. Moreover, two special cases are investigated, i.e., without D2D communication and without BS multicast. Simulation results verify the effectiveness of the proposed scheme which combines BS multicast and cooperative caching.

Hierarchical modulation (HM), a promising non-orthogonal multiple access scheme, is able to provide different levels of protection for data streams and achieve a rate region that cannot be realized by traditional orthogonal schemes. In this paper, we propose a novel HM system with non-uniform constellation inputs and analytically derive the bit-interleaved coded modulation (BICM)-capacity for each user by means of the mutual information. Different from the power optimization method for the superimposed uniform constellation, we directly optimize the constellation positions in conjunction with the optimal binary labels design for HM in additive white Gaussian noise channels. For maximizing the BICM-capacity, we propose a fairness balanced criterion for HM system by considering both power constraints and user QoS constraints. Multi-start interior-point algorithm is proposed to deal with the constellation optimization problems. Numerical results show that the proposed HM verify the performance gains of optimized HM compared with the optimized quadrature amplitude modulation (QAM) based HM and other orthogonal transmission methods.

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.

Sparse code multiple access (SCMA) is a promising non-orthogonal air-interface technology for its ability to support massive connections. In this paper, we design the multiuser codebook and the advanced decoding from the perspective of the theoretical capacity and the system feasibility. First, different from the lattice-based constellation in point-to-point channels, we propose a novel codebook for maximizing the constellation constrained capacity. We optimize a series of 1-D superimposed constellations to construct multi-dimensional codewords. An effective dimensional permutation switching algorithm is proposed to further obtain the capacity gain. Consequently, it shows that the performance of the proposed codebook approaches the Shannon limit and achieves significant gains over the other existing ones. Furthermore, we provide a symbol-based extrinsic information transfer tool to analyze the convergence of SCMA iterative detection, where the complex codewords are considered in modeling the a priori probabilities instead of assuming the binary inputs in previous literature. Finally, to approach the capacity, we develop a low-density parity-check code-based SCMA receiver. Most importantly, by utilizing the EXIT charts, we propose an iterative joint detection and decoding scheme with only partial inner iterations, which exhibits significant performance gain over the traditional one with separate detection and decoding.

Mobile edge computing (MEC) is expected to be an effective solution to deliver 360-degree virtual reality (VR) videos over wireless networks. In contrast to previous computation-constrained MEC framework, which reduces the computation-resource consumption at the mobile VR device by increasing the communication-resource consumption, we develop a communications-constrained MEC framework to reduce communication-resource consumption by increasing the computation-resource consumption and exploiting the caching resources at the mobile VR device in this paper. Specifically, according to the task modularization, the MEC server can only deliver the components which have not been stored in the VR device, and then the VR device uses the received components and the corresponding cached components to construct the task, resulting in low communication-resource consumption but high delay. The MEC server can also compute the task by itself to reduce the delay, however, it consumes more communication-resource due to the delivery of entire task. Therefore, we then propose a task scheduling strategy to decide which computation model should the MEC server operates, in order to minimize the communication-resource consumption under the delay constraint. Finally, we discuss the tradeoffs between communications, computing, and caching in the proposed system.

The hybrid half-duplex/full-duplex (HD/FD) relaying scheme is an effective paradigm to overcome the negative effects of the self-interference incurred by the full-duplex (FD) mode. However, traditional hybrid HD/FD scheme does not consider the diversity gain incurred by the multiple antennas of the FD node when the system works in the HD mode, leading to the waste of the system resources. In this paper, we propose a new hybrid HD/FD relaying scheme, which utilizes both the antennas of the FD relay node for reception and transmission when the system works in the HD mode. With multiple antennas, the maximum ratio combining/maximum ratio transmission is adopted to process the signals at the relay node. Based on this scheme, we derive the exact closed-form system outage probability and conduct various numerical simulations. The results show that the proposed scheme remarkably improves the system outage performance over the traditional scheme, and demonstrate that the proposed scheme can more effectively alleviate the adverse effects of the residual self-interference.

In this paper, we address the multi-user scheduling problem of the multi-user two-way full-duplex (FD) decode-and-forward relay system. According to the availability of the channel state information (CSI) and system state information (SSI), three scheduling schemes are investigated in terms of the system outage performance. Specifically, for the non-CSI case, we analyze the random scheduling scheme, which serves as a baseline for comparison. For the full CSI case, we propose the Max-Min scheduling scheme, which is theoretically proved to be sub-optimal in the low signal-to-interference-plus-noise ratio (SINR) regime but optimal in the high SINR regime. To minimize the outage probability, we propose the optimal scheduling scheme, which involves the full SSI. In addition, the exact closed-form outage probability expressions corresponding to different schemes are derived under the general independent but not identically distributed channels. The residual self-interference incurred by the FD mode is taken into account. Moreover, independent and identically distributed channels are also considered as special cases. Finally, numerical simulations are performed to corroborate the theoretical results. The results reveal that the Max-Min scheduling and optimal scheduling schemes significantly improve the outage performance compared with the random scheduling scheme.

Due to the explosive growth in multimedia traffic demand, scalability and delivery efficiency of video-on-demand (VoD) services have become important issues. In this paper, we develop a joint cache allocation and multicast delivery scheme to minimize the average bandwidth consumption of VoD services with client caching under the zero-delay constraint. Specifically, we first propose a client caching enabled multicast patching (CCE-MP) mechanism which achieves the minimum bandwidth consumption given a certain cache allocation. Then we formulate the cache allocation problem into a convex problem, which can be effectively solved by a water-filling algorithm. In addition to the traditional full access pattern where clients watch the video entirely, we further consider the interval access patterns with uniformly distributed endpoints and fixed-size intervals. The impact of different access patterns on the cache allocation algorithm is also investigated. Simulation results indicate that the proposed scheme significantly outperforms other existing approaches, e.g., more than 50% bandwidth saving under a certain setting.

Caching at the base stations (BSs) has been widely adopted to reduce the delivery delay and alleviate the backhaul traffic between BSs and the core network. In this paper, we consider a collaborative content caching scheme among BSs in cache-enabled multi-cell cooperative networks, where the requested contents can be obtained from the associated BS, the other collaborative BSs or the core network. Novelly, we model the stochastic request traffic and derive a closed form expression for the average delay per request based on multi-class processor sharing queuing theory. We then formulate a cooperative caching optimization problem of minimizing the average delay under the finite cache size constraint at BSs and show it to be at least NP-complete. Furthermore, we prove it equivalent to the maximization of a monotone submodular function subject to matroid constraints, allowing us to adopt the common greedy algorithm with 1/2 performance guarantee. A heuristic greedy caching strategy is also developed, achieving a better performance than the conventional greedy solution. Simulation results verify the accuracy of the analytical results and demonstrate the performance gains obtained by our proposed caching scheme.

In this paper, we devise the optimal caching placement to maximize the offloading probability for a two-tier wireless caching system, where the helpers and a part of users have caching ability. The offloading comes from the local caching, D2D sharing and the helper transmission. In particular, to maximize the offloading probability we reformulate the caching placement problem for users and helpers into a difference of convex (DC) problem which can be effectively solved by DC programming. Moreover, we analyze the two extreme cases where there is only help-tier caching network and only user-tier. Specifically, the placement problem for the helper-tier caching network is reduced to a convex problem, and can be effectively solved by the classical water-filling method. We notice that users and helpers prefer to cache popular contents under low node density and prefer to cache different contents evenly under high node density. Simulation results indicate a great performance gain of the proposed caching placement over existing approaches.

This paper considers a multi-pair two-way relaying network, where multiple pairs of full-duplex users are served via a full-duplex relay with large-scale antennas under the amplify-and-forward protocol. Based on maximum-ratio combining/maximum-ratio transmission (MRC/MRT) processing, an exact achievable rate expression is derived by utilizing the statistical distribution of the channels to detect the desired signals, with the case of a finite number of relay antennas. We show that the exact expression is a tight lower bound of the ergodic rate. Then we obtain its approximate closed-form, which keeps very close to the exact one, especially in large number of antennas. The proposed approximate expression indicates that increasing the relay antenna number can greatly improve the sum rate. In addition, the optimal transmit powers of the users and the relay are obtained in order to maximize the achievable rate. Moreover, we demonstrate that with very large antenna arrays, the two-way full-duplex relaying scheme outperforms both the one-way full-duplex relaying scheme and the two-way half-duplex relaying scheme. Finally, numerical results are presented to verify the accuracy of the analysis.

In this paper, we consider a multipair amplify-and-forward two-way relay channel, where multiple pairs of full-duplex users exchange information through a full-duplex relay with massive antennas. For improving the energy efficiency, four typical power-scaling schemes are proposed based on the maximum-ratio combining/maximum-ratio transmission (MRC/MRT) and zero-forcing reception/zero-forcing transmission (ZFR/ZFT) at the relay. When the number of relay antennas tends to infinity, we quantify the asymptotic spectral efficiencies and energy efficiencies for the proposed power-scaling schemes. We show that the loop interference can be reduced by decreasing the transmit power under massive relay antennas. Besides, the inter-pair interference and inter-user interference in such systems can also be eliminated in large number of antennas. Moreover, we analytically compare the performance between MRC/MRT and ZFR/ZFT, and describe the impact of the number of user pairs on the spectral efficiency. We also evaluate the energy efficiency performance based on the practical power consumption model, and depict the impact of the relay antenna number on the energy efficiencies for the proposed schemes. Furthermore, we provide the available regions where full-duplex systems can outperform half-duplex systems. Finally, we show that the proposed schemes achieve good performance tradeoffs between the spectral efficiency and the energy efficiency.

By pushing popular contents directly to users through broadcast networks while serving individual requests using cellular networks, a wireless converged network provides a highly effective means to cope with the wireless traffic bottleneck. In this paper, we analyze the network capacity and the pushing and caching schemes for the push-based converged network with limited user storage. Both uniform and non-uniform storage cases are investigated. Specifically, we first derive the converged network capacity under the traditional popularity-based pushing and caching scheme. Furthermore, we establish push-limited and cache-limited regions for the uniform case and an additional transitional region for the non-uniform case, to reveal the constraining factor on the network capacity growth. Moreover, the optimal pushing and caching schemes are investigated from both the network and user perspectives. It is shown that both network and user criterion reduce to the same offloading optimization problem, which can be modeled as the 0-1 knapsack problem and effectively solved by the greedy algorithm. We point out that the optimal scheme depends on not only the traffic load, but also the region where the system operates. Finally, the numerical results are provided to confirm the accuracy of the developed analytical results.

In this paper, a wireless energy harvesting and information transfer protocol in cognitive relay networks is investigated, where an energy harvesting secondary network shares the spectrum as well as harvests energy by assisting the primary transmission. Specifically, a secondary transmitter scavenges energy from the received primary signal and then employs the harvested energy to forward the resulting signals along with the secondary signal. The secondary receiver can also harvest the ambient energy, and use the remaining signal to cancel the primary interference. We analytically derive the exact expressions of the outage probabilities for both primary and secondary networks. The rate-energy tradeoff between the ergodic capacity and harvested energy in the secondary network is also discussed. Furthermore, to quantify the energy consumption, we investigate the system energy efficiency. Moreover, we address the optimization power allocation strategy under three performance criteria and theoretically prove that the resulting nonconvex optimization problems can be converted into biconvex problems. The corresponding effective algorithms are then developed to solve the optimization problems. Numerical results show that the proposed protocol not only achieves both the primary and secondary transmissions but also harvests the ambient energy.

Caching popular multimedia content is a promising way to unleash the ultimate potential of wireless networks. In this paper, we propose and analyze cache-based content delivery in a three-tier heterogeneous network (HetNet), where base stations (BSs), relays, and device-to-device (D2D) pairs are included. We advocate proactively caching popular content in the relays and parts of the users with caching ability when the network is off-peak. The cached content can be reused for frequent access to offload the cellular network traffic. The node locations are first modeled as mutually independent Poisson point processes (PPPs) and the corresponding content access protocol is developed. The average ergodic rate and outage probability in the downlink are then analyzed theoretically. We further derive the throughput and the delay based on the multiclass processor-sharing queue model and the continuous-time Markov process. According to the critical condition of the steady state in the HetNet, the maximum traffic load and the global throughput gain are investigated. Moreover, impacts of some key network characteristics, e.g., the heterogeneity of multimedia contents, node densities, and the limited caching capacities, on the system performance are elaborated on to provide valuable insight.

By pushing popular contents directly to users through broadcast networks while serving individual requests using the cellular networks, a “converged” wireless network provides a highly effective means to cope with the wireless traffic bottleneck. In this paper, we contribute to analyze the network capacity of the push-based converged network with limited user storage and then develop the corresponding optimal pushing schemes. Specifically, we first establish two operational regions for the converged network, namely, the push-limited region and the cache-limited region, to reveal the fundamental tradeoff between the broadcast pushing ability and the user caching capacity. The network capacity is then derived under the traditional popularity-based pushing scheme. Moreover, we investigate the optimal pushing scheme under two different criteria, i.e., to maximize either the offloading probability or the offloading data percent. The pushing optimization problem is modeled as the 0-1 knapsack problem, and can be easily solved by the greedy algorithm. Interestingly, the optimal scheme depends on not only the optimization objective, but also the region where the system operates. Finally, numerical results are provided to confirm the accuracy of the developed analytical results.

Pushing and caching the popular multimedia data is a promising way to unleash the ultimate potential of wireless networks. In this paper, we contribute to proposing and analyzing the push-based and cache-based content delivery in a three-tier heterogeneous network, where base stations, relays and device-to-device (D2D) pairs are included. We propose to proactively push the popular contents to the relays and parts of the users with caching ability via broadcasting when the network is off-peak. The cached contents can be reused for frequent access to offload the cellular network traffic. The node locations are first modeled as mutually independent Poisson Point Processes (PPPs) and the corresponding content access protocol is developed. The average ergodic rates in the downlink are then analyzed theoretically. We further derive the global throughput gain and the impact of network characteristics on the system performance is illustrated. Simulation results show that the proposed system has significant performance gain, especially for the high density network.

In this paper, we consider a one-pair amplify-and-forward relay channel, where two full-duplex users exchange information through a full-duplex relay with a very large number of antennas, while each user only has two antennas. Several power-scaling schemes are proposed based on the maximum-ratio combining/maximum-ratio transmission (MRC/MRT) at the relay. When the number of the relay antennas tends to infinity, we derive and quantify the asymptotic spectral efficiency of the proposed power-scaling schemes. We show that the very large antenna array can significantly reduce the effect of loop interference because of the antenna array gain. Furthermore, the theoretical results indicate that the loop interference can also be reduced by cutting down the transmission power, which is verified by the numerical results. The analytical and numerical results show that the proposed power-scaling schemes can achieve good performance tradeoffs between the spectral efficiency and the energy efficiency.

In this paper, we consider a multi-pair two-way amplify-and-forward relay interference channel, where multiple pairs of full-duplex users exchange information through a full-duplex relay with very large number of antennas, while each user only has two antennas. In order to save the transmission power, four typical power scaling schemes are proposed based on the maximum-ratio combining/maximum-ratio transmission (MRC/MRT) at the relay without the degradation of the performance. When the number of the relay antennas tends to infinity, the asymptotic achievable sum rates based on the proposed power scaling schemes are derived. We show that the very large antenna array can significantly reduce the effect of loop interference because of the antenna array gain. Furthermore, the theoretical results indicate that the loop interference can be reduced by cutting down the transmission power. Besides, the inter-pair interference and the inter-user interference in such systems can also be eliminated in the regime of very large number of antennas. Moreover, we analytically compare the upper bounds of the sum rates for the proposed power scaling schemes and describe the impact of the number of user pairs on the upper bounds.

We investigate a two-way full-duplex (TWFD) amplify-forward (AF) relay cooperative communication system, of which all transceiver nodes operate in full-duplex (FD) mode. Considering that the self-interference (SI) introduced by the Co-time Co-frequency transceiver is closely related to the local transmitting power, we view the average power of residual self-interference (RSI) as the transmitting power multiplied by one coefficient representing the SI cancelation capability quantitatively. We analytically derive the exact expressions of the outage probabilities for the forward and backward links. Besides, we investigate the achievable rate which can hardly be obtained in closed-form, however, an approximate expression for it as one upper bound is given by the Jensen's inequality. Numerical results are also provided to verify the validity of theoretical analysis.

Pushing and caching the popular multimedia data can effectively reduce the redundant content transmission and cope with the explosive traffic growth. In this paper, we contribute to analyzing the push-based and cache-based content delivery in the heterogeneous network (HetNet). When the network load is off-peak, the most popular contents can be pushed to the relays and the cache-enabled users via broadcasting, and then be cached down to be reused for frequent access. The locations of the base stations, relays and device-to-device pairs are first modeled as mutually independent Poisson Point Processes (PPPs). Then the energy consumption is derived based on the access protocol and the tier association priority. The impacts of network characteristics such as the heterogeneity of multimedia contents, node densities and the limited caching capacity are further elaborated. Besides, the node densities allocation strategy is discussed for full use of the caching resources, which provides valuable insights on the wireless intelligent network planning.

In this paper, the joint design of power allocation and mapping strategy is developed to maximize the achievable uplink rate for MIMO two-way relay channels (TWRCs) with finite-alphabet inputs. In contrast with the traditional water-filing scheme based on Gaussian inputs, the optimal power allocation for constellation constrained MIMO TWRCs does not only depend on the channel gain, but also on the mapping strategy. The proposed scheme creates multiple non-interference parallel channels in the uplink phase, and multiple independent physical-layer network coding (PNC) streams are implemented over those channels. We then derive the achievable uplink rate in terms of power allocation and mapping strategy. To maximize the achievable rate, the optimal power allocation is obtained for a given mapping strategy, which reduces the joint design problem to one-dimension search of the mapping strategy. Numerical results demonstrate that for the finite-alphabet inputs, the proposed scheme significantly outperforms the traditional water-filling scheme, e.g., 3 dB gain over 8-PSK 4 × 4 MIMO TWRCs.

In this paper, we investigate the shaping loss in a two-user Gaussian multiple-access channel (MAC) with network coding. We conjecture that the shaping loss in this system model is much larger than 1.53 dB, verified by simulation results. To recover such large shaping loss, we consider the combination of physical-layer network coding with constellation shaping, and propose a physical-layer shaped network coding (PLSNC) scheme. Exploiting the constellation shaping, the proposed PLSNC scheme enables the network codeword to be shaped and the relay recovers the network codeword from the shaped network codeword instead of decoding individual node's shaping bits. We thus investigate the design criteria for constructing a non-linear shaping code to avoid the ambiguous detection. The corresponding decoding algorithm of shaped network codeword is then presented to calculate the likelihood of the information about the transmitted shaped network codeword based on the bit-interleaved coded modulation (BICM) scheme. Furthermore, we derive the achievable rate of the proposed scheme. The simulation results show that the proposed scheme with 16-PAM can achieve a maximum shaping gain of 2.61 dB.

Energy harvesting is an efficient method for extending the lifetime of energy-constrained networks. In this paper, we develop a wireless energy harvesting and information transfer protocol in cognitive two-way relay networks, in which a secondary network scavenges energy from ambient signals of primary network while shares the spectrum by assisting the primary transmission. In particular, two primary users exchange information through an energy harvesting secondary user which firstly harvests energy from the received primary signals and then uses the harvested energy to forward the remaining primary signals along with the secondary signals. The exact expressions of the outage probabilities for the primary network are analytically formulated. Besides, we derive the lower and upper bounds of the outage probability for the secondary network. Following aforementioned deduction, we analyze the energy efficiency of the whole system. Simulation results show that we can achieve maximum energy efficiency if we set up proper parameters. Finally, numerical results verify our theoretical derivation and demonstrate that the proposed protocol enables the high-quality transmission for both the primary and secondary network without extra relay energy consumed.

We investigate a wireless energy harvesting and information transfer protocol in cognitive relay networks, where an energy harvesting secondary network shares the spectrum as well as harvests energy by assisting the primary transmission. In particular, the secondary transmitter scavenges energy from the received primary signal and then forwards the resulting signals along with the secondary signal. The secondary receiver can also harvest the ambient energy, and use the remaining signal to remove the primary interference. We analytically derive the exact expressions of the outage probabilities for both primary and secondary networks. Based on the proposed protocol, we analyze the rate-energy trade-off between the maximum ergodic capacity and the maximum harvested energy in the secondary network. Our results demonstrate that the proposed protocol not only achieves both the primary and secondary transmissions but also harvests the ambient energy without the performance loss.

Storing the popular contents in the caches to enable frequency reuse, wireless cooperative caching approach offers an exciting new way to unleash the ultimate potential of wireless networks. In this paper, we formulate the optimal caching problem to minimize the energy consumption in the wireless cooperative caching network by considering some important constraints, including the limited storage capacity, the content popularity and the content access protocol. A sub-optimal caching strategy is then proposed to handle the optimal content placement in the caches, reducing the energy consumption. Simulation results show that the proposed sub-optimal caching scheme has significant energy saving compared with the random caching scheme. Furthermore, the impact of the network resource and parameters on the system performance is also investigated in terms of the energy saving and the cache hit rate.

In this paper, we present and analyze spectrum and energy efficient coded modulation schemes for two-way relay channels. Depending on how the side information is utilized, two relay-to-destination decode-and-forward approaches are investigated. The first one, termed joint modulation relaying (JMR), jointly modulates two source messages by applying side information at the modulator and the demodulator. The second one, termed bit-cooperative coded modulation relaying (BCCMR), is a new joint coded modulation scheme for coded relay systems. In particular, a new constellation mapping for the JMR is proposed. The proposed constellation mapping is a many-to-one function and can be considered as a new form of network coding operation, in which the network coding is performed on the redundancy labeling of each constellation point. We establish the corresponding asymptotic optimal constellation labeling criterion by formulating the constellation constrained capacity with side information. For the BCCMR, a novel transceiver structure based on systematic low-density parity-check (LDPC) codes is developed. The joint coding-modulation design introduces the benefits of increased minimum Euclidean distance and the Hamming distance by jointly exploiting side information in the demapping and the decoding. The advantages of the LDPC-coded BCCMR and the JMR are analyzed using the density evolution method.

In this paper, we propose a multi-level physical-layer network coding (MPLNC) scheme that optimizes the relay performance for both symmetric and asymmetric traffic in a Gaussian two-way relay channel. The proposed MPLNC scheme enables each source to employ multiple linear binary codes for encoding, one per modulation level, and the relay node to decode superimposed network codewords at each modulation level. We first derive the achievable rate for the transmission of arbitrary constellations and then prove that MPLNC with multistage decoding (MPLNC/MSD) can achieve the achievable rate if binary code rates are properly chosen for both sources. Furthermore, the design criteria for the proposed MPLNC scheme is investigated, which includes the rate design rule and the labeling strategy. Moreover, we derive the error exponent and an upper bound of the overall error probability for MPLNC. Our analysis and simulation results show that MPLNC/MSD has a significant performance advantage in comparison to the existing bit-interleaved coded modulation (BICM)-based PLNC scheme.

The fast growing wireless data traffics have brought a significant burden on the mobile cellular network and would soon cause severe congestion in the near future. In this paper, we contribute to the design and theoretical understanding of push-based content delivery in a converged broadcasting and cellular network to relieve the burden. Specifically, we are interested in analyzing the scheme in which the most popular contents are pushed through broadcasting to alleviate the cellular data bottleneck. This strategy not only offloads the multimedia traffics from the cellular network, but also improves the user experience by eliminating download waiting time. To evaluate the performance gains of the converged network, we first develop a mathematic framework to model the converged network, the multimedia content characteristics and the mobile user behaviors. The improvement of the network capacity is then derived and quantified theoretically. Furthermore, we obtain valuable insights on the impact of the network resource and parameters on the system performance. Numerical results are provided to confirm the accuracy of the developed analytical results and to show the significant performance advantages of the converged network over the cellular-only network in terms of the system power consumption and users' quality of service (QoS).

Bit-cooperative coded modulation (BCCM), known as an improved bit-interleaved coded modulation (BICM) scheme [1], introduces known bits to jointly design the channel coding and modulation. In this paper, we contribute to the theoretical analysis of the turbo-coded BCCM scheme, and specifically, the impact of the known bits on the turbo decoding process. The characteristic of the iteration process is also analyzed for BCCM by the EXIT chart. Furthermore, to describe the fundamental tradeoff between the communication reliability and the data rate, we derive the error exponent for BCCM. Numerical results show that BCCM has a larger exponent than BICM and achieves a considerable gain on AWGN channels.

In this paper, we present and analyze high efficiency joint coded modulation schemes for two-way relay channels. Depending on how the side information is utilized, two relay-to-destination decode-and-forward approaches are investigated. The first one, termed joint modulation relaying (JMR), jointly modulates two source messages by applying the side information at the modulator and the demodulator. The second one, termed bit-cooperative coded modulation relaying (BCCMR), is a new joint coded modulation scheme for coded relay systems. In particular, we propose new constellation mapping for the JMR and establish the corresponding optimal constellation labeling criterion. For the BCCMR, the joint coding-modulation design introduces the benefits of increased minimum Euclidean distance and the Hamming distance by exploiting the side information in the demapping and decoding. The advantages of the low-density parity-check (LDPC)-coded BCCMR and JMR are analyzed using the density evolution approach. Numerical results are provided to illustrate the significant gains for the symmetric and asymmetric relaying on Rayleigh fading channels.

In this paper, we propose the multi-level physical-layer network coding (MPLNC) for two-way relay channels (TWRC) to optimize the relay performance. In the proposed MPLNC scheme, each source node involves multiple linear binary codes for encoding, one per modulation level. The relay node receives these streams of signals and attempts to decode superimposed network codewords at each modulation level. We first derive the constellation constrained capacity for TWRC, and then prove that MPLNC can approach the channel capacity if and only if binary code rates are properly chosen for both source nodes. Furthermore, to facilitate practical implementation MPLNC, the design criteria for the proposed MPLNC scheme is investigated, which includes the rate design rule, the decoding order design rule and the labeling strategy design rule. Also discussed is the relation between MPLNC and bit-interleaved coded modulation (BICM)-based PLNC. Our analysis and simulation results indicate that MPLNC has a significant performance advantage in comparison to the BICM-based PLNC.

A new coded modulation technique, termed bit-cooperative coded modulation (BCCM), is proposed in this paper. By introducing a pseudo-random sequence at the transmitter to systematic low-density parity-check (LDPC) codes, the receiver enjoys the benefits of increased minimum Euclidean distance and the Hamming distance when applying this side-information at the receiver. In particular, we propose a new constellation mapping principle to take advantage of the side information. Our analysis indicates that the side information makes the regular LDPC code irregular, with irregularity specified by the check node degree distribution, which allows the density evolution approach to be used to design and optimize the regular LDPC code for the BCCM. We then analytically derive the constellation constrained capacity for the BCCM. The performance of the LDPC-coded BCCM on AWGN channels and Rayleigh fading channels is analyzed using the density evolution and extensive simulations.

Network coding (NC), in principle, is a Layer-3 innovation that improves network throughput in wired networks for multicast/broadcast scenarios. Due to the fundamental differences between wired and wireless networks, extending NC to wireless networks generates several new and significant practical challenges. Two-way information exchange (both symmetric and asymmetric) between a pair of 802.11 sources/sinks using an intermediate relay node is a canonical scenario for evaluating the effectiveness of Wireless Network Coding (WNC) in a practical setting. Our primary objective in this work is to suggest pragmatic and novel modifications at the MAC and PHY layers of the 802.11 protocol stack on a Software Radio (SORA) platform to support WNC and obtain achievable throughput estimates via lab-scale experiments. Our results show that network coding (at the MAC or PHY layer) increases system throughput-typically by 20-30%.

In this paper, we present an in-band on-channel (IBOC) broadcasting system suitable for digital audio and data broadcasting in FM and AM channels. The new system utilizes a combination of frequency hopping, LDPC coding, hierarchical modulation and band aggregation techniques to increase the data rate, improve spectrum efficiency, and at the same time, provide spectrum flexibility to meet the requirements of high quality audio and multimedia services. A prototype has been developed on a personal computer (PC) based software defined radio (SDR) platform. Numerical results and initial laboratory tests demonstrate significant performance advantages of the new design over existing systems such as the Digital Radio Mondiale (DRM) and Hybrid Digital (HD) radio.

In this paper, we consider a two-way asymmetric relaying channel with unmatched traffics from two sources. Aiming at maximizing the sum rate under a minimum rate constraint, we address the optimization problem (in terms of time and power allocation) for two decode-and-forward (DF) strategies, namely, the decode-and-forward with joint modulation (DF-JM) and the decode-and-forward with network-superposition coding (DF-NSC). Closed-form solutions under different relay settings are presented. Numerical results are provided to illustrate the performance gains due to the optimal resource allocation, as well as the achievable rate gaps between two different DF strategies.

We consider a two-way relay system where two sources exchange information with the assistance of multiple, non-cooperative, relay nodes. Specifically, we propose a capacity upper bound for the two-way relay channel and derive the achievable regions for different relay strategies, such as the amplify-and-forward (AF), the decode-and-forward (DF) and the compress-and-forward (CF). Furthermore, the upper bound and the achievable regions for the additive white Gaussian noise channel are presented. Numerical results are provided to illustrate how the capacity varies with respect to the number of relay nodes, the distance between the relays and the sources, and the transmission powers.

Cooperative base station for downlink multiple-input–multiple-output (MIMO) system is known as a critical radio technology for worldwide interoperability for microwave access (WiMAX) communications. This study proposes to use zero-forcing beamforming for cooperative base stations in a downlink multicell MIMO system. It is well known that beamforming requires perfect knowledge of channel state information (CSI) at the transmitter, and in practice the perfect CSI may not be available because of channel estimation errors. In this study, using approximate capacity loss analysis the authors are able to analyse the effect of channel estimation errors on system capacity in a cooperative base station system with zero-forcing beamforming. A power allocation policy is proposed to reduce the capacity loss under per base station power constraints. Numerical results show that the approximate capacity loss is very close to the real capacity loss, which can be reduced with the help of the power allocation policy.