Next-generation wireless networks are expected to develop a novel paradigm of integrated sensing and communications (ISAC) to enable both the high-accuracy sensing and high-speed communications. However, conventional mono-static ISAC systems, which simultaneously transmit and receive at the same equipment, may suffer from severe self-interference, and thus significantly degrade the system performance. To address this issue, this paper studies a multi-static ISAC system for cooperative target localization and communications, where the transmitter transmits ISAC signal to multiple receivers (REs) deployed at different positions. We derive the closed-form of weighted sum Cramér-Rao bound (CRB) on the joint estimations of both the transmission delay and Doppler shift for cooperative target localization, and the weighted sum CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements for the REs. To solve this problem, we first decouple it into two subproblems for RE selection and transmit beamforming, respectively. Then, a minimax linkage-based method is proposed to solve the RE selection subproblem, and a successive convex approximation algorithm is adopted to deal with the transmit beamforming subproblem with non-convex constraints. Finally, numerical results validate our analysis and reveal that our proposed multi-static ISAC scheme achieves better ISAC performance than the conventional mono-static ones with ideal SI cancellation when the number of cooperative REs is large.

Recently, scalable video coding (SVC) and scalable semantic coding (SSC) have emerged as effective solutions for supporting online video streaming in applications such as remote cockpits and telemedicine. Although the significance of SVC and SSC is well established, the underlying principles related to their performance bounds have not been thoroughly investigated. To address this issue, we study the performance bounds for online SVC/SSC in this paper. Initially, to evaluate coding performance, we propose a new metric referred to as effective coding gain (ECG), which jointly considers the entropy of the source data and the mutual information between the source and the encoded video data. Next, from the perspective of information theory, we derive a closed-form expression for the ECG bound while accounting for the impacts of diverse SVC/SSC coding structures. Our results not only ensure that the performance bounds can be efficiently evaluated but also provide valuable insights for resource allocation, cost optimization, performance evaluation and comparison, as well as for understanding the optimization space of existing systems.

Space-air-ground uniformly integrated network (SAGUIN), which integrates the satellite, aerial, and terrestrial networks into a unified communication architecture, is a promising candidate technology for the next-generation wireless systems. Transmitting on the same frequency band, higher-layer access points (AP), e.g., satellites, provide extensive coverage; meanwhile, it may introduce significant signal propagation delays due to the relatively long distances to the ground users, which can be multiple times longer than the packet durations in task-oriented communications. This phenomenon is modeled as a new “ripple effect”, which introduces spatiotemporally correlated interferences in SAGUIN. This paper studies the task scheduling problem in SAGUIN with the ripple effect, and formulates it as a Markov decision process (MDP) to jointly minimize the age of information (AoI) at users and energy consumption at APs. The obtained MDP is challenging due to high dimensionality, partial observations, and dynamic resource constraints caused by ripple effect. To address the challenges of high dimensionality, we reformulate the original problem as a Markov game, where the complexities are managed through interactive decision-making among APs. Meanwhile, to tackle partial observations and the dynamic resource constraints, we adopt a modified multi-agent proximal policy optimization (MAPPO) algorithm, where the actor network filters out irrelevant input states based on AP coverage and its dimensionality can be reduced by more than an order of magnitude. Simulation results reveal that the proposed approach outperforms the benchmarks, significantly reducing users' AoI and APs' energy consumption.

Reconstructing 3D scenes and synthesizing novel views from sparse input views is a highly challenging task. Recent advances in video diffusion models have demonstrated strong temporal reasoning capabilities, making them a promising tool for enhancing reconstruction quality under sparse-view settings. However, existing approaches are primarily designed for modest viewpoint variations, which struggle in capturing fine-grained details in close-up scenarios since input information is severely limited. In this paper, we present a diffusion-based framework, called CloseUpShot, for close-up novel view synthesis from sparse inputs via point-conditioned video diffusion. Specifically, we observe that pixel-warping conditioning suffers from severe sparsity and background leakage in close-up settings. To address this, we propose hierarchical warping and occlusion-aware noise suppression, enhancing the quality and completeness of the conditioning images for the video diffusion model. Furthermore, we introduce global structure guidance, which leverages a dense fused point cloud to provide consistent geometric context to the diffusion process, to compensate for the lack of globally consistent 3D constraints in sparse conditioning inputs. Extensive experiments on multiple datasets demonstrate that our method outperforms existing approaches, especially in close-up novel view synthesis, clearly validating the effectiveness of our design.

Semantic communications (SemComs) have emerged as a promising paradigm for joint data and task-oriented transmissions, combining the demands for both the bit-accurate delivery and end-to-end (E2E) distortion minimization. However, current joint source-channel coding (JSCC) in SemComs is not compatible with the existing communication systems and cannot adapt to the variations of the sources or the channels, while separate source-channel coding (SSCC) is suboptimal in the finite blocklength regime. To address these issues, we propose an adaptive source-channel coding (ASCC) scheme for SemComs over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical deep JSCC and SSCC schemes for both the single- and parallel-channel scenarios while maintaining full compatibility with practical digital systems.

This paper considers a multi-user semantic and data communication (MU-SemDaCom) system, where a base station (BS) simultaneously serves users with different semantic and data tasks through a downlink multi-user multiple-input single-output (MU-MISO) channel. The coexistence of heterogeneous communication tasks, diverse channel conditions, and the requirements for digital compatibility poses significant challenges to the efficient design of MU-SemDaCom systems. To address these issues, we propose a multi-user adaptive source-channel coding (MU-ASCC) framework that adaptively optimizes deep neural network (DNN)-based source coding, digital channel coding, and superposition broadcasting according to the channel conditions. First, we employ a data-regression method to approximate the end-to-end (E2E) semantic and data distortions, for which no closed-form expressions exist due to the complex coupling between DNN-based source coding and channel codes. The obtained logistic formulas decompose the E2E distortion as the addition of the source and channel distortion terms, in which the logistic parameter variations are task-dependent and jointly determined by both the DNN and channel parameters. Then, based on the derived formulas, we formulate a weighted-sum E2E distortion minimization problem that jointly optimizes the source-channel coding rates, power allocation, and beamforming vectors for both the data and semantic users. Finally, an alternating optimization (AO) framework is developed, where the adaptive rate optimization is solved using the subgradient descent method, while the joint power and beamforming is addressed via the uplink-downlink duality (UDD) technique. Simulation results demonstrate that, compared with the conventional separate source-channel coding (SSCC) and deep joint source-channel coding (DJSCC) schemes that are designed for a single task, the proposed MU-ASCC scheme achieves simultaneous improvements in both the data recovery and semantic task performance.

This paper proposes an adaptive source-channel coding (ASCC) scheme for point-to-point digital semantic communications over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical separate and deep joint source-channel coding schemes while maintaining full compatibility with practical digital systems.

The space-air-ground uniformly integrated network (SAGUIN), which integrates space, aerial, and ground networks, is a promising architecture for next-generation wireless systems. Transmitting on the same frequency band, higher-layer access points (AP), such as satellites, provide extensive coverage; meanwhile, it may introduce significant signal propagation delays due to the relatively long distances to the ground users, which can be multiple times longer than the packet durations. This phenomenon is modeled as a new “ripple effect”, which introduces spatiotemporally correlated interference. This paper formulates the task scheduling problem in SAGUIN with ripple effect as a Markov decision process (MDP) to jointly minimize the age of information (AoI) at users and energy consumption at APs. To handle partial observations and dynamic resource constraints caused by ripple effect, we reformulate the problem as an equivalent Markov game and solve it with a modified multi-agent proximal policy optimization (MAPPO) algorithm, reducing input state dimensionality based on AP coverage. Simulation results show that the proposed approach significantly reduces AoI and energy consumption compared to the benchmarks.

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This article investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an$O(1/V)$versus$O(V)$tradeoff for the average AoI and average service delay.

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (D2-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive prior model is designed to encode semantic features according to their distributions. Second, channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.

Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.

Recently, scalable video coding (SVC) has gained significant recognition in mobile video streaming because it can adapt bitstreams to time-varying transmission conditions. However, the coding performance of SVC, which is determined by its coding structure, has not been thoroughly studied. To address this issue, we propose analyzing the redundancy, reduction, distortion, and mutuality of video information within the video coding processes. This analysis facilitates the development of a novel information-theoretical framework for quantifying coding performance, which includes an information theory (IT)-based quantification method and a graphical representation system. The representation system accurately delineates the coding reference structure for encoding each video frame, while the proposed method utilizes mutual information to quantify the achievable coding performance of SVC under the delineated structure. To demonstrate the significance of our research, we apply the proposed framework to encode a basic coding unit, showcasing its effectiveness in improving SVC schemes. Consequently, our framework not only provides an efficient approach for quantifying the coding performance of SVC but also serves as an invaluable tool for optimizing SVC in various applications.

Multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). Multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module. The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO; and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones in terms of applicability, effectiveness, and robustness, and achieves comparable performance to the derived upper bound.

This two-part paper studies a point-to-point resonant beam communication (RBCom) system, where two separately deployed retroreflectors are adopted to generate the resonant beam between the transmitter and the receiver, and analyzes the transmission rate of the considered system under both the quasi-static and mobile scenarios. Part I of this paper focuses on the quasi-static scenario where the locations of the transmitter and the receiver are relatively fixed. Specifically, we propose a new information-bearing scheme which adopts a synchronization-based amplitude modulation method to mitigate the echo interference caused by the reflected resonant beam. With this scheme, we show that the quasi-static RBCom channel is equivalent to a Markov channel and can be further simplified as an amplitude-constrained additive white Gaussian noise channel. Moreover, we develop an algorithm that jointly employs the bisection and exhaustive search to maximize its capacity upper and lower bounds. Finally, numerical results validate our analysis. Part II of this paper discusses the performance of the RBCom system under the mobile scenario.

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This paper investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drift and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an O(1/V) versus O(V) tradeoff for the average AoI and average service delay.

This paper studies a multi-static integrated sensing and communications (ISAC) system for multi-user downlink communications and cooperative target sensing, where the base station transmits ISAC signal and the users deployed at different positions receive it. We analyze the joint estimation of transmission delay and Doppler shift of cooperative users for target sensing, and derive the corresponding Cramér-Rao bound (CRB) in closed form. Then, a CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements among these users. To solve this problem, we propose a minimax linkage-based cooperative method for user selection, and then design an approximation non-convex transforming algorithm for transmit beamforming. Finally, simulation results validate our analysis and reveal significant performance improvement of our proposed methods over the state-of-the-art benchmarks.

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications with high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding ($\mathrm{D}^{2}$-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive density model is designed to efficiently extract and encode semantic features according to their different distributions. Second, channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates. Then to minimize the E2E distortion, we propose an efficient two-step algorithm to find the optimal trade-off between the source and channel rates for a given channel signal-to-noise ratio (SNR). Via experiments on simulating the $\mathbf{D}^{2}$-JSCC with different channel codes and real datasets, the proposed framework is observed to outperform the classic deep JSCC and separation-based approaches.

This paper studies a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the ISAC transmitter sends excitation signals to power multiple passive backscatter devices (BD), and the ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is characterized by the Cramér-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, a CRB minimization problem is formulated by considering the communication rate constraint, and is shown to be non-convex in general. To solve this problem, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the localization and communication performances.

This paper studies the radio map reconstruction by exploiting the point cloud environmental information. We propose a deep-learning-based model, named Neural Reflectance and Attenuation Fields (NeRA), to represent the attenuation and the reflectance functions to describe the influence of the environment on electromagnetic waves. NeRA learns these two functions directly from the voxelized point cloud of the real environment using three multilayer perceptrons (MLPs) and involves the real reflecting process in the neural network cost function design. NeRA models interactions between the radio frequency (RF) signal and the environment by learning and utilizing the reflectance and attenuation fields, which enables NeRA to reconstruct the radio map precisely even in the scene where both the transmitter and receiver are movable. Experiments show that NeRA outperforms Neural Radio-Frequency Radiance Fields (NeRF2) and traditional ray-tracing methods in reconstructing radio maps with less training data, achieving a 0.6 dB (26%) improvement in mean absolute error compared to NeRF2when trained with only 20% of the measured data.

This work focuses on utilizing point cloud data to enhance the accuracy of channel impulse response (CIR) predictions in channel knowledge maps (CKMs). We propose a method, named Point Selector, which uses a set of co-foci ellipsoids to identify reflection and scattering objects in the point cloud. Then, we employ a PointNet neural network that directly utilizes the point cloud from Point Selector to predict the CIR. To validate our method, we employ a multi-view scene reconstruction technique to obtain the point cloud and collect CIR measurements in the real world. Experiments reveal that the proposed method identifies reflection and scattering objects more accurately, and significantly improves CIR prediction to achieve a root mean squared error of 2.88 dB compared to 8.68 dB by using traditional ray tracing methods.

This article considers the problem of distributed estimation in wireless sensor networks (WSNs), which is anticipated to support a wide range of applications, such as the environmental monitoring, weather forecasting, and location estimation. To this end, we propose a joint model and data driven distributed estimation method by designing the optimal quantizers and fusion center (FC) based on the Bayesian and minimum mean square error (MMSE) criterions. First, universal mean square error (MSE) lower bound for the quantization-based distributed estimation is derived and adopted as the design metric for the quantizers. Then, the optimality of the mean-fusion operation for the FC with MMSE criterion is proved. Next, by exploiting different levels of the statistic information of the desired parameter and observation noise, a joint model and data driven method is proposed to train parts of the quantizer and FC modules as deep neural networks (DNNs), and two loss functions derived from the MMSE criterion are adopted for the sequential training scheme. Furthermore, we extend the above results to the case with multibit quantizers, considering both the parallel and one-hot quantization schemes. Finally, simulation results reveal that the proposed method outperforms the state-of-the-art schemes in typical scenarios.

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become a pivotal issue in semantic communications. This article proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data-oriented semantic communications (JTD-SCs) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this problem and reformulating the reward function based on channel statistics, we first simplify the solution space, state space, and optimality criteria, and convert it to an equivalent Markov game, for which the large discrete action space issue is greatly relieved. Then, we propose a Whittle’s index guided multi-agent proximal policy optimization (WI-MAPPO) algorithm to solve the considered game, where the embedded Whittle’s index module further shrinks the action space, and the proposed offline training algorithm extends the training kernel of conventional MAPPO to address the issue of time-varying constraints. Finally, numerical results validate that the proposed algorithm significantly outperforms state-of-the-art age of information (AoI) based algorithms under scenarios with insufficient channel resources.

This two-part paper focuses on the system design and performance analysis for a point-to-point resonant beam communication (RBCom) system under both the quasi-static and mobile scenarios. Part I of this paper proposes a synchronization-based information transmission scheme and derives the capacity upper and lower bounds for the quasi-static channel case. In Part II, we address the mobile scenario, where the receiver is in relative motion to the transmitter, and derive a mobile RBCom channel model that jointly considers the Doppler effect, channel variation, and echo interference. With the obtained channel model, we prove that the channel gain of the mobile RBCom decreases as the number of transmitted frames increases, and thus show that the considered mobile RBCom terminates after the transmitter sends a certain number of frames without frequency compensation. By deriving an upper bound on the number of successfully transmitted frames, we formulate the throughput maximization problem for the considered mobile RBCom system, and solve it via a sequential parametric convex approximation (SPCA) method. Finally, simulation results validate the analysis of our proposed method in some typical scenarios.

In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of Things (IoT) via integrating 6G-oriented Time-sensitive Networks (TSN). Utilizing dispersive local data, industrial IoT devices distributively train a deep neural network (DNN) model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSN is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on deep reinforcement learning, to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge, where a global model is trained iteratively through the collaboration of the edge devices without sharing their data. However, due to the relatively large communication cost between the devices and parameter server (PS), direct computing based on the information from the devices may not be resource efficient. This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

This paper studies the performance trade-off in a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the multi-antenna ISAC transmitter sends excitation signals to power multiple single-antenna passive backscatter devices (BD), and the multi-antenna ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is measured by the Cramér-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, to characterize the trade-off between the localization and communication performances, the CRB minimization problem with the communication rate constraint is formulated, and is shown to be non-convex in general. By exploiting the hidden convexity, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the CRB and sum transmission rate achieved by our proposed method.

This paper proposes a deep separate source-channel coding (DSSCC) scheme for the semantic-aware image transmission, where image is lossily compressed and transmitted to receiver for recovery and processing certain semantic tasks. To improve the compression efficiency, the forward adaption (FA) method is incorporated into the DSSCC scheme to capture the density information of compressed features as side information. For a typical application of image classification task, we derive a novel rate-distortion optimization problem by analyzing the Bayesian model of the FA-based DSSCC framework. Then, a variational autoencoder approach is proposed to effectively compress image for semantic-aware transmission by minimizing the proposed rate-distortion problem. Simulation results reveal that the proposed FA-based DSSCC scheme achieves better image recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

This paper considers smart-duplex (SD) powered user-centric ultra dense networks (UC-UDN), which shifts the conventional access point-centric paradigm to the user-centric one by de-cellular concept, to provide good quality-of-service for a large number of users via flexibly designing the user association, power allocation, and duplex mode. The maximization average ratio of satisfied users for the considered SD UC-UDN in the long-term time scale is firstly formulated as a Markov decision process (MDP) problem with large discrete action space. To reduce the action space, the user association and power allocation processes are modeled as a two-layer tree structure, and then selecting an action is equivalent to finding the path from root to one of the leaf nodes of the tree. A multi-agent tree-structured policy gradient (MATSPG) based deep reinforcement learning (DRL) algorithm is proposed to solve this problem by directly mapping the action space for user association and power allocation to the two layers of the tree, respectively, whose training is shown to be equivalent to the training of neural networks on two-layer paths. The time and space complexity for searching one action in the proposed MATSPG is also proved to be lower than other conventional DRL algorithms. Simulations show that the proposed MATSPG algorithm significantly improves the average ratio of the satisfied users than the conventional DRL methods in typical scenarios.

Ultra dense networks (UDNs) with smart-duplex (SD), which allows the base stations (BSs) to flexibly switch between the half-duplex (HD) and full-duplex (FD), are expected to support high-density transmissions. However, to centrally handle a large network is costly, while distributed processing may suffer from the severe performance loss due to the complicated intercell interferences in the UDNs. This article aims to balance the system performance and clustering cost of the SD UDNs by dividing all small cells into several clusters. A Markov decision process (MDP) problem is formulated to maximize the average weighted sum of network throughput and clustering cost for all clusters. To approximately solve this problem, we first adopt an affinity propagation method to determine the number of clusters and the center of each cluster. Then, by treating small cells as agents, the original MDP problem is proved to be equivalent to a multiagent MDP to maximize the average reward of all small cells. Next, a multiagent deep reinforcement learning (DRL) is proposed to jointly implement the dynamic clustering for noncenter small cells, resource allocation, and duplex mode selection. Simulation results show that SD has prominent advantages over both the HD and FD in UDNs, and the proposed multiagent DRL outperforms other clustering schemes under the considered scenarios.

This article considers a smart-duplex (SD) powered user-centric ultra dense networks (UC-UDNs), where each user is served cooperatively by multiple access points (APs) adopting the de-cellular concept to achieve desired Quality-of-Service (QoS). The average QoS satisfaction ratio maximization problem for the considered SD UC-UDN is formulated as a Markov decision process (MDP) with large discrete action space by designing the user association and power allocation. To reduce the action space, user association and power allocation are modeled as a two-layer tree, and selecting an action for each user is equivalent to finding the path from the root to one leaf of the constructed tree. Then, a multiagent tree-structured policy gradient (MATSPG)-based deep reinforcement learning (DRL) algorithm is proposed to solve the MDP problem, whose training process is shown to be equivalent to that of the two-layer neural networks. Next, the time and space complexity of searching one action in the proposed MATSPG are also proved to be lower than the conventional DRL algorithms. Finally, simulations show that the proposed MATSPG algorithm significantly improves the average QoS satisfaction ratio than the conventional multiagent deep deterministic policy gradient and multiagent deep Q-network methods in typical scenarios.

Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timely situational awareness, IoT devices continuously sample computation-intensive status updates, generate perception tasks and offload them to edge servers for processing. In this sense, the timeliness of information is considered as one major contextual attribute of status updates. In this paper, we derive the closed-form expressions of timeliness of information for computation offloading at both edge tier and fog tier, where two-stage tandem queues are exploited to abstract the transmission and computation process. Moreover, we exploit the statistical structure of Gauss-Markov process, which is widely adopted to model temporal dynamics of system states, and derive the closed-form expression for process-related timeliness of information. The obtained analytical formulas explicitly characterize the dependency among task generation, transmission and execution, which can serve as objective functions for system optimization. Based on the theoretical results, we formulate a computation offloading optimization problem at edge tier, where the timeliness of status updates is minimized among multiple devices by joint optimization of task generation, bandwidth allocation, and computation resource allocation. An iterative solution procedure is proposed to solve the formulated problem. Numerical results reveal the intertwined relationship among transmission and computation stages, and verify the necessity of factoring in the task generation process for computation offloading strategy design.

This paper studies the joint device selection and power control for wireless federated learning (FL), considering both the analog downlink and over-the-air computation (AirComp)-based uplink communications between the parameter server (PS) and the terminal devices. First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and derive the upper bound on the expected optimality gap between the expected and optimal global loss values with respect to (w.r.t.) the selected devices and downlink and uplink transmit power values. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problem, which is solved by using the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this problem, we first simplify it and convert it to an equivalent Markov game, for which the large and discrete action space issue is greatly relieved. Then, we propose a Whittle's index guided multi-agent proximal policy optimization (WI-MAPPO) algorithm to solve the considered game, where the embedded Whittle's index module further shrinks the action space, and the proposed offline training algorithm extends the training kernel of the conventional MAPPO to address the issue of time-varying constraints.

This paper considers an ultra dense network (UDN) with smart-duplex (SD), which allows the base stations (BSs) to flexibly switch between half-duplex (HD) and full-duplex (FD) modes over time. All the small cells are divided into several clusters, where the BSs in the same cluster jointly serve their users. A Markov decision process (MDP) problem is formulated to maximize the average weighted sum of network throughput and clustering cost for all clusters. To approximately solve this problem, we first adopt an affinity propagation method to determine the number of clusters and the center of each cluster. Then, by treating small cells as agents, the original MDP problem is proved to be equivalent to a multi-agent MDP to maximize the average reward of all small cells. Next, a multi-agent deep reinforcement learning (DRL) algorithm is proposed to jointly implement the dynamic clustering for the non-center small cells, resource allocation, and duplex mode selection. Simulation results show that SD has prominent advantages over both the HD and FD modes in UDNs, and the proposed algorithm outperforms other clustering schemes under the considered scenarios.

This article studies the energy efficiency (EE) of a three-node decode-and-forward (DF) relay channel, where the relay operates in full-duplex (FD) or half-duplex (HD) mode. In particular, for the FD mode, both the residual self-interference (RSI) and the extra circuit power consumption introduced by the self-interference cancellation (SIC) at the relay node are considered and modeled as linear functions over the relay transmission power. Under this setup, the EE maximization problems for the considered FD and HD modes subject to the total transmission power and spectral efficiency (SE) constraints are formulated as max–min problems, whose optimal power allocation is computed by fractional programming. Next, the EEs for the FD, HD, and direct transmission (DT) modes are compared under different channel conditions, and a hybrid mode selection scheme, i.e., selecting the one with the maximum EE among the above three modes, is proposed. Besides, we also prove that both the EE and the SE monotonically increase with the total transmission power under a given condition. Finally, numerical results show that the maximum EE of the FD relaying exceeds those of the HD and DT ones when the required SE is relatively high. Furthermore, the proposed hybrid mode selection scheme can provide up to 192.4% higher EE compared with the DT mode.

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the idealFedAvgscheme with error-free model exchange and full device participation.

Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information.

This paper studies the joint digital self-interference (SI) cancellation and data detection in an orthogonal-frequency-division-multiplexing (OFDM) full-duplex (FD) system, considering the effect of phase noise introduced by the oscillators at both the local transmitter and receiver. In particular, an universal iterative two-stage joint SI cancellation and data detection framework is considered and its performance bound independent of any specific estimation and detection methods is derived. First, the channel and phase noise estimation mean square error (MSE) lower bounds in each iteration are derived by analyzing the Fisher information of the received signal. Then, by substituting the derived MSE lower bound into the SINR expression, which is related to the channel and phase noise estimation MSE, the SINR upper bound in each iteration is computed. Finally, by exploiting the SINR upper bound and the transition information of the detection errors between two adjacent iterations, the universal bit error rate (BER) lower bound for data detection is derived.

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum${a}$posteriori(MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions.

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper. Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise.

Resonant beam communication (RBCom) is a promising technology to satisfy the need for high data rate mobile communication. In this paper, we present the system model of kilometer-level RBCom by analyzing the resonant beam in one complete reflection round. Then, we derive the channel capacity for the scenario that the receiver moves in a fixed direction at a constant velocity. Numerical and simulation results reveal that the channel capacity decreases dramatically after a certain duration due to the cumulative doppler shift.

Due to the nonlinear distortion in Orthogonal frequency division multiplexing (OFDM) systems, the timing synchronization (TS) performance is inevitably degraded at the receiver. To relieve this issue, an extreme learning machine (ELM)-based network with a novel learning label is proposed to the TS of OFDM system in our work and increases the possibility of symbol timing offset (STO) estimation residing in intersymbol interference (ISI)-free region. Especially, by exploiting the prior information of the ISI-free region, two types of learning labels are developed to facilitate the ELM-based TS network. With designed learning labels, a timing-processing by classic TS scheme is first executed to capture the coarse timing metric (TM) and then followed by an ELM network to refine the TM. According to experiments and analysis, our scheme shows its effectiveness in the improvement of TS performance and reveals its generalization performance in different training and testing channel scenarios.

This article studies the energy efficiency (EE) of the two-way wireless communication system operating in the full-duplex (FD) and half-duplex (HD) modes, respectively. Particularly, both the residual self-interference (RSI) and power consumption for the self-interference cancellation (SIC) in the FD mode are modeled as linear functions over the transmit power. The EE maximization problems for FD, time-division duplex (TDD), and frequency-division duplex (FDD) modes with the sum and individual spectral efficiency (SE) constraints are studied, respectively. With the sum SE constraint, closed-form expressions for the maximum EE of these three modes are derived, and the maximum EE among them are compared. Based on the comparison results, a duplex selection scheme to achieve the highest EE among the three duplex modes is proposed. With the individual SE constraint, the optimal and suboptimal resource allocation are obtained by utilizing fractional programming for the three duplex modes. Somehow surprisingly, numerical results reveal that the FD mode achieves the best EE performance when the target sum SE or the distance between the two transceivers is relatively large.

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this article, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 b/s/Hz.

This paper considers the blind recognition of the type and the encoding parameters of channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural network (RNN), the attention mechanism, and the residual neural network (ResNet), three universal recognizers are proposed to identify the type, rate, and length of the target channel codes, with a training set generated by a small portion of all the possible code parameters. The proposed architectures need near zero a priori knowledge about the target channel code, and only require the length of the received signal to be dozen times of the codeword length. Numerical experiments show that the proposed deep learning methods own strong generalization to identify channel codes from the testing samples not generated by the encoding parameters utilized for the training set.

Massive machine-type communications (mMTC) are expected to support bunches of low cost devices, which are stochastically active. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the data in mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to suppress the multi-user interference. Then, maximum a posteriori (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellations at each user are designed. Finally, the scaling behavior of the considered system is analyzed, and it is shown that to guarantee reliable communications, the number of the receiver antennas per user should vanish in the order of O([1/log(N)]) with N being the number of the total users.

This paper considers a massive single-input multiple-output (SIMO) system, where multiple single-antenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas. Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading, we propose a joint transceiver design method based on machine learning, requiring a limited number of channel realizations. In the proposed method, the multiple transmitters, the channel, and the receiver are represented with a deep neural network (NN), and an autoencoder is adopted to minimize the end-to-end transmission error probability. Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios, and is more robust against the channel parameters variation compared with the existing methods.

In order to improve the multicast transmissions, a full-duplex (FD) receiver cooperation scheme is proposed in this paper. The transmitter sends one common message to two FD receivers, and each receiver forwards its received signals to its counterpart by the amplify-and-forward (AF) scheme. Due to the imperfect SI cancellation at the receivers and the AF scheme, the residual self-interference and the additive noise (RIAN) will be accumulated over time. First, this paper analyzes the equivalent channel model of the considered system as well as the the statistics of the accumulated RIAN. Then, with the forward decoding scheme, the corresponding achievable rates are derived, and the optimal power allocation is obtained via solving a max-min problem. In particular, one-side cooperation scheme (i.e., only one receiver forwards its received signal to its counterpart) is shown to be optimal to achieve the best system performance.

In millimeter wave (mmWave) based communication system, hybrid beamforming is regarded as a pivotal technique for the sake of reducing the hardware complexity. In this paper, we studied the optimal hybrid beamforming and combiner design for a multi-user mmWave MIMO system in order to simultaneously multicast information to the information users (IUs) and transfer wireless power to the energy users (EUs). By considering practical non-linear RF energy harvester, our transceiver design aims for maximising the downlink multicast throughput, while satisfying the charging requirements of the EUs. This sub-optimal solution is obtained by a low-complexity algorithm. This algorithm firstly designs separately the beamformer and combiners by minimizing mean square error principle. Then given the resultant combiners, the algorithm update the hybrid multicast beamforming at the transmitter until it converges. Our transceiver design also achieves good detection performance for the IUs. The numerical results demonstrate the advantage of our joint transceiver design over the separated counterpart in terms of both the multicast throughput and the energy harvested.

This article considers the design of the transmitter and receiver in a noncoherent massive single-input and multiple-output (SIMO) system over a multipath channel, representing a typical Internet-of-Things (IoT) scenario that consists of multiple single-antenna transmitters and one receiver with a large number of antennas. In particular, the autoencoders, which consist of multiple independent neural networks (NNs), are adopted at the transmitters and the receiver and are trained jointly, while working separately. To avoid the delicate design for mitigating the intersymbol interference (ISI) caused by multipath channels, the modulation schemes at the transmitters and the demodulation rule at the receiver are learned by the NNs over a limited number of channel samples. Moreover, the relationship between the number of channel samples and the performance of the trained transceiver is analyzed. The simulation results show that the proposed method achieves a lower error probability in comparison with the conventional optimization-based methods under typical channel conditions.

This paper studies the joint digital self-interference (SI) cancellation and data detection in an orthogonal-frequency-division-multiplexing (OFDM) full-duplex (FD) system, consisting the effects of phase noise. In particular, a two-stage scheme is proposed to estimate the SI channel state and detect the data from the remote transmitter. Based on the proposed scheme, the SI cancellation and the data detection abilities are analyzed, and the expression for the bit-error-rate (BER) is derived. Simulation results indicate that the derived theoretical BER performance is highly consistent with the simulations.

In this paper, we focus on a two-transmitter and two-receiver interference channel (IC), where each transmitter sends a message to the desired receiver. Especially, the full-duplex (FD) amplify-and-forward (AF) protocol is adopted to build up the receiver cooperations. With the considered scheme, the equivalent channel model is analyzed, and the statistics of the accumulated residual interference and noise (ARIN), generated by the imperfect self interference (SI) cancellation and AF scheme, are calculated. Then, the achievable rate regions for both the single-user and joint decoding schemes are characterized by a concave-convex procedure (CCCP). Next, from the achievable rates, one-side cooperation is analyzed to explain its optimality. Simulation results show that the achievable rate regions can be improved by the proposed scheme in certain scenarios.

This paper studies the throughput maximization problem for a three-node relay channel with direct link and non-ideal circuit power. The relay operates in a half- duplex manner, and the decode-and-forward (DF) relaying is adopted. Considering the extra power consumption by the circuits, the optimal power allocations over infinite time horizon are investigated. First, two special scenarios, i.e., the direct link transmission (only use the direct link to transmit) and the relay assisted transmission (the source and the relay transmit with equal probability), are studied. By solving two non-convex optimization problems, the solutions show that the source and the relay transmit with certain probability, which is determined by the average power budgets, circuit power consumptions, and channel gains. Then, based on the above results, the optimal power allocation for the original throughput maximization problem is investigated, which is shown to be a mixed transmission scheme between the direct link transmission and the relay assisted transmission.

As the mobile communication technologies evolving, spectrum resource has become extremely scarce. Accordingly, spectrum aggregation (SA) is proposed as an emerging solution to efficiently utilize the dispersive resource. To address such a challenging issue, this paper introduces power domain into the conventional SA which only works in the time and frequency domains, and extends the resource block (RB), which is a time-frequency spectrum management unit in LTE standard, to a time-frequency-power spectrum unit termed resource cube. Based on this, a novel radio resource management (RRM) scheme is proposed to manage the spectrum with multiplexing division in time, frequency, and power domains (3D) simultaneously. Through theoretical derivations, we show that under certain simplifications, the proposed 3D RRM could be formulated as a convex objective function with linear constrains, and can be solved with low computational complexity. When compared with the conventional RB-based RRM, the proposed 3D RRM matches the real-time requirement in SA. Furthermore, it is proved that the proposed scheme could achieve better energy efficiency than the RB-based ones.

We study the optimal precoding for a full-duplex (FD) system, where one FD multi-antenna base station (BS) respectively transmits to and receives from two half-duplex single-antenna mobile users (MUs) on the same time slot and frequency band. At the FD BS, the received signal from the desired MU is severely affected by the extremely strong self-interference (SI) from its transmit antennas to the receive antennas. In the presence of residual SI after imperfect SI cancellation, the downlink transmission rate maximization problem subject to a targeted uplink rate is formulated as a non-convex optimization problem to characterize the achievable rate region for the considered system. Considering the case in which the SI channel is strongly correlated, the above problem is transformed into a convex problem by exploiting the rank-one property of the SI channel, which can be solved efficiently. Finally, numerical results validate the effectiveness of the proposed scheme.

This paper studies the throughput maximization problem for a three-node relay channel with non-ideal circuit power. In particular, the relay operates in a half-duplex manner, and the decode-and-forward (DF) relaying scheme is adopted. Considering the extra power consumption by the circuits, the optimal power allocation to maximize the throughput of the considered system over an infinite time horizon is investigated. First, two special scenarios, i.e., the direct link transmission (only use the direct link to transmit) and the relay assisted transmission (the source and the relay transmit with equal probability) are studied, and the corresponding optimal power allocations are obtained. By transforming two non-convex problems into quasi-concave ones, the closed-form solutions show that the source and the relay transmit with certain probability, which is determined by the average power budgets, circuit power consumptions, and channel gains. Next, based on the above-mentioned results, the optimal power allocation for both the cases with and without direct link is derived, which is shown to be a mixed transmission scheme between the direct link transmission and the relay assisted transmission.

Energy harvesting communication has raised great research interests due to its wide applications and its feasibility of commercialization. In this paper, the multiuser energy diversity is investigated in energy harvesting communication systems. Considering centralized access schemes, the scaling of the average throughput over the number of transmitters is studied, along with the scaling of corresponding available energy in the batteries that store the harvested energy. It is shown that the throughput gain mainly comes from two aspects: the increase of total available energy harvested over time/space; and the combined dynamics of batteries that lead to the improvement in effective transmission power.

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. This paper considers a general case of imperfect channel state information (CSI), aiming at a joint and robust design of the input covariances for the multicast message, confidential message and AN, such that the worst-case secrecy rate region is maximized subject to the sum power constraint. To this end, we reveal its hidden convexity and transform the original worst-case robust secrecy rate maximization (SRM) problem into a sequence of semidefinite programming. Numerical results are presented to show the efficacy of our proposed method.

This paper considers an artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for all receivers and one confidential message intended for only one authorized receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. Assuming imperfect channel state information (CSI) of unauthorized receivers at the transmitter, our goal is to jointly design the input covariances of the multicast message, confidential message and AN such that the outage secrecy rate is maximized for a given outage probability, while keeping the outage probability of multicast message for each user below a certain threshold. Due to the intrinsical complexity of this problem, a safe and convex albeit suboptimal reformulation, based on two advanced convex restriction approaches, is applied to generate a tractable approximation for this problem. By this means, a computationally efficient lower bound on the outage secrecy rate can be determined. We also prove the feasibility of beamforming to achieve the obtained secrecy rate. Numerical results are presented to verify the efficacy of our proposed method.

This paper considers an energy-efficient transmit design in a three-node MISO wiretap channel in the eyes of service integration. Specifically, we combine two sorts of services, and serve them simultaneously: one multicast message intended for both receivers and one confidential message intended for only one authorized receiver. The confidential message must be kept perfectly secure from the unauthorized receiver. Our goal is to jointly design the input covariance matrices of the multicast message and confidential message such that the secrecy energy efficiency (SEE) is maximized, subject to the multicast rate, secrecy rate and total transmit power constraints. Due to the nonconvexity of this problem, an equivalent parametric reformulation, based on the fractional programming theory, is proposed to recast this problem as a sequence of semidefinite programs. By this means, the maximum SEE can be found via a root search algorithm. Moreover, we also give an approach to constructing a rank-one optimal covariance matrix of the confidential message from our proposed algorithm, which implies the feasibility of transmit beamforming to achieve the maximum SEE. Numerical results are finally presented to verify the efficacy of our proposed method.

This paper proposes a simple hybrid decode- compress- and-forward and compress-and-forward (DCF&CF) relay scheme for the coordinated multipoint (CoMP) uplink transmission. In particular, we consider a scenario that two mobile users (MUs) transmit signal to two access points (APs), which are linked to the central unit (CU) via lossless capacity-limited fronthaul. To characterize the achievable rate region of the considered system, a rate maximization problem is formulated and solved by exploiting the monotonicity and convexity of its objective function. Furthermore, maximum sum rate of the two MUs is obtained according to the maximized rate region boundary. Finally the analysis is validated by numerical results.

Multicast transmission based on real-time network state information is a resource-friendly technique to improve the energy efficiency and reduce the traffic burden for cellular systems. This paper evaluates the effectiveness of this technique for downlink transmissions. In particular, a scenario is considered in which multiple mobile users (MUs) asynchronously request to download one common message locally cached at a base station (BS). Due to the randomness of both the channel conditions and the request arrivals from the MUs, the BS may choose to intelligently hold the arrived requests, especially when the channel conditions are bad or the number of requests is small, and then serve them in one shot later via multicasting. Clearly it is of great interest to balance the delay (incurred by holding the requests) and the energy efficiency (EE, defined as the energy cost per request), and this motivates us to quantify the fundamental tradeoff for the proposed “hold-then-serve” scheme. For the scenario with single channel and unit message sizes, it is shown that for a fixed channel bandwidth, the delay-EE tradeoff reduces to judiciously choosing the optimal stopping rule for when to serve all the arrived requests, where the effect of the bandwidth on the achievable delay-EE region is discussed further. By using optimal stopping theory, it is shown that the optimal stopping rule exists for general Markov channel models and request arrival processes. Particularly, for the hard deadline and proportional delay penalty cases, it is shown that the optimal stopping rule exhibits a threshold structure, and the corresponding threshold in the former case is time varying while in the latter case it is a constant. Finally, for the more general scenario with multiple channels and arbitrary message sizes, the optimal scheduling is formulated as a Markov decision process problem, where some efficient suboptimal scheduling algorithms are proposed.

Design of high-efficiency power amplifier (PA) is one of the key challenges to realize green radios, wherein digital predistortion (DPD) is deployed to reduce the PA's power back-off and thus increase its power efficiency. As the bandwidth of the transmit signal increases, stringent requirements are posed on the DPD linearization performance with limited sampling rate and dynamic range for the analog-to-digital converter (ADC) in the DPD feedback channel. In this paper, under a fixed ADC sampling rate, novel DPD architecture is proposed to compensate for the PA nonlinearity with limited ADC dynamic range. In the feedback channel of the proposed architecture, an extra radio frequency (RF) cancellation chain is introduced to eliminate the linear component of the PA amplified signal, and thus the requirement on the ADC dynamic range can be significantly reduced. Subsequently, by accurately estimating the loop delay and attenuation of the cancellation chain, the baseband replica of the RF cancelling signal is recovered, and the original PA output signal is rebuilt to estimate the DPD coefficients. Finally, experiments show that for the long term evolution (LTE)-advanced signals, the proposed architecture can achieve an adjacent channel leakage ratio lower than -47.6 dBc, which outperforms the conventional DPD by about 3.4 dB, with the effective bits of the ADC being reduced by 4.4 and a power added efficiency of 43.8% with 7.3 dB power back-off being observed for a fabricated Doherty PA with 50-dBm saturation power.

This paper considers a heterogeneous ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for one shared channel by random access. In particular, we focus on two different scenarios: the constant energy harvesting (EH) rate model where the EH rate remains constant within the time of interest and the i.i.d. EH rate model where the EH rates are independent and identically distributed across different contention slots. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with two-stage probing and save-then-transmit energy utilization is proposed. Then, the optimal throughput and the optimal scheduling strategy are obtained via one-dimension search, i.e., an iterative algorithm consisting of the following two steps in each iteration: First, assuming that the stored energy level at each transmitter is stationary with a given distribution, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is proven to exist and then derived for both models; second, for a fixed stopping rule, the energy level at each transmitter is shown to be stationary and an efficient iterative algorithm is proposed to compute its steady-state distribution. Finally, we validate our analysis by numerical results and quantify the throughput gain compared with the best-effort delivery scheme.

Energy harvesting (EH) based communication has raised great research interests due to its wide applications and the feasibility of commercialization. In this paper, we consider wireless communications with EH constraints at the transmitter. First, for delay-tolerant traffic, we investigate the long-term average throughput maximization problem and analytically compare the throughput performance against that of a system supported by conventional power supplies. Second, for delay-sensitive traffic, we analyze the outage probability by studying its asymptotic behavior in the high energy arrival rate regime, where the new concept of energy diversity is formally introduced. Moreover, we show that the speed of outage probability approaching zero, termed energy diversity gain, varies under different power supply models.

In wireless communication, the amplitude and phase of the transmitted signal have certain patterns during one symbol duration, which introduces high correlation among the samples obtained by oversampling at the receiver. In this work, we aim to explore such correlation information for cognitive radios to enhance the performance of spectrum sensing. By jointly considering the signal modulation, multipath fading, and oversampling rate, we derive the distribution of the empirical autocorrelation function for the obtained samples, on which we propose two efficient spectrum-sensing algorithms, and then analyze their performance. Our theoretical results reveal that the proposed algorithms with oversampling perform strictly better than the conventional energy detection scheme, while requiring the same level of prior information. Finally, we show through simulations that the derived statistical characteristics approximate the true statistical distribution of the autocorrelation function well, and the proposed sensing algorithms significantly improve the sensing performance compared to several existing sensing schemes.

This paper investigates the resource allocation problem for the Gaussian multiple access channel (MAC) with conferencing links, where the two transmitters can talk to each other via wired rate-limited channels. Moreover, the two transmitters are powered by a shared energy harvester which captures energy from the environment. We consider both the non-causal (the energy arrival levels at future time slots are known before transmissions) and the causal (only the energy arrival levels of past and present slots are known) energy-harvesting (EH) models. For the non-causal case, we formulate a resource allocation problem over a finite horizon ofNtime slots to characterize the boundary of the maximum departure region. We then develop the optimal offline power and rate allocation scheme by exploiting the hidden convexity of this problem. Interestingly, it is shown that there exists a maximum transmission rate (named the capping rate) for one of the transmitters. For the causal case, we examine the performance of the greedy scheme, in which the energy is depleted within each slot. In particular, we measure the utility of this scheme against the optimal offline one by competitive analysis, where the competitive ratio of the online greedy scheme, i.e., the maximum ratio between the profits obtained by the offline and online schemes over arbitrary energy arrival profiles, is derived.

This paper exploits co-channel interference (CCI) to secure the multi-antenna wiretap IFC consisting of two source-destination-eavesdropper triples, where each source-destination link is wiretapped by an external eavesdropper. To this end, we first propose a cooperative secrecy beamforming scheme, which is proved to be sufficient and necessary to achieve the secure degrees of freedom (S.D.o.F.) pair (1,1). By investigating the feasibility of the proposed beamforming scheme, we obtain the sufficient and necessary condition and also the beamforming vectors in closed-form to achieve the S.D.o.F pair (1,1). To the best of our knowledge, this is the first time that the benefit brought by CCI has been quantified.

This paper considers an ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for the same channel by random access. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with a save-then-transmit scheme is proposed. First, in the channel probing stage, each transmitter probes the CSI via channel contention; next, in the data transmission stage, the successful transmitter decides to either give up the channel (if the expected reward calculated over the CSI and ESI is small) or hold and utilize the channel by optimally exploring the energy harvesting and data transmission tradeoff. With a constant energy arrival model, i.e., the energy harvesting rate keeps identical over the time of interest, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is shown to exist and have a threshold-based structure, for both the homogeneous and heterogenous cases. Furthermore, we prove that there exists a steady-state distribution for the stored energy level at each transmitter, and propose an efficient iterative algorithm for its computation. Finally, we show via numerical results that the proposed scheme can achieve a potential 175% throughput gain compared with the method of best-effort delivery.

This paper studies the optimal power allocation for outage probability minimization in point-to-point fading channels with the energy-harvesting constraints and channel distribution information (CDI) at the transmitter. Both the cases with non-causal and causal energy state information (ESI) are considered, which correspond to the energy-harvesting (EH) rates being known and unknown prior to the transmissions, respectively. For the non-causal ESI case, the average outage probability minimization problem over a finite horizon of N EH periods is shown to be non-convex for a large class of practical fading channels. However, the globally optimal "offline" power allocation is obtained by a forward search algorithm with at most N one-dimensional searches, and the optimal power profile is shown to be non-decreasing over time and have an interesting "save-then-transmit" structure. In particular, for the special case of N=1, our result revisits the classic outage capacity for fading channels with uniform power allocation. Moreover, for the case with causal ESI, we propose both the optimal and suboptimal "online" power allocation algorithms, by applying the technique of dynamic programming and exploring the structure of optimal offline solutions, respectively.

This paper considers the use of energy harvesters for cooperative relaying in a large relay network, which consists of N energy-harvesting (EH) relays and one source-destination pair. In particular, a threshold-based “save-then-transmit” scheme is employed at the relays, where each relay transmits only when both the backward and forward link channel coefficients are above certain thresholds. We assume that the time scale of EH is much larger than that of communication blocks. For general channel fading models, we derive the asymptotic average throughput for the case with many relays, by using the amplify-and-forward (AF) relaying scheme. The throughput maximization is cast as a joint optimization problem over the transmission thresholds corresponding to all possible harvested energy rate states, which is shown to be non-convex in general. By applying a convexification technique via randomization, the original problem is transformed into a new formulation with a generalized threshold-based transmission scheme, which is shown to be efficiently solvable by bisection search, with the help of an offline look-up table only related to the channel statistics. Finally, with some numerical experiments, we demonstrate the performance gain of the proposed threshold-based transmission scheme against some suboptimal ones.

This paper considers joint estimation with multiple sensors powered by energy harvesters in wireless sensor networks. In particular, we focus on a network with K sensor nodes, which communicate with a fusion center via K orthogonal channels and power themselves by harvesting energy from the environment. Assuming a deterministic energy-harvesting model under which the harvested energy profile is known prior to transmission, the worst-case mean-square error (MSE) minimization problem over a finite horizon of T estimation periods is investigated. We consider the cases that the sensors have either infinite or finite battery capacity, and develop efficient iterative algorithms to compute the optimal power allocation strategy, with numerical results presented to validate our analysis.

This paper investigates the optimal resource allocation for the Gaussian multiple access channel (MAC) with conferencing links, where the two transmitters could talk to each other via some wired rate-limited channels. Moreover, the two transmitters are assumed to be powered by a shared energy harvester, and a deterministic energy-harvesting (EH) model is adopted by assuming that the energy arrival times and the corresponding harvested amounts are non-causally known prior to transmissions. We formulate a continuous-time power allocation problem to characterize the maximum departure region over a finite time horizon. By exploiting its convexity, this problem is simplified as a discrete-time problem and the optimal solution is obtained. In particular, it is shown that there exists a certain maximum possible transmission rate (the capping rate) at one of the transmitters. Finally, we compare the performance of the optimal offline algorithm against that of the online one.

This paper investigates the optimal resource allocation for a Gaussian multiple access channel (MAC) with two transmitters powered by a shared energy harvester. A deterministic energy-harvesting (EH) model is adopted, which assumes that the energy arrival amounts and timing are non-causally known before transmissions. Besides, packets for both transmitters are assumed always ready before transmissions. We first formulate the resource allocation problem to characterize the maximum departure region over a finite time horizon as a convex optimization problem. The structural properties of the sum power profile is then studied by exploiting the convexity of the sum power function, which simplifies the optimization problem. Finally, the optimal resource allocation between the two transmitters, in which there exists a cut-off rate at the stronger transmitter, is obtained. We also demonstrate that under the same energy arrival profile, MAC with shared energy harvester achieves the same maximum departure region as its dual broadcast channel (BC).

This paper considers the use of energy harvesters, instead of conventional time-invariant energy sources, in wireless cooperative communication. For the purpose of exposition, we study the classic three-node Gaussian relay channel with decode-and-forward (DF) relaying, in which the source and relay nodes transmit with power drawn from energy-harvesting (EH) sources. Assuming a deterministic EH model under which the energy arrival time and the harvested amount are known prior to transmission, the throughput maximization problem over a finite horizon of N transmission blocks is investigated. In particular, two types of data traffic with different delay constraints are considered: delay-constrained (DC) traffic (for which only one-block decoding delay is allowed at the destination) and no-delay-constrained (NDC) traffic (for which arbitrary decoding delay up to N blocks is allowed). For the DC case, we show that the joint source and relay power allocation over time is necessary to achieve the maximum throughput, and propose an efficient algorithm to compute the optimal power profiles. For the NDC case, although the throughput maximization problem is non-convex, we prove the optimality of a separation principle for the source and relay power allocation problems, based upon which a two-stage power allocation algorithm is developed to obtain the optimal source and relay power profiles separately. Furthermore, we compare the DC and NDC cases, and obtain the sufficient and necessary conditions under which the NDC case performs strictly better than the DC case. It is shown that NDC transmission is able to exploit a new form of diversity arising from the independent source and relay energy availability over time in cooperative communication, termed "energy diversity", even with time-invariant channels.

In this paper, the Gaussian diamond relay channel is considered, which consists of one source-destination pair and two relay nodes connected with rate-limited out-of-band conferencing links. In particular, we focus on the half-duplex alternative relaying strategy, in which the two relays operate alternatively over time. With different amounts of delay, two conferencing strategies are proposed, each of which can be implemented by either a general two-side conferencing scheme (for which both of the two conferencing links are used) or a special-case one-side conferencing scheme (for which only one of the two conferencing links is used). Based on the most general two-side conferencing scheme, we derive the achievable rates by using the decode-and-forward (DF) relaying scheme. By further exploiting the properties of the optimal solutions, the simpler one-side conferencing is shown to achieve the same rate as the two-side conferencing in term of the achievable rates under arbitrary channel conditions. Based on this result, the DF rate in closed-form is obtained, and the principle to use which one of the two conferencing links for one-side conferencing is also established. Moreover, the DF scheme is shown to be upper-bound-achieving under certain relay scheduling and conferencing strategy. Finally, numerical results are provided to validate our analysis.

In this paper, we consider a two-hop interference network, which consists of two source-destination pairs and two relay nodes connected with signal-to-noise ratio (SNR) limited out-of-band conferencing links. Assuming that the amplify-and-forward (AF) relaying scheme is adopted, this network is shown to be equivalent to a two-user interference channel (IC). By deploying two IC decoding schemes, i.e., single-user decoding and joint decoding, respectively, we characterize the achievable rate regions with a two-stage iterative optimization method: First, we fix the source power pair and maximize the sum rate over the relay combining vector; second, we fix the relay combining vector and optimize the source power pair. Specifically, for single-user decoding, we design a new routine to compute the optimal solution for the first subproblem, which is more efficient than the existing scheme; and for the second subproblem, we develop an iterative algorithm, with the closed-form solution for each iteration. Furthermore, it is revealed that the AF scheme with relay conferencing achieves the full degree-of-freedom (DoF), which outperforms the case without relay conferencing. Finally, simulation results show that relay conferencing can significantly improve the system performance under certain channel conditions.

This paper revisits the optimal power allocation for outage minimization in the classic point-to-point fading channels with the channel distribution information (CDI) known at the transmitter. The channel state information (CSI) is assumed to be perfectly known at the receiver, but not available at the transmitter. In particular, we consider a finite horizon of N-block transmissions subject to an average power constraint at the transmitter. Although minimizing the time-averaged per-block outage probability over each N-block transmission is shown to be a non-convex problem for a large class of practical fading channels, we show that the globally optimal power allocation is obtainable by a simple one-dimensional search. It is shown that if the average transmit power is above a certain threshold determined by the distribution of the fading channel and the target transmission rate, the uniform power allocation is optimal; otherwise, an on-off power allocation is optimal. Moreover, a suboptimal low-complexity power allocation scheme is proposed, which is shown to be asymptotically optimal as N goes to infinity. Finally, numerical results are provided to validate our analysis.

In this paper, we study the three-node Gaussian relay channel with decode-and-forward (DF) relaying, in which the source and relay nodes transmit with power drawn from energy-harvesting sources. Assuming a deterministic energy-harvesting model under which the energy arrival time and the harvested amount are known prior to transmission, the throughput maximization problem over a finite horizon of N transmission blocks is investigated. We consider the nodelay-constrained (NDC) traffic case, for which the relay can store the decoded information from the source with arbitrary delay before forwarding it to the destination in each N-block transmission. Although the formulated problem is non-convex, we prove the optimality of a separation principle for the source and relay power allocation over time, based upon which a two-stage algorithm is developed to obtain the optimal source and relay power profiles separately.

This paper considers the use of energy harvesters, instead of conventional time-invariant energy sources, in wireless cooperative communication. For the purpose of exposition, we study the classic three-node Gaussian relay channel with decode-and-forward (DF) relaying, in which the source and relay nodes transmit with power drawn from energy-harvesting sources. Assuming a deterministic energy-harvesting model under which the energy arrival time and the harvested energy amount are known prior to transmission, the throughput maximization problem over a finite horizon of N transmission blocks is investigated for the delay-constrained (DC) case (for which only one-block decoding delay is allowed at the destination). By exploiting the structures of the optimal source and relay power profiles, we show that the joint source and relay power allocation over time is necessary to achieve the maximum throughput, and propose an efficient forward two-dimensional search algorithm to compute the optimal power profiles.

This paper considers the use of energy harvesters in delay-constrained point-to-point wireless communications, where the source transmits with power drawn periodically from a device that harvests energy from the environment. In particular, the source is assumed to transmit over a block-fading channel with a constant transmission rate. It is also assumed that the channel state information (CSI) is unknown at the source but perfectly known at the destination, and the energy harvesting process is deterministic and known a priori at the source. The optimal power allocation is studied to minimize the receiver outage probability over a finite horizon of N energy-harvesting periods, each of which contains M communication blocks with independent channel fading coefficients. Although the outage minimization problem is shown to be non-convex, the optimal power allocation solution is obtained by the proposed forward search algorithm, which corresponds to an on-off transmission scheme. Moreover, a threshold-based sub-optimal low-complexity power allocation algorithm is proposed, which is shown to be asymptotically optimal as M goes to infinity. Finally, numerical results are provided to validate our analysis.

In this correspondence, we consider a half-duplex large relay network, consisting of one source-destination pair and N relay nodes, each of which is connected with a subset of the other relays via signal-to-noise ratio (SNR)-limited out-of-band conferencing links. The asymptotic achievable rates of two basic relaying schemes with the "p-portion" conferencing strategy are studied: For the decode-and-forward (DF) scheme, we prove that the DF rate scales as \mathcal{O} ( log (N) ); for the amplify-and-forward (AF) scheme, we prove that it asymptotically achieves the capacity upper bound in some interesting scenarios as N goes to infinity.

We consider the half-duplex diamond relay channel, which consists of one source-destination pair and two relay nodes connected with two-way rate-limited out-of-band conferencing links. Three basic coding schemes are studied: For the decode-and-forward (DF) scheme, we obtain an achievable rate by letting the source send a common message and two private messages; for the compress-and-forward (CF) scheme, we exploit the conferencing links to help with the compression of the received signals, or to exchange messages intended for the second hop to introduce different levels of cooperations; for the amplify-and-forward (AF) scheme, we study the optimal combining strategy between the received signals from the source and the conferencing link. Moreover, we show that these schemes could achieve the capacity upper bound under certain conditions. Finally, we evaluate various achievable rates for the Gaussian case with numerical results.

In this paper, we consider a two-hop interference network, which consists of two source-destination pairs and two relay nodes connected with signal-to-noise ratio (SNR) limited out-of-band conferencing links. Assuming that the amplify-and-forward (AF) relaying scheme is adopted, this network is shown to be equivalent to a two-user interference channel (IC). By deploying two IC decoding schemes, i.e., single-user decoding and joint decoding, respectively, we characterize the achievable rate regions with a two-stage iterative optimization method. The associated convergence issue is also studied. Furthermore, we compare the rates in the high SNR regime. Finally, simulation results show that relay conferencing can significantly improve the system performance under certain channel conditions.

To understand the network response to large-scale physical attacks, we investigate the asymptotic capacity of a half-duplex fading relay network with random node failures when the number of relays N gets infinitely large. In this paper, a simplified independent attack model is assumed where each relay node fails with a certain probability. The noncoherent relaying scheme is considered, which corresponds to the case of zero forward-link channel state information (CSI) at the relays. Accordingly, the whole relay network can be shown equivalent to a Rayleigh fading channel, where we derive the ε-outage capacity upper bound according to the multiple access (MAC) cut-set, and the ε-outage achievable rates for both the amplify-and-forward (AF) and decode-and-forward (DF) strategies. Furthermore, we show that the DF strategy is asymptotically optimal as the outage probability ε goes to zero, with the AF strategy strictly suboptimal over all signal to noise ratio (SNR) regimes. Regarding the rate loss due to random attacks, the AF strategy suffers a less portion of rate loss than the DF strategy in the high SNR regime, while the DF strategy demonstrates more robust performance in the low SNR regime.

In this paper, we investigate the asymptotic e-outage capacity of a half-duplex large fading relay network, which consists of one source node, one destination node, and N relay nodes. The relay nodes are assumed to be randomly deployed in a given area and under fatal independent random attacks with probability p. With a total power constraint on all the nodes, we examine the e-outage rate of the amplify-and-forward (AF) strategy when N tends to infinity, assuming no channel state information at the relays. We further quantify the gap between the e-outage rate and the e-outage cut-set bound, which is determined by the attack probability p, the source vs. sum power allocation factor ¿, and the topology of the networks. Moreover, we examine the effect of random attacks on the eoutage rate, and calculate the relative losses in low and high SNR regimes, respectively. Finally, for general SNR, we show that it is a quasiconcave problem to determine the optimal power allocation between the source and the relays, and we could obtain the optimal ¿ efficiently.