Covert communications have recently emerged as an innovative technology to enhance communication security and can utilize multiple antennas to improve system performance. Existing transmit precoding algorithms rely on the Gaussian inputs assumption, which is impractical in reality. Notably, conventional transmit precoding strategies for finite-alphabet inputs lack covertness considerations, limiting their effectiveness in ensuring covert communications. Therefore, based on finite-alphabet inputs, the practical transmit precoding for multiple-input multiple-output covert communication systems is investigated in this paper, where a warder equipped with multiple antennas aims to detect whether the transmission occurs. Specifically, an explicit expression for the outage probability of the legitimate communication is derived under imperfect channel state information. Then the covert throughput is formulated to measure transmission performance, while the Kullback-Leibler (KL) divergence is employed to evaluate covertness performance. Since both covert throughput and KL divergence are highly non-convex with respect to the transmit precoding matrix, maximizing covert throughput while satisfying the covertness constraint presents a significant challenge. To address the intractable optimization problem, an effective robust transmit precoding optimization algorithm based on deep reinforcement learning (DRL) is proposed to find the solution. Numerical results highlight the key differences between transmit precoding for Gaussian inputs and finite-alphabet inputs, while also demonstrating that the DRL algorithm outperforms other benchmark methods.

The stochastic geometry-based modeling and analysis of large-scale Integrated Sensing and Communication (ISAC) networks are vital for providing useful ISAC design insights. One important ISAC network characteristic is the sensing and communication (S&C) spatial correlations since the communication users (CUs) are more interested in the sensing target (STs) around them and the base stations prefer to utilize one ISAC signal to serve the CUs and STs close to each other to enable effective S&C coverage. However, most existing works focused on ISAC systems where the locations of CUs and STs are assumed to be independent. This paper bridges this gap by proposing an analytical framework for the ISAC networks where the unified ISAC waveform is modeled with limited main lobe beamwidth and the CUs and STs served by one signal are assumed to be correlatively distributed in spatial domain. Given the model, we first derive some prerequisite auxiliary quantities (i.e., the probability that an ST is served by the main lobe or side lobe, the link distance distribution, etc.) to analyze the network characteristics. Further, the communication/sensing coverage probability, as well as the joint and conditional ISAC coverage probability are analyzed to provide the comprehensive analysis results. Combining the theoretical analysis and simulation results, it verifies the accuracy of the analytical framework and quantifies the impact of the network parameters and spatial correlations on the S&C performance. Moreover, our results reveal how the sensing performance and communication performance are mutually restricted to describe the tradeoffs of S&C performance.

Public communication links can serve as shelters to facilitate covert transmissions. To improve the covertness performance, we regard multiple multi-antenna public users as friends and leverage their abundant spatial degrees of freedom (SDoF) to resist illegal surveillance. However, the resulting complex directional interference introduces challenges in measuring the covertness and optimizing the beamformers. Accordingly, this study investigates a covert communication system where multiple public user pairs coexist with one covert user pair in multiple-input multiple-output interference channels. Specifically, a covert rate maximization problem through joint transceiver beamforming is formulated, which is difficult to solve as it is a non-convex problem. To address this, a centralized algorithm is first developed based on the successive convex approximation method that transforms the problem into a convex optimization framework. Additionally, to alleviate the backhaul overhead, we develop a decentralized solution utilizing primal decomposition to decouple the interference management and the covertness metrics at each transmitter, relying only on local channel state information. Simulations demonstrate that our proposed algorithms fully exploit the SDoF jointly provided by the spatial distribution of public users and the employment of multi-antenna technology. Therefore, our algorithms achieve superior performance over baselines that do not effectively leverage these spatial resources.

Integrated sensing and communication (ISAC) is a promising technique for future wireless applications that demand both high-quality communication and precise sensing. Characterizing the fundamental tradeoff between sensing and communication (S&C) is essential for guiding cost-effective ISAC designs. However, the exact performance boundary characterizing the tradeoff between the communication rate and the sensing estimation accuracy remains unknown. To tackle this issue, this paper studies the exact Pareto boundary of the Cramér-Rao bound (CRB) and communication rate performance region, as well as the optimal input signal distributions that achieve this boundary. First, the explicit analytical expression of the expected CRB is derived with arbitrary random inputs, which quantify the accuracy of sensing estimation. It is proved that the bound-achieving distribution, which maximizes communication rate while ensuring CRB and power constants, is both timeindependent and circular symmetric. Leveraging these properties, a modified Blahut-Arimoto algorithm is developed based on functional analysis to numerically determine the optimal distribution on the CRB-rate boundary and correspondingly characterize the exact CRB-rate region. Numerical results demonstrate that ISAC systems enable a flexible CRB-rate tradeoff by adjusting the input signal distribution, outperforming separated S&C design benchmark. It is revealed that the communication benefits from greater signal amplitude randomness, while sensing accuracy improves with higher instantaneous power.

This paper investigates an integrated sensing and communication (ISAC) system with finite constellations. Both the problems of performance loss caused by discrete inputs and the conflicting requirements on symbol distribution of sensing and communication (S&C) functionalities are considered. Towards this, a waveform design based on the probabilistic shaping (PS) scheme is proposed, where a base station sends unified signals to estimate a sensing target and communicate with a user simultaneously. The analytical closed-form expressions of the sensing estimation rate (SER) and communication ergodic rate for PS-based ISAC are derived to evaluate the performance of the S&C functionalities, which are found to be mutually influenced by both the probability mass function (PMF) and power scaling factor. Furthermore, the optimal PMF and scaling factor are obtained by the alternative optimization algorithm to maximize SER while preserving communication capability. Simulation results validate the efficiency of our proposed PSbased waveform compared with those of schemes based on other input distributions. Additionally, the performance limit of ISAC systems with finite constellations is numerically obtained, which characterizes the tradeoffs between S&C functionalities.

The existing works on intelligent reflecting surface (IRS) aided covert communications consider the Gaussian input, which is however infeasible in practical systems. Two core issues remain to be answered: 1) How much performance gain can be obtained by applying the IRS for covert communications with finite-alphabet inputs? 2) How to jointly design the highly coupled parameters (constellation distribution and reflection coefficients) to obtain the optimal performance? To address these issues, in this work, the performance of the IRS aided covert communications with finite-alphabet inputs is analyzed, and a joint optimization scheme is proposed. In particular, the channel cutoff rate (CR) and the lower bound of the average detection error probability at the warder are derived under fading channels. Further, the impact of the reflection coefficients on the performance is discussed to reveal the benefits brought by the IRS. In addition, a two-layer algorithm is proposed to maximize the channel CR, where a channel variance tuple is introduced to decouple the optimization variables (constellation distribution and reflection coefficients). Numerical simulation demonstrates the superiority of the proposed scheme over various benchmarks. Moreover, the stricter the covertness constraint, the more concentrated the optimal probability distribution is at central constellation points.

Integrated Sensing and Communications (ISAC) is emerged as one of the key technologies in next generation wireless systems. However, ISAC systems have been commonly explored neglecting mutual coupling. This paper investigates the mutual coupling exploitation to further improve the performance of ISAC systems. We aim to maximize the sensing beampatern gain by optimizing the tunable loads and the dual-functional beamforming while satisfying the minimum signal-to-interference-plus-noise (SINR) per user and the hardware constraint of the tunable loads. To solve the non-convex problem, we propose a penaltybased iterative algorithm to obtain a stationary point. Specifically, in each iteration we adopt the block coordinate descent (BCD) method where the dual-functional beamforming is obtained by using Lagrange duality, and the tunable loads are solved with closed forms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms compared to the baseline schemes. To the best of our knowledge, this is the first study utilizing the MC effect to improve system performance in an ISAC system with tunable loads.

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

Intelligent reflecting surface (IRS) based symbiotic radio (SR) technology offers a promising approach to enhance Internet of Things (IoT) connectivity. Despite its potential, current simple implementations face significant security challenges that could undermine the reliability of IoT connectivity. To tackle this security issue, this paper introduces a novel model for covert SR transmission that employs joint passive and active beamforming strategies to counteract risks from multiple warders. In this model, the IRS acts as a secondary transmitter, backscattering binary phase shift keying signals. The system is categorized into two operational modes: parasitic SR (PSR) and commensal SR (CSR), based on the synchronization requirements between primary and secondary signals. A comprehensive analysis of the bit error rate for each mode is conducted, and the Kullback-Leibler divergence is leveraged to measure system covertness, establishing a tractable covertness constraint for subsequent optimization. To address the effects of imperfect channel state information on beamforming performance, a robust joint beamforming algorithm is proposed. Our results show that this algorithm outperforms existing baselines. Particularly in CSR mode, high reliability and strong covertness can be achieved with a minimal number of IRS reflecting elements, providing a cost-effective solution for communication security in IoT applications.

Covert communication is an important approach to ensure information security by hiding the transmission behavior. Space-domain-coding intelligent reflecting surface (SDC-IRS) can adjust the phase of the reflection signal for passive beamforming in angle domains, which is widely employed in covert communications. However, the gains by SDC-IRS vanish when the warder is proximal to the receiver in angle domains. To overcome this limitation, in this paper, the space-time-coding IRS (STC-IRS) is considered, which can adjust both the phase and the frequency of the reflection signal for passive beamforming in angle-distance domains. Specifically, system performance under STC-IRS and SDC-IRS is compared, revealing the essence that angle and distance discrimination for the receiver is achieved with STC-IRS. Further, to fully exploit STC-IRS, optimization problems are formulated to maximize the covert rate in both line-of-sight scenarios and Rician fading scenarios. To solve the above problems, penalty-based algorithms are proposed where the transmit power, the phase shift and the frequency shift at STC-IRS are optimized jointly with majorization-minimization and block successive upper bound minimization techniques. Considering more general and adverse cases, the proposed algorithms are also extended to the scenario with multiple warders. Simulation results demonstrate the superiority of the proposed scheme compared with other benchmarks. Especially, when the warder and the receiver overlap in angle domains, covert rates with STC-IRS exceed 3 bps by distance domain discrimination, whereas covert rates with SDC-IRS are less than 0.01 bps.

Integrated sensing and communication (ISAC) has been recognized as a key technology of the next-generation wireless networks. This paper focuses on the ISAC system with discrete signal inputs. Compared with the ISAC systems with continuous inputs in most prior works, it can provide useful insights for ISAC networks with digital modulation at the expense of compensating for the performance gap caused by the finite constellations. To tackle the above problem, a unified analytical framework is proposed to analyze and optimize the ISAC systems with finite constellations in this paper. Based on the analytical framework, both the communication ergodic rate and sensing estimation rate considering non-uniform constellations are derived to characterize the sensing and communication (S&C) performance and quantify the performance gap caused by discrete signals in ISAC networks. To compensate for the performance gap, the probabilistic shaping (PS)-based waveform design is formulated as a multi-objective optimization problem by jointly optimizing the discrete symbol distribution and the power scaling factor for achieving the Pareto-optimal S&C performance. Given the non-convexity of the above problem, an alternating algorithm is proposed to obtain the PS-based waveform. Moreover, the properties of the sensing-optimal and communication-optimal PS-based waveform are analyzed. It is proved that the sensing function prefers amplitude-deterministic signals, and the probabilities of outermost constellations are equal if they are distributed in a circularly symmetric manner. It is indicated that communication functionality requires quasi-Gaussian distribution, which is consistent with the results of our proposed algorithm. Numerical results characterize the tradeoff between S&C and validate the superiority of the proposed PS scheme in comparison with other input distributions.

The intelligent reflecting surface (IRS) has been widely applied in covert communications to hide the transmission behavior. However, the existing IRS relies on grid/battery power for its operation, which is unprocurable for practical covert communication applications. To address this issue, a novel multi-functional IRS (MF-IRS) is proposed for harvesting energy, signal reflecting, and amplifying simultaneously, where each element can flexibly switch between energy harvesting mode and passive/active reflection modes. To reveal the benefit of the MF-IRS for covert communications against multiple warders, the critical performance is analyzed, and effective design schemes are also provided. In particular, the detection error probabilities at warders are derived when the warders are non-collusive/collusive. In addition, the covert rate maximization problem is formulated by jointly optimizing the beamforming vector, element allocation matrices, and the reflection coefficient matrix. To solve this non-convex problem with highly coupled variables, an efficient successive convex approximation-based algorithm is proposed for the non-collusive scenario first and then extended to the collusive scenario. Simulation results demonstrate that the proposed MF-IRS always outperforms the self-sustainable passive/active IRSs, and it even outperforms the full-passive/active IRSs powered by grid/battery when the IRS is located near the signal source.

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

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

In this paper, the covert communication is considered with a full-duplex (FD) receiver under covertness and reliability constraints in the finite blocklength regime. Conventional covert communication systems are restricted by the square root law. To break through this limitation, an FD receiver is introduced, generating artificial noise (AN) to confuse the warder’s detection and achieving positive covert rates. To investigate the tradeoff among covertness, reliability, blocklength and throughput, the expressions of the warder’s detection error probability and the receiver’s decoding error probability in the finite blocklength regime are derived. Moreover, the asymptotic throughput is analyzed when the maximum transmit power of the FD receiver approaches infinity. Through the asymptotic analysis, we find that the blocklength’s impact on the covertness performance vanishes, which is a reason for achieving positive covert rates. However, the AN will also cause harmful residual self-interference to the receiver for which the FD receiver should be carefully designed by making a tradeoff between the covertness and the effective throughput. Hence, an optimization problem is formulated and solved to maximize the covert throughput. Numerical results show that the proposed scheme is effective in the finite blocklength regime where positive covert rates can be obtained.

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

Intelligent reflecting surface (IRS) is widely utilized in covert communications to enhance the system performance by passive beamforming. However, transmission covertness is seriously threatened when the warder is located on the line between IRS and the legitimate receiver. To overcome this limitation, the space-time-modulation IRS (STM-IRS) is considered in this paper, which can adjust both the phase and the frequency of the reflection signal to realize three-dimensional (3D) beam focusing. To maximize the signal-to-noise ratio at the receiver, an optimization problem is formulated by jointly adjusting the transmit power, the phase and the frequency shift at each reflection element. To solve above non-convex problem with highly coupled optimization variables, a penalty-based algorithm is proposed wherein the majorization-minimization technique and block successive upper bound minimization technique are adopted. Simulation results demonstrate that 3D beam focusing can be realized by STM-IRS to distinguish the warder and the legitimate receiver in both angle and range domains. Besides, the proposed scheme outperforms the scheme without frequency shift and other existing frequency shift schemes.

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

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

Integrated sensing and communication (ISAC) technique has recently gained significant research interest by supporting dual functions by a unified hardware platform. Traditionally, the ISAC system deemed the sensing and communication functions restricted by each other. In this paper, we demonstrate that a mutual benefit between sensing and communication functions can be achieved for the millimeter-wave ISAC system by efficiently exploiting the common information shared by both functions. Specifically, the sensing estimation can potentially be utilized to improve beamforming accuracy and the communication echo signal can enhance the sensing estimation as well. To demonstrate the mutual performance gain, we evaluate the joint outer bounds of the communication and sensing estimation rates. First, we derive the ergodic communication rate when the communication beamforming is assisted by sensing estimation. The asymptotic characteristic is further explored when the number of antennas approaches to infinity. Then, the closed-form estimation rate is obtained in terms of the Cramér-Rao Bound when the sensing estimation is assisted by communication signals. Numerical results verify the superiority of the mutual benefit when the common information was efficiently exploited by the ISAC BS transceiver. And when the transmission power is equally allocated, there are 10% and 2% gains on the communication rate and the sensing estimation rate simultaneously.

Probabilistic shaping (PS) has been demonstrated as an efficient method to achieve the shaping gain and to approach the Shannon limit in additive white Gaussian noise channels. To narrow the shaping gap caused by the channel fluctuation and the finite blocklength, a novel PS strategy is designed for polar coded communication systems with finite blocklength in Rayleigh flat fading channels. The proposed system transmits the quadrature amplitude modulation symbols and demodulates the received signal through log-maximum-a-posteriori (Log-MAP) detection. The channel capacity is theoretically derived in terms of the mutual information considering the influence of PS and the finite blocklength. Moreover, to solve the non-convex problem that maximizes the channel capacity on the basis of mutual information, an alternating optimization algorithm is proposed. Finally, numerical results indicate that with the optimal probabilistic shaping scheme, the system has 1.7 dB and 0.45 dB gain on the block error rate and channel capacity of the uniform distribution, respectively.

Limited access to available spectrum motivates new age radio frequency (RF) systems to co-exist and cooperate. In this paper, we consider a joint communication and radar system (labeled Comm-Radar), that is capable of receiving and decoding communication signals while simultaneously enabling radar functionalities. Traditionally, systems that leverage principles of spectrum convergence, deemed each functions to be detrimental to the others. This has led to successive interference cancellations strategies that effectively isolate each of the functionalities. Here, in the context of Comm-Radar processing structure, we show that ambient communication transmissions actually assist in radar estimation. The insight is that communications multipath reflects off radar targets (we denote these comm-target paths) in the environment, and can be processed at the receiver to extract extra information. In this work we characterize the fundamental performance limits of such a joint system.To demonstrate the radar performance gain achievable via the processing of extra comm-target paths, the closed-form Cramér-Rao Bound (CRB) for the estimation of direction, range and velocity of a target is derived, for both the general case, and practical radar and communication signalling schemes. Based on the CRB, joint outer bounds are obtained to characterize the trade off between estimation performance and communication rate. Finally, numerical results verify the advantages of utilizing the target reflections from the communication signal to bolster radar performance.

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

Coded caching is a technique that promises significant reductions in network traffic by exploiting the multicast opportunities among multiple cache-enabled users. Most works in this area investigate the fundamental performance limits of coded caching from an information-theoretic perspective. In this paper, from a practical perspective, we focus on the design and analysis of the coded caching scheme in large-scale stochastic networks with dynamic traffic. Aiming at the challenges of the large number of users, dynamic packet requests, and distance-dependent interference, the packet caching scheme and packet delivery scheme are jointly designed to ensure the availability of the coded caching gain. To characterize the coded caching performance, the queue dynamic of the packet requests and channel state are first analyzed. Then by combining the successive group decoding theory and stochastic geometry approach, the network interference is characterized and the successful transmission probability of the multicast coded signal is further derived. Moreover, the coded caching scheme is optimized to minimize the average delay of the packet request, which is proved to ensure service fairness and reduce the complexities of network performance analysis. The analytical and numerical results provide useful insights for the provisioning and planning of the coded caching network.

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

To realize the vision of the Internet of Things (IoT), mobile-edge computing (MEC) has recently emerged as a promising paradigm to meet the computation demand from mobile users (MUs). In this article, we study the network performance in large-scale stochastic MEC wireless networks, where the tasks can be computed locally by the local computation capabilities (LCCs) or be offloaded to MEC servers for edge computing. To this end, a MEC network is modeled featuring random node distribution, dynamic task requests, orthogonal frequency-division multiple access, task retransmission, and parallel computing in MEC servers. Given the model, a 2-D discrete-time Markov chain is first adopted to characterize the task execution process, including local computing and task offloading. Based on the coupling between communication and computing, the average outage probability of the task transmission and the average MEC computation load are derived by integrating the stochastic geometry and queuing theory. Furthermore, by jointly analyzing the local computation latency, transmission latency, and edge computation latency, we derive the average end-to-end latency of the task execution. Our results show that the LCCs in MUs can improve the network performance, including communication and computation performance, in stochastic MEC networks. In addition, useful guidelines for MEC network provisioning and planning are provided to avoid either the local computing or the task offloading being the latency performance bottleneck.

In this paper, we analyze the system performance of a stochastic network, which is in the stationary state. Different from previous full-loaded assumptions that base stations (BSs) are always transmitting signals, which overestimates the inter-cell interference (ICI), dynamic traffic is considered in our model and BSs can be silent in some time slots. Packet retransmission is adopted, which brings a coupled relationship between the outage probability and the buffer empty probability. That is, as the outage probability increases, packets that are not successfully received will remain at the head of the buffer and be retransmitted until they are successfully received, which increases the proportion of active BSs and aggravates the ICI in turn. To better analyze this coupled relationship, the ICI is analyzed with the aid of the probability generating function and the Laplace transform. Then a queuing model is established to depict the packet arrival and departure processes. Some important system metrics, including the outage probability, the buffer empty probability, and the stable throughput region, are derived. Numerical results verify our theoretical analysis, and it is revealed that packet retransmission can enhance the system performance when the packet request rate is within the stable throughput region.

In this paper, the downlink outage performance of full-duplex (FD) multi-cell networks considering interference cancellation at receivers is investigated. Different from previous FD cellular works, where all the interference is assumed to be perfectly cancelled, we propose the concept of strong interference region to distinguish strong interference and weak interference. A practical interference cancellation scheme is proposed to suppress the strong interference. Instead of assuming full-loaded cells in the analysis on FD networks, we consider the dynamic traffic request of the users in the stochastic networks where the cells may not always actively transmit and/or receive signals, consequently they do not always introduce interference to others. The probabilities of base stations (BSs) working in the FD, downlink, uplink and idle modes have been derived. To evaluate the system performance, we derive the outage probability and throughput. The simulation results show that outage performance is significantly improved compared to that without interference cancellation. Meanwhile, after cancelling the strong interference, the performance approaches that of cancelling all the interference. The impacts of the number of user equipments (UEs), the traffic request rate, the density of BSs and the size of the strong interference region on outage performance and throughput performance are further revealed.

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

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

Probabilistic shaping (PS) combined with bipolar amplitude shift keying modulation is a promising transmission scheme. However, such 1-dimensional PS scheme inevitably suffers shaping loss in Rayleigh fading channels due to the lack of signal diversity. In this paper, we design a rotated quadrature amplitude modulation based PS strategy. With the discrete inputs, the ergodic constellation constrained (CC) capacity is derived based on the mutual information conditioned on full channel state information available at the receiver, which is found to be mutually influenced by both the probability mass function (PMF) and the rotation angle (RA) of the input signals. Furthermore, the closed-form cutoff rate is also derived to simplify the analysis, which is the lower bound of the mutual information with finite decoding complexity. Correspondingly, the optimizations of RA and PMF are both considered for maximizing the ergodic CC capacity and the cutoff rate, respectively, where the single parameter RA is firstly obtained by the exhaustive search. And then, the optimal PMFs of the subproblem for the fixed RAs and the fixed constellation scaling factor are obtained. Furthermore, with this non-equiprobable transmission scheme, the closed-form pairwise error probability is derived for the first time, which also validates the superiority of the proposed PS scheme in comparison with the uniform schemes with elaborate rotations.

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

With the common property of the sparse structure, multi-carrier low density signature (MC-LDS) and low density parity-check (LDPC) codes can be combined into a joint sparse factor graph (JSFG) while the joint detection and decoding (JDD) algorithm could bring significant gains in terms of the bit error rate (BER). In this paper, considering the coupling character of the soft information interaction in the elaborate JSFG, we carve the distribution of the merged decoding soft information from both sides and theoretically analyze the error performance of the uplink LDPC-coded MC-LDS system. Correspondingly, not only the average BER but also the practical decoding threshold could be effectively derived for any given number of iterations. Furthermore, based on the theoretical analysis, we optimize the degree distribution of the JSFG by leveraging the differential evolution method. Finally, simulation results show that the practical threshold of the JDD receiver with the optimized code approaches the signal to noise ratio of the theoretical capacity.

Probabilistic shaping (PS) combined with amplitude shift keying modulation is a spectrally efficient transmission scheme. To exploit the shaping gain in fading channels, we design a rotated quadrature amplitude modulation (QAM) based PS strategy. With discrete inputs, we first derive the ergodic constellation constrained (CC) capacity, which is found to be mutually influenced by both the probability mass function (PMF) and the rotation angle (RA) of input signals. Correspondingly, the optimal PMF and RA for maximizing the ergodic CC capacity are attained by the alternative optimization method. Furthermore, based on this non-equiprobable transmission scheme, the closed- form pairwise error probability is obtained for the first time. Finally, compared with the uniform scheme with elaborate rotation, the proposed PS strategy shows more than 1 dB gain in terms of both the efficiency and the error performance.

Probabilistic shaping (PS) combined with bipolar amplitude shift keying modulation is a promising transmission scheme to increase spectral efficiency. Different from the existing design of the point to point communications, we propose a PS-based uplink nonorthogonal multiple access (NOMA) scheme for fading channels to support multiple connections simultaneously, where successive interference cancellation (SIC)-based maximum a posterior (MAP) detection is implemented at the receiver. To exploit the shaping gain for each user, we first theoretically analyze the ergodic channel capacity and the constellation constrained (CC) capacity with discrete-constellation inputs, respectively. And then, a multi-step optimization scheme for maximizing the ergodic CC capacity is proposed to obtain the optimal probability mass function (PMF) of these input signals. The order of optimization is inverse to the SIC decoding order so that for each specific user, the remaining multiuser interference is calculated based on the optimal PMF of the later decoded users. Furthermore, based on this non-equiprobable transmission scheme, the closed-form pairwise error probability expressions for each user are obtained for the first time. Finally, compared with the uniform scheme, the simulations show that the proposed PS NOMA strategies achieve more than 1 dB gain in terms of the error performance.

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

Multi-User scheduling is a challenging problem under the relaying scenarios. Traditional schemes, which are based on the instantaneous signal-to- interference-plus-noises ratios (SINRs), cannot solve the inherent disparities of the qualities between different links. Hence, the system performance is always limited by the weaker links. In this paper, from the whole system throughput view, we propose an optimal multi-user scheduling scheme for the multi-user full-duplex (FD) buffer aided relay systems. We first formulate the throughput maximization problem. Then, according to the characteristics of the Karush-Kuhn-Tucker conditions, we obtain the optimal decision functions and the optimal weighted factors of different links of the proposed scheme. Simulation results show that the proposed scheme not only solves the disparities of the qualities between Si- R and R-Dilinks, but also that between different Si-R or Di-R links, which can be used as guidance in the design of the practical systems.

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

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

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

We consider a cache- and SIC-enabled wireless network, where the locations of the base stations (BSs) and users are modeled by the Poisson point process and Poisson cluster process, respectively. The BS serves two users simultaneously by transmitting a superimposed signal, and each user employs a joint scheme of cache-assisted interference cancellation (CAIC) and successive interference cancellation (SIC). Each user is classified as a caching or non-caching user depending on whether or not it caches data sent to its interferer in the same cell. The receiver operation of each user is classified into three possible cases: CAIC only, both CAIC and SIC, and SIC only. Then, the successful packet transmission probabilities of the caching and non-caching users are derived, based on which the average spectral efficiency (SE) and the average area SE (ASE) are obtained. The average SE and ASE are further optimized. Moreover, extensions to the scenario where the superimposed transmissions are within a finite radius around the BS are provided. Finally, the result for the general scenario of the BS serving multiple users simultaneously is obtained. The optimization problem of the general scenario is solved by converting it to the convex or the difference of convex problems in different subregions. Simulation results verify the analytical accuracy and the advantages of the proposed schemes.

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

We consider a cache- and SIC-enabled wireless network where the locations of the base stations (BSs) and users are modeled by the Poisson point process (PPP) and Poisson cluster process, respectively. The BS serves two users simultaneously by transmitting a superimposed signal and each user employs a joint scheme of cache-assisted interference cancellation (CAIC) and successive interference cancellation (SIC). Each user is classified as a caching or non-caching user depending on whether or not it caches data sent to its interferer in the same cell. The receiver operation of each user is classified into three possible cases: CAIC only, both CAIC and SIC, and SIC only. Then the successful packet transmission probabilities of the caching and non-caching users are derived, based on which the spectral efficiency (SE) are obtained. The power allocation problems are further formulated to maximize the SE, which prove to be convex.

The integration of two complementary technologies, millimeter-wave (mmWave) communications and massive multiple-input multiple-output (MIMO), will play a key role in enabling gigabit access in 5G systems. However, implementing mmWave massive MIMO using the traditional fully digital architecture will lead to prohibitive hardware complexity as it requires a massive number of RF chains matching antennas in number. To address this issue, the hybrid beamforming architecture has been recently proposed for efficient implementation of mmWave massive MIMO. Specifically, large-scale MIMO beamforming is implemented in the analog domain, called analog beamforming, that exploits the sparsity in mmWave channels for dramatic dimension reduction for digital MIMO signal processing. The typical phase-array implementation of analog beamforming introduces the uni-modulus constraints on the beamforming coefficients and renders the classic MIMO techniques unsuitable. This motivates the novel design framework, called Kronecker analog beamforming, proposed in this paper for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factor vectors with uni-modulus elements. Exploiting the properties of Kronecker product, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Thereby, Kronecker analog beamforming achieves interference nulling and signal enhancement both in the analog domain as well as dimension reduction for digital beamforming.

In this paper, we focus on the efficient deployment of high altitude platforms (HAPs). As the accuracy of the channel model highly affects the deployment scheme, we first propose a geometry-based HAP channel model, which considers the statistical and geometry properties of the terrestrial environments comprehensively. Based on the proposed channel model, we derive the Line-of-Sight (LoS) transmission probability of air-to-ground communication, and characterize the path loss analytically. We further propose an algorithm to maximize the deployment efficiency in terms of the coverage ratio, i.e., the ratio of the radius of the HAP to the inter-HAP distance. The accuracy of our theoretical analysis is validated by simulations.

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

In this paper, we investigate wireless information and power transfer in full-duplex (FD) two-way massive MIMO amplify-and-forward (AF) relaying network. In the considered network, the relay is an energy demander and applies time switching scheme to harvest energy from the users for amplifying and forwarding the users' signals. Different from prior studies, MIMO users and antenna correlation are considered. Achievable sum-rate is analyzed and the impact of antenna correlations is discovered. It is revealed from the analysis that the transmission power at the MIMO users can be cut down proportionally to 1/Mrand/or the transmission power at the relay can be scaled down proportionally to 1/Mr2to maintain a constant sum-rate, when massive antennas are equipped at the relay. Here Mrdenotes the number of antennas at the relay. Moreover, the optimal time switching ratio for energy harvesting at the relay is found to maximize the sum-rate. Finally, the analytical results are validated by computer simulations.

In this paper, we investigate fundamental throughput and delay tradeoffs in cognitive radio networks (CRN) with a primary user (PU) and a cooperative secondary user (SU). Considering the user fairness, we propose a novel cooperation scheme, where the PU transmits packets with a probability of α instead of always occupying the spectrum, in exchange for the SU's cooperation. The unauthorized SU can sense the environment periodically and get access to the licensed spectrum when the PU is idle. The SU will either transmit its own packets or forward the PU's packets based on a priority factor β. Considering the bursty nature of the sources, Markov chain model is used to characterize the evolution of buffer states. Based on the steady state probabilities, the average buffer length and the system's stable throughput region are obtained. The impacts of the cooperation scheme (α,β) on the throughput tradeoff between the PU and the SU are theoretically analyzed. We also derive the average end-to-end transmission delay of the two users. The throughput-delay tradeoff and the impacts of the link qualities on the system performance are analyzed. The accuracy of the analytical results are validated by simulations.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO, which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.

In this paper, the performance of full-duplex (FD) two-way massive multiple-input multiple-output (MIMO) relay systems is analyzed. One popular linear relaying scheme, i.e., maximum ratio combining/maximum ratio transmission relaying, is particularly investigated. Different from prior analyses, multi-pair of MIMO users and antenna correlation at both the relay and the users are considered. Asymptotic sum-rate under a general case is first derived. Four special power scaling cases are then discussed and the corresponding asymptotic sum-rates are derived in simple forms with clear insights. The analytical results clearly quantify the impacts of self-loop interference, intra-group interference and antenna correlation, and discover the power scaling laws under various cases. It is found that the transmission powers at both the MIMO users and the relay can be scaled down inversely proportional to the number of antennas at the relay while maintaining a desirable sum-rate when the number of antennas at the relay grows large. The FD two-way massive MIMO relay system is finally compared with the half-duplex (HD) counterpart. It is revealed that under two special power scaling cases, the FD system can achieve double sum-rate when compared with the HD system. However, under the other two power scaling cases, self-loop interference and intra-group interference degrade the asymptotic sum-rate and the FD system may perform worse than the HD system. The maximum allowable self-loop interference for the FD system to outperform the HD system is given explicitly. In addition, the impact of antenna correlation at the users and the relay is analyzed. The results show that the antenna correlation at the users has complicate impact on the sum-rate, while the antenna correlation at the relay may not affect the sum-rate under certain power scaling cases.

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

Exploiting cognition to the cache-enabled device-to- device (D2D) communication underlaying the multi- channel cellular network is the main focus of this paper. D2D pairs perform direct communications via sensing the available cellular channels, bypassing the base station (BS). Dynamic service is considered and the network performance is evaluated with the stochastic geometry. Node locations are first modeled as mutually independent Poisson Point Processes, and the service queueing process is formulated. Then the corresponding tier association and cognitive access protocol are developed. The delay and the length for the queue at the BS and D2D transmitter are further elaborated, with modeling the traffic dynamics of request arrivals and departures as the discrete-time multiserver queue with priorities. Moreover, impacts of the physical layer and content-centric features on the system performance are jointly investigated to provide a valuable insight.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper proposes a neural mechanism motivated system to extract noise resistant features for robust speech recognition. We use nonnegative matrix factorization to construct two layers of auditory neurons which captures the essence of speech patterns. The responses of these neurons to speech are further processed to form an auditory neural cepstral coefficient (ANCC) representation for speech recognition. We test the robustness of ANCC feature on a 51-word corpus, with recognizers trained on clean speech in noisy conditions. Compared with MFCC, ANCC shows less performance degradation and achieves satisfactory recognition accuracies in both non-stationary noise and high noise level conditions.