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Federated edge learning (FEEL) is a popular distributed learning framework that allows privacy-preserving collaborative model training via periodic learning-updates communication between edge devices and server. Due to the constrained bandwidth, only a subset of devices can be selected to upload their updates at each training iteration. This has led to an active research area in FEEL studying the optimal device scheduling policy for communication time minimization. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially and indirectly by minimizing either the iteration rounds or per-round latency, while the total communication time is determined by both metrics. To close this research gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the remaining communication time through cross-disciplinary effort that combines the learning theory for convergence rate analysis and communication theory for per-round latency analysis. Building on the novel analytical result, an optimized probabilistic device scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. Extensive experiments based on real-world dataset and a use case on collaborative 3D objective detection in autonomous driving are provided to verify the superiority of the proposed policy over three benchmark policies based on the indirect solution approaches. [ABSTRACT FROM AUTHOR]

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a promising solution for enabling the control of wireless environments. Most of existing works consider that an IRS consists of a large number of reconfigurable passive elements and each of them can reflect the signal independently. However, it is difficult and costly in practice to manufacture this kind of large-scale IRS. In this paper, we propose a practical electromagnetic-compliant reflection coefficient model for element-grouping IRS, which configures each sub-surface (including multiple homogeneous elements) but not each single element. Under this model, the reflection amplitude and the phase shift are coupled, which brings new challenges for optimizing the IRS configuration. We study a phase shift optimization problem to maximize the received power in an IRS-aided wireless system. As this problem is difficult to solve due to the complicated coupling in the practical model, we transform it into a vector composition problem equivalently. By constructing a geometry-based phase analysis model, we analyze the relationship of the vectors in a two-dimensional space to maximize the length of the sum vector. By exploiting the geometrical properties, we propose a low-complexity phase control algorithm to find the optimal phase shifts of IRS. Simulation results reveal that substantial performance gains are achieved by the proposed algorithm compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity. [ABSTRACT FROM AUTHOR]

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the “divide-and-conquer” policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel semi-passive elements-aided channel estimation framework for intelligent reflecting surface (IRS), where a small portion of IRS reflecting elements are able to process the received signal for facilitating the channel estimation. Specifically, the BS-IRS channel is estimated by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), while the user-IRS channels are estimated by combining the use of total least square (TLS) ESPRIT and multiple signal classification (MUSIC) methods. The required training time of the proposed channel estimation scheme is irrelevant to the number of IRS reflecting elements, thus substantially reducing the training overhead. Simulation results show the great advantages of our proposed scheme over both the conventional compressed sensing (CS)-based channel estimation and cascaded channel estimation schemes. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician $K$ factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users. [ABSTRACT FROM AUTHOR]

Massive machine-type communication (mMTC) is expected to provide reliable and low-latency connectivity for an enormous number of devices, which turn active sporadically or frequently. In this highly dynamic situation, it is crucial to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this article, by exploiting the large sequence space, the excellent correlation property and especially the elegant nested structure of Reed-Muller (RM) sequences, we propose a new RA scheme, which facilitates both instantaneous access for newly active users and incremental access for the existing users who suffer from detection failures. In particular, when a failure occurs, the user continues accessing the channel employing an expanded RM sequence, which is combined with the previously received ones at the access point (AP) to form a longer sequence so as to attain potentially better detection probability. Furthermore, a recursive detection algorithm is designed for jointly detecting the resultant RM sequences and the channel coefficients of both the newly active users and the existing ones. The performance of the proposed algorithm is analyzed in detail. Our simulation results validate the analysis and show the scheme’s superior access probability, access latency and computational complexity. [ABSTRACT FROM AUTHOR]

This letter presents a detailed investigation on the performance of reconfigurable intelligent surface (RIS)-assisted communication system with user scheduling. Depending on the availability of channel state information (CSI) at the RIS, two separate scenarios are considered, namely without CSI and with CSI. Closed-form expressions are derived for the ergodic capacity of the system in both scenarios. It is found that CSI has a significant impact on the performance of the system. Without CSI, the RIS provides an array gain of N, where N is the number of reflecting elements, and user scheduling provides an multi-user gain of $\log \log \text {M}$ , where M is the number of users. With CSI, the RIS provides an array gain of $\text {N}^{2}$ , while no multi-user diversity gain can be obtained. [ABSTRACT FROM AUTHOR]

In this article, we investigate the issue of integrated sensing, computation and communication (SCC) in beyond fifth-generation (B5G) cellular internet of things (IoT) networks. According to the characteristics of B5G cellular IoT, a comprehensive design framework integrating SCC is put forward for massive IoT. For sensing, highly accurate sensed information at IoT devices are sent to the base station (BS) by using non-orthogonal communication over wireless multiple access channels. Meanwhile, for computation, a novel technique, namely over-the-air computation (AirComp), is adopted to substantially reduce the latency of massive data aggregation via exploiting the superposition property of wireless multiple access channels. To coordinate the co-channel interference for enhancing the overall performance of B5G cellular IoT integrating SCC, two joint beamforming design algorithms are proposed from the perspectives of the computation error minimization and the weighted sum-rate maximization, respectively. Finally, extensive simulation results validate the effectiveness of the proposed algorithms for B5G cellular IoT over the baseline ones. [ABSTRACT FROM AUTHOR]

In this work, we study the statistically robust beamforming design for an intelligent reflecting surfaces (IRS) assisted multiple-input single-output (MISO) wireless system under imperfect channel state information (CSI), where the channel estimation errors are assumed to be additive Gaussian. We aim at jointly optimizing the transmit/receive beamformers and IRS phase shifts to minimize the average mean squared error (MSE) at the user. In particular, to tackle the non-convex optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization and majorization-minimization techniques. Simulation results show that the proposed scheme achieves robust MSE performance in the presence of CSI error, and substantially outperforms conventional non-robust methods. [ABSTRACT FROM AUTHOR]

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements. [ABSTRACT FROM AUTHOR]

To jointly address the critical issues of the sixth-generation (6G) cellular internet of things (IoT), i.e., energy supply, data aggregation, and information transmission, we design a framework integrating energy, computation and communication (ECC). Firstly, the base station (BS) charges massive IoT devices simultaneously via wireless power transfer (WPT) in the downlink. Then, IoT devices with the harvested energy carry out the computation task and the communication task in the uplink over the same spectrum. To improve the overall performance of ECC, we propose a joint beamforming design algorithm for the BS and the IoT devices. Finally, simulation results validate the effectiveness of the proposed algorithm in 6G cellular IoT. [ABSTRACT FROM AUTHOR]

In this letter, an intelligent reflecting surface (IRS) enhanced full-duplex MIMO two-way communication system is studied. The system sum rate is maximized through jointly optimizing the source precoders and the IRS phase shift matrix. Adopting the idea of Arimoto-Blahut algorithm, the non-convex optimization problem is decoupled into three sub-problems, which are solved alternatingly. All the sub-problems can be solved efficiently with closed-form solutions. In addition, practical IRS assumptions, e.g., discrete phase shift levels, are also considered. Numerical results verify the convergence and performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has recently emerged as a promising candidate to improve the energy and spectral efficiency of wireless communication systems. However, the unit modulus constraint on the phase shift of reflecting elements makes the design of optimal passive beamforming solution a challenging issue. The conventional approach is to find a suboptimal solution using the semi-definite relaxation (SDR) technique, yet the resultant suboptimal iterative algorithm usually incurs high complexity, hence is not amenable for real-time implementation. Motivated by this, we propose a deep learning approach for passive beamforming design in RIS-assisted systems. In particular, a customized deep neural network is trained offline using the unsupervised learning mechanism, which is able to make real-time prediction when deployed online. Simulation results show that the proposed approach maintains most of the performance while significantly reduces computation complexity when compared with SDR-based approach. [ABSTRACT FROM AUTHOR]

We consider a wireless powered two-way relaying system consisting of two energy constrained single antenna sources and one multi-antenna relay with constant power supply. The time division protocol is adopted, where the relay first acts as the energy source and employs energy beamforming to charge the two sources, and then switches its role as a relay to help forward the information to the sources. To maintain user fairness, we aim to maximize the minimum rate of two sources, by jointly optimizing the energy beamforming vector, time splitting factor and relay transformation matrix. To further reduce the complexity of the optimal algorithm, we propose an alternating optimization method, where closed-form expressions for the energy beamforming and time splitting factor are obtained. To gain more insights, we propose a simple suboptimal design and analyze the outage probability and the average rate when the relay applies simple energy beamforming and transformation matrix. The analysis shows that the system can achieve a diversity order of $\frac {N}{3}$ for a system with a relay of $N$ antennas. Numerical results show that the performance of the proposed low-complexity alternating optimization method approaches the optimal algorithm over the entire SNR range, but has a significantly low complexity, and the proposed suboptimal design can also achieves a decent performance especially in small $N$ regime. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the issue of sustainable communications for beyond fifth-generation (B5G) cellular internet of things (IoT) networks under adverse but practical conditions. A massive number of simple IoT devices without batteries harvest requisite energy from a part of the received signal. A design framework including channel state information (CSI) acquisition, signal construction, information decoding and energy harvesting, is first provided for sustainable communications of massive IoT. Then, based on the proposed design framework, we reveal the impacts of practically adverse factors, e.g., channel uncertainty, successive interference cancellation (SIC) and non-linear energy harvesting, on the performance of B5G cellular IoT. Furthermore, in order to effectively alleviate the impacts of these adverse factors, an outage-constrained robust algorithm is designed to maximize the overall performance of sustainable B5G cellular IoT. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider a power beacon (PB) assisted two-way relaying network, where two single antenna energy constrained users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. The key aim of the paper is to design the energy beamforming vector and time split parameter for optimizing the system performance. In particular, two different design objectives are investigated, namely, max-min rate design and sum rate maximization design. Due to the non-convex nature of the optimization problems, the global optimal solutions are difficult to obtain. Instead, we propose an alternating optimization design framework where near optimal performance can be achieved through iterative optimization. In addition, to further reduce the computation complexity, closed-form suboptimal designs are provided. Simulation results are presented to validate the effectiveness of the proposed alternating optimization design and suboptimal design. The outcomes of the paper suggest that adopting the proposed energy beamforming vector at the PB can substantially boost the system performance. Also, the topology of the network has a significant impact on the achievable performance. [ABSTRACT FROM AUTHOR]

This paper considers a two-way half-duplex decode-and-forward relaying system, where multiple pairs of single-antenna users exchange information via a multiple-antenna relay. Assuming that the channel knowledge is nonideal and the relay employs maximum ratio processing, we derive a large-scale approximation of the sum spectral efficiency (SE) that is tight when the number of relay antennas M becomes very large. Furthermore, we study how the transmit power scales with M to maintain a desired SE. In particular, three special power-scaling cases are discussed and the corresponding asymptotic SE is deduced with clear insights. Our elegant power-scaling laws reveal a tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, we formulate a power allocation problem in terms of maximizing the sum SE and obtain a local optimum by solving a sequence of geometric programming problems. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a point-to-point multi-input multi-output wireless-powered communication system, where the source S is powered by a dedicated power beacon (PB) with multiple antennas. Employing the time splitting protocol, the energy constrained source S first harvests energy through the radio-frequency signals sent by the PB and then uses this energy to transmit information to the destination D. Unlike several prior works, we assume that the energy transfer link is subjected to Rician fading, which is a real fading environment, due to relatively short range power transfer distance and the existence of a strong line of sight path. We present a comprehensive analysis of the achievable ergodic capacity in two scenarios, depending on the availability of channel state information (CSI) at PB, namely, the absence of CSI and partial CSI. For the former case, equal power allocation is used, while for the later one, energy beamforming is used to enhance the energy transfer efficiency. For both the cases, closed-form expressions for the upper and lower bounds of the ergodic capacity are derived. Furthermore, the optimal time split is discussed, and the capacity in the low and high signal-to-noise ratio regimes is studied through simple closed-form expressions. Numerical results and simulations are provided to validate the theoretical analysis. The results show that the Rician factor $K$ has a significant impact on the ergodic capacity performance, and this impact strongly depends on the availability of the CSI at the PB. [ABSTRACT FROM AUTHOR]

To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme. [ABSTRACT FROM PUBLISHER]

Assuming non-ideal circuit power consumption at the energy harvesting (EH) nodes, we propose two practical protocols that optimize the performance of the harvest-then-transmit wireless powered communication networks (WPCNs) under two different objectives: (1) proportional fair (PF) resource allocation, and (2) sum rate maximization. These objectives lead to optimal allocations for the transmit power by the base station, which broadcasts RF radiation over the downlink, and optimal durations of the EH phase and the uplink information transmission phases within the dynamic time-division multiple access frame. Compared to the max-sum-rate protocol, the PF protocol attains a higher level of system fairness at the expense of the sum rate degradation. The PF protocol is advantageous over the max-sum-rate protocol in terms of system fairness regardless of the circuit power consumption, whereas the uplink sum rates of both protocols converge when this power consumption increases. [ABSTRACT FROM PUBLISHER]

This paper investigates the impact of the channel state information (CSI) and antenna correlation at the multi-antenna relay on the performance of wireless powered dual-hop amplify-and-forward relaying systems. Depending on the available CSI at the relay, two different scenarios are considered, namely, instantaneous CSI and statistical CSI where the relay has access only to the antenna correlation matrix. Adopting the power-splitting architecture, we present a detailed performance study for both cases. Closed-form analytical expressions are derived for the outage probability and ergodic capacity. In addition, simple high signal-to-noise ratio (SNR) outage approximations are obtained. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only a diversity order of one can be achieved with statistical CSI. In addition, the transmit antenna correlation and receive antenna correlation exhibit different impacts on the ergodic capacity. Moreover, the impact of antenna correlation on the ergodic capacity also depends heavily on the available CSI and operating SNR. [ABSTRACT FROM PUBLISHER]

In this letter, we consider a secrecy wireless powered communication network (WPCN), where an information source sends message to a legitimate destination with the energy harvested from a power source, while an eavesdropper intends to intercept the information. In such a secrecy WPCN, transmit power and transfer time at the power source, and spatial beam at the information source, are interrelated, and together affect the secrecy performance. Considering imperfect channel state information about the eavesdropper channel, a robust resource allocation scheme is proposed for maximizing the secrecy rate in the worst case. Finally, the simulation results validate the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel power beacon-assisted wiretap channel, where an energy constrained source, powered by a dedicated power beacon, aims to establish secure communications with a legitimate user in the presence of an eavesdropper. The power beacon and the source are assumed to have multiple antennas, while the legitimate user and the eavesdropper are equipped with a single antenna each. Considering a time-switching protocol, the power beacon first charges the source through wireless power transfer, and then acts as a friendly jammer to assist the source for secure communication. Depending on the available channel state information (CSI) at the power beacon, we propose several different jamming schemes for secrecy performance enhancement. Closed-form expressions are derived for the achievable secrecy outage probability of the proposed schemes. In addition, simple and accurate approximations are obtained at the high signal-to-noise ratio regime. The outcomes suggest that jamming is an effective approach to improve the secrecy performance: with full CSI available at the power beacon, employing simple zero-forcing scheme attains significant secrecy diversity gain and without the CSI of the eavesdropper link, jamming provides only some array gain. Moreover, we show that the proposed limited feedback-based random beamforming is an effective approach, which may outperform the antenna selection scheme. Furthermore, numerical results reveal that there exists a unique time switching ratio that maximizes the effective secrecy throughput. [ABSTRACT FROM PUBLISHER]

We investigate the secrecy performance of dual-hop amplify-and-forward multi-antenna relaying systems over Rayleigh fading channels, considering the direct link between the source and the destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely: 1) zero-forcing/maximal ratio combining (ZF/MRC); 2) ZF/selection combining (ZF/SC); 3) maximal ratio transmission/MRC (MRT/MRC); and 4) MRT/SC. For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: 1) MRT/MRC and MRT/SC achieve a full diversity order of $M+1$ , ZF/MRC and ZF/SC achieve a diversity order of $M$ , while CJ/ZF only achieves unit diversity order, where $M$ is the number of antennas at the relay; 2) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC (MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR; and 3) all the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC. [ABSTRACT FROM PUBLISHER]

This paper considers a wirelessly powered wiretap channel, where an energy constrained multi-antenna information source, powered by a dedicated power beacon, communicates with a legitimate user in the presence of a passive eavesdropper. Based on a simple time-switching protocol, where power transfer and information transmission are separated in time, we investigate two popular multi-antenna transmission schemes at the information source, namely, maximum ratio transmission and transmit antenna selection. Closed-form expressions are derived for the achievable secrecy outage probability and average secrecy rate for both schemes. In addition, simple approximations are obtained at the high signal-to-noise ratio (SNR) regime. Our results demonstrate that by exploiting the full knowledge of channel state information (CSI), we can achieve a better secrecy performance, e.g., with full CSI of the main channel, the system can achieve substantial secrecy diversity gain. On the other hand, without the CSI of the main channel, no diversity gain can be attained. Moreover, we show that the additional level of randomness induced by wireless power transfer does not affect the secrecy performance in the high SNR regime. Finally, our theoretical claims are validated by the numerical results. [ABSTRACT FROM PUBLISHER]

The vision of seamlessly integrating information transfer (IT) and microwave-based power transfer (PT) in the same system has led to the emergence of a new research area, called wirelessly powered communications (WPC). Extensive research has been conducted on developing WPC theory and techniques, building on the extremely rich wireless communications literature covering diversified topics such as transmission, resource allocation, medium access control, and network protocols and architectures. Despite these research efforts, transforming WPC from theory to practice still faces many unsolved problems concerning issues such as mobile complexity, power transfer efficiency, and safety. Furthermore, the fundamental limits of WPC remain largely unknown. Recent attempts to address these open issues have resulted in the emergence of numerous new research trends in the WPC area. A few promising trends are introduced in this article. From the practical perspective, the use of backscatter antennas can support WPC for low-complexity passive devices, the design of spiky waveforms can improve the PT efficiency, and analog spatial decoupling is proposed for solving the PT-IT near-far problem in WPC. From the theoretic perspective, the fundamental limit of WPC can be quantified by leveraging recent results on super-directivity, and the limit can be improved by the deployment of large-scale distributed antenna arrays. Specific research problems along these trends are discussed, the solutions of which can lead to significant advancements in WPC. [ABSTRACT FROM PUBLISHER]

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multiantenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semidefinite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains. [ABSTRACT FROM AUTHOR]

This paper investigates the average throughput of a wireless-powered communications system, where an energy constrained source, powered by a dedicated power beacon (PB), communicates with a destination. It is assumed that the PB is capable of performing channel estimation, digital beamforming, and spectrum sensing as a communication device. Considering a time-splitting approach, the source first harvests energy from the PB equipped with multiple antennas, and then transmits information to the destination. Assuming Nakagami-$m$ fading channels, analytical expressions for the average throughput are derived for two different transmission modes, namely, delay tolerant and delay intolerant. In addition, closed-form solutions for the optimal time split, which maximize the average throughput are obtained in some special cases, i.e., high-transmit power regime and large number of antennas. Finally, the impact of cochannel interference is studied. Numerical and simulation results have shown that increasing the number of transmit antennas at the PB is an effective tool to improve the average throughput and the interference can be potentially exploited to enhance the average throughput, since it can be utilized as an extra source of energy. Also, the impact of fading severity level of the energy transfer link on the average throughput is not significant, especially if the number of PB antennas is large. Finally, it is observed that the source position has a great impact on the average throughput. [ABSTRACT FROM AUTHOR]

With the growing popularity of mobile Internet, providing secure wireless services has become a critical issue. Physical layer security (PHY-security) has been recognized as an effective means to enhance wireless security by exploiting wireless medium characteristics, for example, fading, noise, and interference. A particularly interesting PHY-security technology is cooperative relay due to the fact that it helps to provide distributed diversity and shorten access distance. This article offers a tutorial on various multi-antenna relaying technologies to improve security at physical layer. The state-of-the-art research results on multi-antenna relay aided PHY-security as well as some secrecy performance optimization schemes are presented. In particular, we focus on large-scale MIMO relaying technology, which is effective in tackling various challenging issues for implementing wireless PHY-security, such as short-distance interception without eavesdropper CSI and with imperfect legitimate CSI. Moreover, the future directions are identified for further enhancement of secrecy performance. [ABSTRACT FROM AUTHOR]

Simultaneous wireless information and power transfer (SWIPT) is a promising solution to increase the lifetime of wireless nodes and hence alleviate the energy bottleneck of energy constrained wireless networks. As an alternative to conventional energy harvesting techniques, SWIPT relies on the use of radio frequency signals, and is expected to bring some fundamental changes to the design of wireless communication networks. This article focuses on the application of advanced smart antenna technologies to SWIPT, including multiple-input multiple-output and relaying techniques. These smart antenna technologies have the potential to significantly improve the energy efficiency and also the spectral efficiency of SWIPT. Different network topologies with single and multiple users are investigated, along with some promising solutions to achieve a favorable trade-off between system performance and complexity. A detailed discussion of future research challenges for the design of SWIPT systems is also provided. [ABSTRACT FROM PUBLISHER]

This paper studies the Demmel condition number of Wishart matrices, a quantity which has numerous applications to wireless communications, such as adaptive switching between beamforming and diversity coding, link adaptation, and spectrum sensing. For complex Wishart matrices, we give an exact analytical expression for the probability density function (p.d.f.) of the Demmel condition number, and also derive simplified expressions for the high tail regime. These results indicate that the condition of complex Wishart matrices is dominantly decided by the difference between the matrix dimension and degree of freedom (DoF), i.e., the probability of drawing a highly ill conditioned matrix decreases considerably when the difference between the matrix dimension and DoF increases. We further investigate real Wishart matrices, and derive new expressions for the p.d.f. of the smallest eigenvalue, when the difference between the matrix dimension and DoF is odd. Based on these results, we succeed to obtain an exact p.d.f. expression for the Demmel condition number, and simplified expressions for the high tail regime. [ABSTRACT FROM AUTHOR]