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Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks. [ABSTRACT FROM AUTHOR]

We design a centralized and a decentralized variational Bayesian learning (C- and D-VBL) algorithms for the base station (BS) of a frequency division duplex massive multiple input multiple output (mMIMO) cellular system, wherein users send compressed information for it to estimate their downlink channels. The BS in the decentralized algorithm consists of multiple processing units (PUs), and each PU separately estimates the channels of a group of users, by employing the proposed D-VBL algorithm. To reduce channel estimation error, the PUs exploit the structured sparsity inherent in multi-user mMIMO channels by exchanging information among themselves. We investigate the proposed C-VBL and low-complexity D-VBL algorithms and show that i) they substantially outperform the state-of-the-art centralized and decentralized algorithms in terms of the normalized mean squared error and the bit error rate. This is because they beneficially exploit the inherent channel sparsity, while the existing state-of-the-art solutions fail to do so. The proposed D-VBL is also robust to PU failures, and provides a similar performance as its centralized counterpart (C-VBL), but with a much reduced complexity. [ABSTRACT FROM AUTHOR]

The envisioned wireless networks of the future entail the provisioning of massive numbers of connections, heterogeneous data traffic, ultra-high spectral efficiency, and low latency services. This vision is spurring research activities focused on defining a next generation multiple access (NGMA) protocol that can accommodate massive numbers of users in different resource blocks, thereby, achieving higher spectral efficiency and increased connectivity compared to conventional multiple access schemes. In this article, we present a multiple access scheme for NGMA in wireless communication systems assisted by multiple reconfigurable intelligent surfaces (RISs). In this regard, considering the practical scenario of static users operating together with mobile ones, we first study the interplay of the design of NGMA schemes and RIS phase configuration in terms of efficiency and complexity. Based on this, we then propose a multiple access framework for RIS-assisted communication systems, and we also design a medium access control (MAC) protocol incorporating RISs. In addition, we give a detailed performance analysis of the designed RIS-assisted MAC protocol. Our extensive simulation results demonstrate that the proposed MAC design outperforms the benchmarks in terms of system throughput and access fairness, and also reveal a trade-off relationship between the system throughput and fairness. [ABSTRACT FROM AUTHOR]

The large-scale multiple-input multiple-output (MIMO) uplink is investigated in the presence of channel-induced uncertainty, where variable-resolution analog-to-digital converters (ADCs) are used at the base station (BS) and a reconfigurable intelligent surface (RIS) is employed for supporting communications between the single-antenna users and the multi-antenna BS. We formally maximize the system throughput by jointly optimizing the ADC’s resolution, the transmit power, the passive reflection coefficients of the RIS and the hybrid combiner of the BS subject to practical constraints under statistical cascaded channel state information (CSI) error model. The robust nonconvex optimization problem is firstly decoupled via the classic Lagrangian dual transform and fractional programming method, followed by a powerful decoupling-based alternating maximization (D-AltMax) algorithm to solve this challenging problem. Our simulation results reveal the supremacy of our proposed algorithm over the benchmark schemes by quantifying the improved system throughput of this robust design. [ABSTRACT FROM AUTHOR]

Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the capacity of wireless networks at a low cost. In practical RIS-assisted communication systems, the acquisition of channel state information (CSI) and RIS reflection optimization constitute a pair of challenges. In this paper, a low-complexity channel estimation and passive beamforming design is proposed. First of all, we conceive a low-complexity framework for maximizing the achievable rate of RIS-assisted multiple-input multiple-output (MIMO) systems having discrete phase shifts at each RIS element. In contrast to existing solutions, the proposed arrangement partitions the channel training stage into several phases, where the RIS reflection coefficients are pre-designed and the effective superposed channel is estimated instead of separately training the source-destination and source-RIS-destination links. Based on this, the active beamformer can be designed at low complexity and the RIS reflection optimization is performed by selecting that one from the pre-designed training set which maximizes the achievable rate. Secondly, we propose novel techniques for generating the training set of RIS reflection coefficients. The theoretical performance of the proposed scheme is analyzed and compared to the optimal RIS configuration. Finally, our simulation results demonstrate that the proposed framework is more competitive than its existing counterparts when relying on imperfect CSI, especially for rapidly time-varying channels having short channel coherence time. [ABSTRACT FROM AUTHOR]

Low-complexity improved-rate generalised spatial modulation (LCIR-GSM) is proposed to mitigate the high complexity of the mapping book design and demodulation of variable- ${N_a}$ GSM. Specifically, first of all, we propose two efficient schemes for mapping the information bits to the transmit antenna activation patterns, which can be readily scaled to massive MIMO setups. Secondly, we derive a pair of low-complexity near-optimal detectors, one of which has a reduced search scope, while the other benefits from a decoupled single-stream based signal detection algorithm. Finally, the performance of the proposed LCIR-GSM system is characterised by the improved error probability upper bound. Our simulation results confirm the improved error performance of our proposed scheme, despite its reduced signal detection complexity. [ABSTRACT FROM AUTHOR]

A sparse channel state information (CSI) estimation model is proposed for reducing the pilot overhead of orthogonal time frequency space (OTFS) modulation aided multiple-input multiple-output (MIMO) systems. Explicitly, the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI that leads to a reduction of the pilot overhead, training duration and pre-processing complexity. Furthermore, it completely avoids placing multiple DD-domain guard intervals corresponding to each transmit antenna within the same OTFS frame, while keeping the training duration flexible, hence increasing the bandwidth efficiency. A unique benefit of the proposed CSI estimation model is that it can efficiently handle fractional Dopplers also. The resultant DD-domain CSI becomes simultaneously row and group (RG)-sparse. To exploit this compelling property, an orthogonal matching pursuit (OMP)-based RG-OMP technique is developed, conveniently complemented by an enhanced Bayesian learning (BL)-based RG-BL framework, both of which substantially outperform the state-of-the-art methods. Furthermore, low-complexity linear detectors are designed for the ensuing data detection phase, which directly employ the estimated DD-domain sparse CSI, without assuming any further knowledge concerning the number of dominant multipath components. Finally, simulation results are provided to demonstrate performance improvement of the proposed BL-based schemes over the OMP and the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

We propose a unitary precoding scheme, namely polar-precoding, to improve the performance of polar-coded MIMO systems. In contrast to the traditional design of MIMO precoding criteria, the proposed polar-precoding scheme relies on the polarization criterion. In particular, the precoding matrix design comprises two steps. After selecting a basic matrix for maximizing the capacity in the first step, we design a unitary matrix for maximizing the polarization effect among the data streams without degrading the capacity. Our simulation results show that the proposed polar-precoding scheme outperforms the state-of-the-art DFT precoding scheme. [ABSTRACT FROM AUTHOR]

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory, presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically, fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applying to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure in and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm. [ABSTRACT FROM AUTHOR]

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier’s quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel’s frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis. [ABSTRACT FROM AUTHOR]

Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a partially connected structure (PCS) and a fully connected structure (FCS), respectively. The decision rules are based on the transmission of local binary sensor decisions and also take into account the accuracy of local detection at the individual sensors. Closed-form analytical expressions are derived for the probabilities of false alarm and correct detection to analyze the system's performance. Furthermore, the asymptotic distributed detection (DD) performance corresponding to both antenna architectures is analyzed in the large-scale antenna regime along with the pertinent power scaling laws. Additionally, digital signaling matrices are designed for enhancing the system performance. Our simulation results quantify the performance gains of the proposed architectures, which closely match the analytical results. [ABSTRACT FROM AUTHOR]

Hybrid analog-digital (A/D) transceivers designed for millimeter wave (mmWave) systems have received substantial research attention, as a benefit of their lower cost and modest energy consumption compared to their fully-digital counterparts. We further improve their performance by conceiving a Tomlinson-Harashima precoding (THP) based nonlinear joint design for the downlink of multiuser multiple-input multiple-output (MIMO) mmWave systems. Our optimization criterion is that of minimizing the mean square error (MSE) of the system under channel uncertainties subject both to realistic transmit power constraint and to the unit modulus constraint imposed on the elements of the analog beamforming (BF) matrices governing the BF operation in the radio frequency domain. We transform this optimization problem into a more tractable form and develop an efficient block coordinate descent (BCD) based algorithm for solving it. Then, a novel two-timescale nonlinear joint hybrid transceiver design algorithm is developed, which can be viewed as an extension of the BCD-based joint design algorithm for reducing both the channel state information (CSI) signalling overhead and the effects of outdated CSI. Moreover, we determine the near-optimal cancellation order for the THP structure based on the lower bound of the MSE. The proposed algorithms can be guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution of the original problem. The simulation results demonstrate that our proposed nonlinear joint hybrid transceiver design algorithms significantly outperform the existing linear hybrid transceiver algorithms and approach the performance of the fully-digital transceiver, despite its lower cost and power dissipation. [ABSTRACT FROM AUTHOR]

We investigate massive access in the Internet-of-Things (IoT) relying on multi-pair two-way amplify-and-forward (AF) relay systems using massive multiple-input multiple-output (MIMO). We utilize the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation. Furthermore, we analyze the achievable rates for multiple pairs of active devices and derive the closed-form expressions for both maximum-ratio combining/maximum-ratio transmission (MRC/MRT) and zero-forcing reception/zero-forcing transmission (ZFR/ZFT)-based beamforming schemes adopted at the relay. Moreover, to improve the achievable sum rates, we propose a low-complexity algorithm for optimizing the pilot length L. Our simulation results verify the accuracy of the closed-form expressions of the MRC/MRT and ZFR/ZFT scenarios. Finally, the proposed pilot-length optimization algorithm performs well in both the MRC/MRT and ZFR/ZFT scenarios. [ABSTRACT FROM AUTHOR]

Polar coding has been ratified for employment in the 3GPP New Radio standard and several soft-decision decoders achieved comparable performance to that of the state-of-the-art successive cancellation list decoder. Aiming for further improving the performance of the soft-decision polar decoders, we propose a soft-output successive cancellation stack (SSCS) polar decoder, which jointly exploits the benefits of the depth-first search of the stack decoder and the soft information output of the belief propagation decoder. This has the substantial benefit of facilitating soft-input soft-output (SISO) decoding and seamless iterative information exchange in turbo-style receivers. As a further contribution, we intrinsically amalgamate our SSCS decoder into polar-coded large-scale multiple-input multiple-output (MIMO) systems and conceive an iterative turbo receiver, operating on the basis of logarithmic likelihood ratios (LLRs). Our simulation results show that the proposed SSCS decoder is capable of outperforming the state-of-the-art SISO polar decoders, despite requiring a lower complexity at moderate to high signal-to-noise ratios (SNRs). Additionally, compared with the non-iterative hard-output SCS decoder, our SSCS scheme attained 1.5 dB SNR gain at a bit error ratio level of 10−5, when decoding the [256,512] polar code of a (64 × 64) MIMO system. [ABSTRACT FROM AUTHOR]

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes. [ABSTRACT FROM AUTHOR]

A large-scale clustered massive MIMO network is proposed for improving the spectral efficiency of the next-generation wireless infrastructure by maximizing its sum-rate. Our solution combines the advantages of the centralized processing architecture and massive MIMO. Explicitly, the network is divided into multiple clusters; each cluster is handled by a centralized processing unit, which connects to a certain number of massive MIMO-aided BSs, where only limited information is exchanged among the clusters; each user of a cluster can be served by several nearby BSs in a user-centric way. We analyze the maximum sum-rate of the network with multiple antennas at BSs and UEs, relying on the optimal transmit precoder matrix of each BS configured for each user, and on the optimal frequency-domain power sharing scheme of each cluster. The optimizations are conceived for multiple coordination schemes that were widely studied in literature, namely the coherent-joint-transmission (CJT) scheme, the non-coherent-joint-transmission (NCJT) scheme and the coordinated-beamfoming/scheduling (CBF/CS) scheme. Our simulations show that the optimal CJT achieves 2.2 – 4.5 times higher average sum-rate than its non-cooperative massive MIMO network counterpart, while the optimal NCJT and the optimal CBF/CS achieve at most a factor 1.3 average sum-rate gain. The popular signal-to-leakage-and-noise-ratio (SLNR) scheme is also extended to the multi-antenna UE scenario and achieves a factor 1.1 – 1.2 gain. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS-transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two-stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems. [ABSTRACT FROM AUTHOR]

The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas. [ABSTRACT FROM AUTHOR]

Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signal-to-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart. [ABSTRACT FROM AUTHOR]

A probabilistically robust transmit beamforming problem is referred, when the wireless downlink (DL) communication is supported by a robust non-orthogonal transmission (NOT)-aided design. Realistic imperfect channel state information (CSI) is considered in the face of rapidly fluctuating vehicular wireless channels, when the road side unit (RSU) communicates with multiple vehicles. Our design objective is to keep the probability of each vehicle’s signal-to-interference-plus-noise ratio (SINR) outage below a given threshold. Minimizing the outage probability presents a significant analytical and computational challenge, since it does not lend itself to tractable closed-form expressions. Assuming a Gaussian CSI uncertainty distribution, we provide an approximation method by resorting to the semidefinite relaxation (SDR) and then apply a convex restriction to the original SINR outage constraints. Furthermore, the infinite constraints are reformulated into linear matrix inequalities (LMIs) by exploiting the popular S-procedure. As a benefit, the reformulated program can be solved efficiently using off-the-shelf solvers. Computer simulations are performed for benchmarking our convex method both against the non-robust non-orthogonal as well as the classical orthogonal designs. The results show that our robust beamforming design offers excellent high-mobility performance. [ABSTRACT FROM AUTHOR]

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users’ confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption. [ABSTRACT FROM AUTHOR]

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a structured orthogonal matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media-modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications. [ABSTRACT FROM AUTHOR]

The densely packed antennas of millimeter-Wave (mmWave) MIMO systems are often blocked by the rain, snow, dust and even by fingers, which will change the channel‘s characteristics and degrades the system's performance. In order to solve this problem, we propose a cross-entropy inspired antenna array diagnosis detection (CE-AAD) technique by exploiting the correlations of adjacent antennas, when blockages occur at the transmitter. Then, we extend the proposed CE-AAD algorithm to the case, where blockages occur at transmitter and receiver simultaneously. Our simulation results show that the proposed CE-AAD algorithm outperforms its traditional counterparts. [ABSTRACT FROM AUTHOR]

Both time-switching (TS) and power splitting has been used at the receiver for wireless information and power transfer in the downlink of massive multiple-input-multiple-output systems. By contrast, this correspondence adopts the transmit-TS approach, where the energy and information are transferred over different fractions of a time slot. Our goal is to jointly optimize the transmit-TS factor and power allocation coefficients during energy and information transfer for maximizing the users’ minimum throughput subject to transmit power and minimum harvested energy constraints. This nonconvex problem is solved by our path following algorithm. Our simulation results demonstrate the benefits of the proposed transmit-TS algorithm, which easily doubles the throughput compared to that of the existing techniques. [ABSTRACT FROM AUTHOR]

This paper analyses the performance of filter bank multicarrier (FBMC) signaling in conjunction with offset quadrature amplitude modulation (OQAM) in multi-user (MU) massive multiple-input multiple-output (MIMO) systems. Initially, closed form expressions are derived for tight lower bounds corresponding to the achievable uplink sum-rates for FBMC-based single-cell MU massive MIMO systems relying on maximum ratio combining (MRC), zero forcing (ZF) and minimum mean square error (MMSE) receiver processing with/without perfect channel state information (CSI) at the base station (BS). This is achieved by exploiting the statistical properties of the intrinsic interference that is characteristic of FBMC systems. Analytical results are also developed for power scaling in the uplink of MU massive MIMO-FBMC systems. The above analysis of the achievable sum-rates and corresponding power scaling laws is subsequently extended to multi-cell scenarios considering both perfect as well as imperfect CSI, and the effect of pilot contamination. The delay-spread-induced performance erosion imposed on the linear processing aided BS receiver is numerically quantified by simulations. Numerical results are presented to demonstrate the close match between our analysis and simulations, and to illustrate and compare the performance of FBMC and traditional orthogonal frequency division multiplexing (OFDM)-based MU massive MIMO systems. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) multiple-input multiple-out (MIMO) systems relying on lens antenna arrays are capable of achieving a high antenna-gain at a considerably reduced number of radio frequency (RF) chains via beam selection. However, the traditional beam selection network suffers from significant performance loss in wideband systems due to the effect of beam squint. In this paper, we propose a phase shifter-aided beam selection network, which enables a single RF chain to support multiple focused-energy beams, for mitigating the beam squint in wideband mmWave MIMO systems. Based on this architecture, we additionally design an efficient transmit precoder (TPC) for maximizing the achievable sum-rate, which is composed of beam selection and beamspace precoding. Specifically, we decouple the design problems of beamspace precoding and beam selection by exploiting the fact that the beam selection matrix has a limited number of candidates. For the beamspace precoding design, we propose a successive interference cancellation (SIC)-based method, which decomposes the associated optimization problem into a series of subproblems and solves them successively. For the beam selection design, we propose an energy-max beam selection method for avoiding the high complexity of exhaustive search, and derive the number of required beams for striking an attractive trade-off between the hardware cost and system performance. Our simulation results show that the proposed beamspace precoding and beam selection methods achieve both a higher sum-rate and a higher energy efficiency than its conventional counterparts. [ABSTRACT FROM AUTHOR]

A new joint best relay and jammer selection (JBRJS) scheme is conceived for enhancing the physical layer security (PLS) of cooperative networks relying on multiple energy harvesting (EH) aided intermediate nodes, which accumulate energy based on the power splitting (PS) protocol. Specifically, we select the best intermediate node as the relay, whilst exploiting all the remaining nodes as friendly jammers. Furthermore, we investigate the joint optimization of the PS ratio and the relay-aided beamforming for maximizing the system’s secrecy rate and present a full channel state information (CSI) based joint PS and beamforming ($\it {f}$ JPSB) scheme as the optimal solution by converting the optimization problem formulated into a single-variable optimization problem. We also propose a partial-CSI based JPSB ($\it {p}$ JPSB) method for the scenario where only the main link’s CSI is available. Our numerical results show that the proposed JBRJS scheme beneficially enhances the PLS compared to the joint random relay and jammer selection (JRRJS) and to the pure best relay selection (PBRS) schemes. Moreover, the secrecy rate of the proposed $\it {f}$ JPSB and $\it {p}$ JPSB schemes is obviously higher than that of the fixed PS and beamforming (FPSB) method, and it further increases with the number of the relay’s transmit antennas. [ABSTRACT FROM AUTHOR]

Hybrid precoding is essential for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems due to its inherent advantage of a high gain, whilst alleviating the high cost of hardware. However, most of the existing literature considered either the narrowband or wideband single-user mmWave MIMO scenarios. Hence in this paper we focus our attention on the more challenging design of hybrid Transmit Precoding (TPC) for wideband multiuser mmWave MIMO systems by exploiting the long-term channel’s covariance matrix and the angle of departure (AoD) information. Specifically, in the analog TPC designed, firstly, the analog TPC matrix having an infinite angular resolution is constructed based on the channel’s covariance matrix. Then, we also propose a non-uniformly spaced quantization codebook based analog TPC having finite angular resolution. Furthermore, a phase compensation operation is carried out to alleviate the effect of beam squint. As for the design of the digital TPC, a two-stage scheme is proposed to cancel the inter-user interference and to attain multiplexing gains. We study the effects of various parameters on the achievable sum rate and demonstrate with the aid of our simulation results that the proposed hybrid TPC is capable of achieving a similar performance to the excessive-complexity fully digital TPC. [ABSTRACT FROM AUTHOR]

Beamspace channel estimation is indispensable for millimeter-wave MIMO systems relying on lens antenna arrays for achieving substantially increased data rates, despite using a small number of radio-frequency chains. However, most of the existing beamspace channel estimation schemes have been designed for narrowband systems, while the rather scarce wideband solutions tend to assume that the sparse beamspace channel exhibits a common support in the frequency domain, which has a limited validity owing to the effect of beam squint caused by the wide bandwidth in practice. In this paper, we investigate the wideband beamspace channel estimation problem without the common support assumption. Specifically, by exploiting the effect of beam squint, we first prove that each path component of the wideband beamspace channel exhibits a unique frequency-dependent sparse structure. Inspired by this structure, we then propose a successive support detection (SSD) based beamspace channel estimation scheme, which successively estimates all the sparse path components following the classical idea of successive interference cancellation. For each path component, its support at different frequencies is jointly estimated to improve the accuracy by utilizing the proved sparse structure, and its influence is removed to estimate the remaining path components. The performance analysis shows that the proposed SSD-based scheme can accurately estimate the wideband beamspace channel at a low complexity. Simulation results verify that the proposed SSD-based scheme enjoys a reduced pilot overhead, and yet achieves an improved channel estimation accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we consider a generalized sub-array-connected (GSAC) architecture for arbitrary radio frequency (RF) chain and antenna configurations, where the number of RF chains connected to a sub-array and the number of antennas in each sub-array can be arbitrary. Our design objective is to improve the energy efficiency of the hybrid precoder of millimeter-wave massive multiple input multiple output systems. We first propose a successive interference cancellation based hybrid precoding algorithm to maximize the achievable rate for any given RF chain and antenna configuration. This algorithm first decomposes the total achievable rate optimization problem into multiple sub-rate optimization problems, then it successively maximizes these sub-rates. Since the number of RF chains is limited, we can afford using an exhaustive search scheme to evaluate all configurations and identify the one having the best energy efficiency. Moreover, to rely on an attractive limited feedback, we also propose a beamsteering codebook for our hybrid precoding aided GSAC architecture. Our simulation results demonstrate that the proposed scheme achieves a similar rate as the corresponding optimal unconstrained precoder. Furthermore, we show that the energy efficiency of the proposed scheme is better than that of the existing schemes in the fully connected and sub-array-connected architectures. [ABSTRACT FROM AUTHOR]

A novel multiple-input and multiple-output (MIMO) transmission scheme termed as generalized quadrature spatial modulation (G-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and spatial multiplexing for the sake of achieving a high throughput, despite relying on a low number of radio frequency (RF) chains. In the proposed G-QSM scheme, the conventional constellation points of the spatial multiplexing structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by the classic amplitude/phase modulated (APM) constellation points. The upper bounds of the average bit error probability (ABEP) of the proposed G-QSM system in high throughput massive MIMO configurations are derived. Furthermore, an efficient multipath orthogonal matching pursuit (EM-OMP)-based compressive sensing (CS) detector is developed for our proposed G-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes in massive MIMO configurations. [ABSTRACT FROM AUTHOR]

To reduce the pilot overhead of downlink channel estimation in massive multiple-input–multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector approximate message passing (VAMP) technique is proposed. More specifically, an a-priori channel model characterized by a multivariate Bernoulli–Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels. Moreover, the hyperparameters of the a-priori model are learned by invoking the expectation maximization algorithm. Our numerical results demonstrate that the proposed algorithm is capable of reducing the pilot overhead by 50% in massive MIMO systems. [ABSTRACT FROM AUTHOR]

In this letter, we conceive a multilayered coding scheme for the recently proposed layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) system, which exploits its layered structure for protecting the information bits transmitted in the base layer. Explicitly, we intrinsically integrate error correction into the layered structure of the LACO-OFDM system and invoke soft iterative decoding between the layers at the receiver. Our system operates within 2 dB of the capacity limit at a bit error ratio of $10^{-4}$. [ABSTRACT FROM AUTHOR]

The limited fronthaul capacity imposes a challenge on the uplink of a centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of a massive multiple-input multiple-output (MIMO)-aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise ratio for the massive MIMO-aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs’ power sharing factors and the RRUs’ power sharing factors aiming at maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by the RRU and 128 RAs deployed by the BBU, the sum rate of 10 UDs achieved with the optimal power sharing factors improves by 33% compared with the one attained without optimizing power sharing factors. [ABSTRACT FROM AUTHOR]

Harnessing the abundant availability of spectral resources at millimeter wave (mmWave) frequencies is an attractive solution to meet the escalating data rate demands. Additionally, it has been shown that full-duplex (FD) communication has the potential of doubling the bandwidth efficiency. However, the presence of significant residual self-interference (SI), which is especially more pronounced at mmWave frequencies because of the non-linearities in the hardware components, erodes the full potential of FD in practice. Conventionally, the residual SI is canceled in the baseband using digital processing with the aid of a transmit precoder. In this work, we propose a hybrid beamforming design for FD mmWave communications, where the SI is canceled by the joint design of beamformer weights at the radio-frequency and the precoder as well as combiner in the baseband. Our proposed design preserves the dimensions of the transmit signal, while suppressing the SI. We demonstrate that our joint design is capable of reducing the SI by upto 30 dB, hence performing similarly to the interference-free FD system while being computationally efficient. Our simulation results show that the proposed design significantly outperforms eigen-beamforming. [ABSTRACT FROM AUTHOR]

The maturing massive multiple-input multiple-output (MIMO) literature has provided asymptotic limits for the rate and energy efficiency (EE) of maximal ratio combining/maximal ratio transmission (MRC-MRT) relaying on two-way relays (TWRs) using the amplify-and-forward (AF) principle. Most of these studies consider time-division duplexing and a fixed number of users. To fill the gap in the literature, we analyze the MRC-MRT precoder performance of an $N$ -antenna AF massive MIMO TWR, which operates in a frequency-division duplex mode to enable two-way communication between $2M=\lfloor N^{\alpha }\rfloor $ single-antenna users, with $\alpha \in [0,1$), divided equally into two groups of $M$ users. We assume that the relay has realistic imperfect uplink channel state information (CSI), and that quantized downlink CSI is fed back by the users relying on $B\geq 1$ bits per-user per relay antenna. We prove that for such a system with $\alpha \in [0,1$), the MRC-MRT precoder asymptotically cancels the multi-user interference (MUI) when the supremum and infimum of large-scale fading parameters are strictly non-zero and finite, respectively. Furthermore, its per-user pairwise error probability converges to that of an equivalent AWGN channel, as both $N$ and the number of users $2M=\lfloor N^{\alpha }\rfloor $ tend to infinity, with a relay power scaling of $P_{r}=({2ME_{r}}/{N})$ and $E_{r}$ being a constant. We also derive upper bounds for both the per-user rate and EE. We analytically show that the quantized MRC-MRT precoder requires as few as $B=2$ bits to yield a BER, EE, and per-user rate close to the respective unquantized counterparts. Finally, we show that the analysis developed herein to derive a bound on $\alpha $ for MUI cancellation is applicable both to Gaussian as well as to any arbitrary non-Gaussian complex channels. [ABSTRACT FROM AUTHOR]

The matrix-based differential encoding invoked by Differential Space-Time Modulation (DSTM) typically results in an infinite-cardinality of arbitrary signals, despite the fact that the transmit antennas (TAs) can only radiate a limited number of patterns. As a remedy, the recently developed differential spatial modulation (DSM) is capable of avoiding this problem by conceiving a beneficial sparse signal matrix design, which also facilitates low-complexity single-RF signal transmission. Inspired by this development, the differential space-time block code using index shift keying (DSTBC-ISK) further introduces a beneficial diversity gain without compromising the DSM’s appealingly low transceiver complexity. However, the DSTBC-ISK’s performance advantage tends to diminish as the throughput increases, especially when an increased number of Receive Antennas (RAs) is used. By contrast, the classic Differential Group Code (DGC) that actively maximizes its diversity gain for different multiple-input-multiple-output (MIMO) system setups is capable of achieving a superior performance, but its detection complexity grows exponentially with the throughput. Against this background, we propose the differential space-time shift keying using Diagonal Algebraic Space-Time scheme, which is the first DSTM that is capable of achieving the DGC’s superior diversity gain at high throughputs without compromising the DSM’s low transceiver complexity. As a further advance, we also conceive a new differential space-time shift keying using Threaded Algebraic Space-Time arrangement, which is capable of achieving an even further improved diversity gain at a substantially reduced signal detection complexity compared to the best DGCs. Furthermore, in order to strike a practical tradeoff, we develop a generic multi-element and multi-level-ring Amplitude Phase Shift Keying design, and we also arrange for multiple reduced-size DSTM sub-blocks to be transmitted in a permuted manner, which exhibits an improved diversity-throughput tradeoff. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple yet powerful mapping scheme that converts any conventional square-matrix-based differential space-time coding (DSTC) into a nonsquare-matrix-based DSTC. This allows DSTC schemes to be used practically in open-loop large-scale multiple-input multiple-output scenarios. Our proposed scheme may be viewed as the differential counterpart of coherent spatial modulation (SM), of the generalized SM, of Bell Laboratories layered space-time architecture, and of subcarrier-index modulation. The fundamental impediment of the existing DSTC schemes is the excessive complexity imposed by the unitary constraint. Specifically, the transmission rate of conventional DSTC schemes decays as the number of transmit antennas increases. Our proposed scheme eliminates this impediment and thus achieves a significantly higher transmission rate. We introduce four novel construction methods for the nonsquare codewords, some of which include an arbitrary number of nonzero elements in each codeword column. Our analysis shows that the proposed encoding technique reduces the complexity of both the inverse Fourier transform and the detection processes. Our proposed scheme is shown to approach the performance of its coherent counterpart for low-mobility scenarios, where the number of transmit antennas is increased up to 256. [ABSTRACT FROM AUTHOR]

A novel multiple-input and multiple-output transmission scheme termed as space–time block coded quadrature spatial modulation (STBC-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and space–time block coding (STBC) to exploit the diversity benefits of STBC relying on sparse radio frequency chains. In the proposed STBC-QSM scheme, the conventional constellation points of the STBC structure are replaced by the QSM symbols; hence, the information bits are conveyed both by the antenna indices as well as by conventional STBC blocks. Furthermore, an efficient Bayesian compressive sensing (BCS) algorithm is developed for our proposed STBC-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes. Moreover, the proposed BCS detector is capable of approaching the maximum likelihood detector's performance despite only imposing a complexity near similar to that of the minimum mean square error detector in the high signal-to-noise ratio regions. [ABSTRACT FROM AUTHOR]

This correspondence proposes a novel multiuser computational offloading scheme for a fog-based scenario. Specifically, a controlling user (CU) distributes its computational tasks to multiple trusted helping users (HUs) by exploiting both nonorthogonal multiple access (NOMA) and beamforming. First, social relationships between the CU and HUs are exploited for the selection of trusted HUs. Then, the HUs having sufficient computing resources and high social trust are selected and grouped into multiple pairs. For the sake of improving the spectral efficiency, NOMA is invoked for simultaneously supporting several HUs. Meanwhile, a process of zero-forcing beamforming is used by the CU to completely eliminate the interpair interference, hence further enhancing the sum rate. By analyzing the achievable rate and energy consumption of the uplink and downlink transmission, an energy consumption minimization problem is formulated subject to the constraints of signal-to-interference plus noise ratio and transmission latency. We design a user-pairing and resource allocation algorithm for successively optimizing the power allocation and computational task distribution, while satisfying the classic Karush–Kuhn–Tucker conditions. Our numerical results demonstrate that by combining the social relationships, the computational capacities, and the channel conditions, the proposed multiuser computational offloading scheme relying on social trust, NOMA, and beamforming improves the energy consumption, sum rate, and transmission latency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a combined full duplex (FD)- and half duplex (HD)-based transmission and channel acquisition model for an open-loop multiuser multiple-input multiple-output (MIMO) systems. Assuming residual self-interference at the base station (BS), the idea is to utilize the FD mode during the uplink (UL) training phase in order to achieve simultaneous downlink (DL) data transmission and UL CSI acquisition. More specifically, the BS begins serving a user when its CSI becomes available, while at the same time, it also receives UL pilots from the next scheduled user. We investigate both zero-forcing (ZF) and maximum ratio transmission MIMO beamforming techniques for the DL data transmission in the FD mode. The BS switches to the HD mode once it receives the CSI of all users and it employs ZF beamforming for the DL data transmission until the end of the transmission frame. Furthermore, we derive closed-form approximations for the lower bounded ergodic achievable rate relying on the proposed model. Our numerical results show that the proposed FD–HD transmission and channel acquisition approach outperforms its conventional HD counterpart and achieves higher data rates. [ABSTRACT FROM AUTHOR]

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors. [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) frequency band spanning from 30 to 300 GHz constitutes a substantial portion of the unused frequency spectrum, which is an important resource for future wireless communication systems in order to fulfill the escalating capacity demand. Given the improvements in integrated components and enhanced power efficiency at high frequencies, wireless systems can operate in the mmWave frequency band. In this paper, we present a survey of the mmWave propagation characteristics, channel modeling, and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mmWave communication systems. We commence by introducing the main channel propagation characteristics of mmWaves followed by channel modeling and design guidelines. Then, we report on the main measurement and modeling campaigns conducted in order to understand the mmWave band’s properties and present the associated channel models. We survey the different channel models focusing on the channel models available for the 28, 38, 60, and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design. [ABSTRACT FROM AUTHOR]

We consider two-way amplify-and-forward relaying, where multiple full-duplex user pairs exchange information via a shared full-duplex massive multiple-input multiple-output (MIMO) relay. Most of the previous massive MIMO relaying works maximize the spectral efficiency (SE). By contrast, we maximize the non-convex energy efficiency (EE) metric by approximating it as a pseudo-concave problem, which is then solved using the classic Dinkelbach approach. We also maximize EE of the least energy-efficient user relying on the max–min approach. We also compare SE and EE of the proposed design with existing full-duplex systems and quantify the significant improvement achieved by the proposed algorithm. We also compare EE of the proposed full-duplex system to that of its half-duplex counterparts, and characterize the self-loop and inter-user interference regimes, for which the proposed full-duplex system outperforms the half-duplex ones. [ABSTRACT FROM AUTHOR]

Nowadays, mmWave MIMO systems are favorable candidates for 5G cellular systems. However, a key challenge is the high power consumption imposed by its numerous RF chains, which may be mitigated by opting for low-resolution ADCs, while tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges, including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system with ADC quantizers are discussed. [ABSTRACT FROM AUTHOR]

Spatial modulation (SM) constitutes an appealing low-complexity single radio frequency (RF) aided multiple-input multiple-output technique. Conventional SM schemes tend to rely on transmit antenna (TA) configurations, where the number of TAs is a power of two. In order to circumvent this limitation, a novel single RF-aided SM conceived for an arbitrary number of TAs (SM-ATA) is proposed, which is then further developed to a twin-RF enhanced SM-ATA (ESM-ATA) schedule for exploiting the diversity advantage of space-time block coding. Furthermore, low-complexity near-optimal detectors are designed for both the SM-ATA and ESM-ATA schemes. Our simulation results show that the proposed SM-ATA schemes offer almost the same performance as conventional SM systems, despite using a reduced number of TAs at the same transmission rate. The proposed twin-RF ESM-ATA schemes provide a beneficial performance gain over the existing twin-RF multiplexing based and space-time coding based SM schemes. Finally, an upper bound is derived for the average bit error probability, which is confirmed by our simulation results. [ABSTRACT FROM AUTHOR]

Given the scarcity of spectral resources in traditional wireless networks, it has become popular to construct visible light communication (VLC) systems. They exhibit high energy efficiency, wide unlicensed communication bandwidth as well as innate security; hence, they may become part of future wireless systems. However, considering the limited coverage and dense deployment of light-emitting diode (LED) lamps, traditional network association strategies are not readily applicable to VLC networks. Hence, by exploiting the power of online learning algorithms, we focus our attention on sophisticated multi-LED access point selection strategies conceived for hybrid indoor LiFi-WiFi communication systems. We formulate a multi-armed bandit model for supporting the decisions on beneficially selecting LED access points. Moreover, the ‘exponential weights for exploration and exploitation’ algorithm and the ‘exponentially weighted algorithm with linear programming’ algorithm are invoked for updating the decision probability distribution, followed by determining the upper bound of the associated accumulation reward function. Significant throughput gains can be achieved by the proposed network association strategies. [ABSTRACT FROM PUBLISHER]