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Single carrier modulation (SCM) schemes are attractive for uplink (UL) transmissions due to improved power efficiency at the user equipment (UE) transmitter compared with multi-carrier modulation schemes. In a massive MIMO scenario with SCM, the UL detection must mitigate the effects of inter-symbol interference and multi-user interference. This processing is effectively performed in the frequency domain (FD) using a minimum mean squared error (MMSE) detector when the transmission is framed with a cyclic prefix. This paper analyzes an MMSE-based detector compatible with distributed processing in a time-division duplex (TDD) system. The matrix inverses computed for the UL detection are then reused to perform multi-user precoding for the downlink (DL). We find that this scheme yields tremendous savings in computational complexity compared to commonly used zero-forcing (ZF) precoding without sacrificing any performance. Since MMSE processing introduces a bias to the estimates, we derive the scalar coefficients necessary to cancel the MMSE bias. The impact of channel estimation errors is analyzed for both the UL and DL cases in conjunction with a power-efficient approach to SCM channel estimation. Moreover, extensive simulations are performed to confirm our theoretical findings.

In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI error model for primary user (PU)-related channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) at the secondary user (SU) transmitter (ST) and phase shifts at the IRS to minimize the ST' s total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. The successive convex approximation (SCA) method, Schur's complement, General sign-definiteness principle, inverse Chi-square distribution and penalty convex-concave procedure are invoked for dealing with these intricate constraints. The non-convex optimization problems are transformed into several convex subproblems and efficient algorithms are proposed. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems. Simulation results also show that the number of transmit antennas at the ST and the number of phase shifts at the IRS should be carefully chosen to balance the channel realization feasibility rate and the total transmit power., Accepted by IEEE Transactions on Cognitive Communications and Networking. Keywords: Reconfigurable Intelligent Surface, Intelligent Reflecting Surface, Cognitive Radio Networks, Robust Design, Imperfect CSI

Massive MIMO and mmWave communication are the technologies for achieving 5G design goals. Fortunately, these two technologies share a symbiotic integration. As a result, amalgamating mmWave communications with massive MIMO forms,mmWave-massive MIMO,” which significantly improves spectral and energy efficiency. It also achieves high multiplexing gains and increases mobile network capacity. However, massive MIMO, mmWave communications, and mmWave-massive MIMO systems have been studied independently. Consequently, this article explores the ideas, performances, comparisons, and discussions of these three 5G technologies jointly, considering their precoding and beamforming methods. On the other hand, the complexity of these technologies increases when a large number of antennas and radio frequencies (RFs) are used. Thus, several investigations are going on to search for the appropriate precoding and beamforming strategies with low cost, power, and complexity. Therefore, massive MIMO linear precoding techniques such as zero-forcing, maximum ratio transmission, regularized zero-forcing, truncated polynomial expansion and phased zero forcing are addressed in this work. In addition, the most common non-linear precoding schemes: dirty-paper coding, Tomlinson-Harashima, and vector perturbation, are presented. Furthermore, a detailed discussion of the beamforming techniques called analog, digital, and hybrid analog-digital beamforming schemes is included. We also examine the potential of hybrid analog-digital beamforming with its fully-connected and sub-connected architecture approaches in making mmWave massive MIMO a reality. We evaluate their performance with the parameters: bit error rate, signal to noise ratio, complexity, spectral efficiency, and energy efficiency. According to the analytical and simulation results, the partially-connected hybrid analog/digital beamforming architecture offers better all-over performance for mmWave-Massive MIMO communications by compromising: power consumption, cost, complexity, and performance. Finally, the potential future directions in mmWave-massive MIMO precoding and beamforming challenges are addressed.

Owing to the flexible mobility and convenient deployment, unmanned aerial vehicle (UAV) aided networks can facilitate many applications. However, the open accessibility of wireless communication brings a great risk of privacy to UAV-based networks. Thus, in this paper, we propose a UAV-aided covert communication scheme assisted by a multi-antenna jammer to maximize the transmission rate between a ground transmitter and a UAV receiver against several randomly distributed wardens. The transmitter adopts the maximum ratio transmission, while the jammer zero-forces its transmitted signal at the UAV to disturb the monitoring at wardens without interfering the legitimate transmission. First, we analyze the detection performance and derive the optimal threshold for each warden to minimize its detection outage probability (DOP). Then, with the worst situation in which all wardens set their respective optimal thresholds to achieve the minimum global DOP, the location and the transmit power of the jammer are optimized to maximize the DOP. The location of UAV and the transmit power of the ground transmitter are also optimized to maximize the transmission rate with the minimum DOP requirement satisfied. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-aided covert communication scheme.

In this letter, we propose a training based hybrid precoding scheme for multiuser massive MIMO-OFDM time-division duplexing (TDD) systems, where we combine hybrid precoding design with channel estimation in a mixed-timescale structure. Firstly, we design a training based fully-digital precoder/combiner using the statistics of the transmitted and received signal under minimum mean square error (MMSE) criterion through an iterative process between downlink (DL) and uplink (UL), from which the radio frequency (RF) precoder/combiner is extracted using principal component analysis (PCA). Secondly, a low dimensional baseband (BB) channel is estimated and the BB precoder/combiner is designed by block diagonalization (BD) with the estimated BB channel. Thirdly, we discuss the transmission strategy for our proposed scheme in block-fading channels and limited feedback systems. Finally, we evaluate the sum spectral efficiency (SSE) and bit error rate (BER) performance of our proposed scheme and prove that our scheme has better performance compared to the existing schemes.

In this paper, the emerging orthogonal time frequency space (OTFS) modulation is firstly restructured as a precoded orthogonal frequency division multiplexing (OFDM) system, so that the well-established frequency-domain approach can be applied to perform signal in fast fading channels. Then a frequency-domain minimum mean squared error (MMSE) equalizer for OTFS is introduced and its performance is analyzed based on the eigenvalue decomposition of the channel matrix. Inspired by the frequency-domain precoding structure, an adaptive transmission scheme with frequency-domain precoding matrix composed of the eigenvectors of the channel matrix is proposed to improve the system performance under MMSE equalization, and its optimized performance is derived with simple expression. Finally, considering two extreme channel conditions, the lower and upper bounds for the diversity performance of the adaptive transmission scheme are derived. Simulation results show that the proposed adaptive transmission achieves significantly better performance for short signal frames and can work well with imperfect channel state information (CSI). The derived performance bounds can serve as benchmarks for OTFS and other precoded OFDM systems.

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS, and the user equipment (UE), is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead.

This letter describes a wideband precoding design for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems under per-antenna power constraints, wherein all subcarriers share one wideband precoder. With new insight into the objective function, we first reformulate the precoding designing as a stochastic optimization problem. Then, an algorithm based on the free probability theory is proposed to solve the stochastic optimization problem; consequently, a wideband precoder is obtained, with a computational complexity nearly equivalent to that required for a single narrowband precoder. The effectiveness of this wideband precoder is validated through simulations.

Generally, the downlink channel state information (CSI) used for precoding is conveyed back in massive multi-input multi-output (MIMO) systems with frequency division duplex (FDD) mode, which costs too much bandwidth. However, most of CSI compression algorithms including deep learning based schemes show poor performance with high compression ratio (CR) in ourdoor scenery. In this letter, we propose a new neural network that utilizes the deep neural network (DNN) to improve the learning ability of residual network and a novel activation function to adapt the neural network. Simulation results show that the proposed CsiNet+DNN method exhibits better performance than exsiting methods in recovery quality and accuracy, which also achieves remarkable robustness at noise situations.

We characterize the capacity region of the two-user broadcast packet erasure channel (PEC) with single-user delayed channel state information (CSI). More precisely, we assume one receiver does not provide its channel state to the other two nodes (the other receiver and the transmitter), while the other receiver reveals its state globally with unit delay. This is a hybrid CSI at the transmitter (CSIT) setting where the transmitter has the delayed CSI of one user but not the other. Previous results developed opportunistic network coding schemes for this setting, which strictly enlarge the achievable rate region compared to the no-CSIT baseline. Characterization of the capacity region with single-user delayed CSI, however, remained open. In this work, we develop an improved achievability strategy and show that the capacity region, surprisingly, matches that of the broadcast PEC with global delayed CSI of both users. The key to such improvement over previous results is a new precoding strategy for the retransmission phase of the opportunistic network coding scheme. It harnesses the single-user delayed CSI in the retransmission phase, so that interference from the feedback receiver can be aligned at the other receiver. Besides the broadcast PEC with two private messages, an extension to a model with an additional common message is also provided and the corresponding capacity region with single-user CSI also matches that with global delayed CSI. Finally, further extensions to three-user cases are also provided.

Massive multiple-input-multiple-output (MIMO) systems use few hundred antennas to simultaneously serve large number of wireless broadband terminals. It has been incorporated into standards like long term evolution (LTE) and IEEE802.11 (Wi-Fi). Basically, the more the antennas, the better shall be the performance. Massive MIMO systems envision accurate beamforming and decoding with simpler and possibly linear algorithms. However, efficient signal processing techniques have to be used at both ends to overcome the signaling overhead complexity. There are few fundamental issues about massive MIMO networks that need to be better understood before their successful deployment. In this paper, we present a detailed review of massive MIMO homogeneous, and heterogeneous systems, highlighting key system components, pros, cons, and research directions. In addition, we emphasize the advantage of employing millimeter wave (mmWave) frequency in the beamforming, and precoding operations in single, and multi-tier massive MIMO systems. Keywords: 5G wireless networks; massive MIMO; linear precoding; encoding; channel estimation; pilot contamination; beamforming; HetNets

This paper investigates the secrecy performance of the Cell-Free massive multiple-input multiple-output network with finite resolution analog-to-digital converters at the access points (APs) and users in presence of an active eavesdropper. Using the additive quantization noise model, the uplink minimum mean squared error channel estimation and downlink data precoding are respectively operated. Specifically, the lower bound on the achievable ergodic rate and upper bound for the information leakage to the eavesdropper are theoretically derived in details. Thereby, the closed-form expression for the achievable ergodic secrecy rate is accordingly obtained with respect to the number of APs, number of each APs antenna, number of users, pilot and data transmission power and quantization bits, etc. In addition, the asymptotic approximation for the ergodic secrecy rate has been presented. Moreover, the path-following power control algorithm has been proposed aiming at maximizing the secrecy rate subject to both power and achievable rate constraints. Finally, extensive simulations are provided to corroborate the theoretical analytical results and the validity of the proposed power allocation scheme.

Channel state information (CSI) feedback is a key technical challenge for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a two-stage adaptive and compressed CSI feedback scheme to reduce the CSI feedback overhead and improve the feedback accuracy. Specifically, a binary angle-of-departure (AoD) vector and the associated angular-domain channel coefficients are first extracted from the massive MIMO channel. Then, by exploiting the dynamic burst-sparsity of the massive MIMO channel in the joint time-angular domain, a two-stage differential feedback scheme is proposed to compress and feed back the AoD vector, where the feedback overhead is adaptively adjusted according to the AoD changing rate. In addition, an adaptive-codebook-based feedback scheme is proposed for the angular-domain channel coefficients. Compared with the 5G New Radio (NR) Type-2 feedback scheme in 3GPP R15, the proposed scheme can significantly reduce the feedback overhead and achieve better block error rate (BLER) performance.

As the demand to alleviate the high cost and energy consumption problem in cellular communication systems grows, research on ways to compensate the hardware impairments for introducing low-cost devices into multi-input multi-output (MIMO) systems is becoming important. In particular, the nonlinearity of power amplifiers may severely degrade the performance of MIMO systems by distorting the precoded signals. In this work, we study the performance and precoding strategy of downlink multiuser MIMO systems with nonlinear power amplifiers. First, we analyze the signal-to-interference-plus-noise ratio (SINR) of nonlinear systems to establish the relationship between the transmit power and SINR. We find that the condition for maximizing the SINR depends on the average channel gain. Specifically, the maximum SINR is determined by the uncorrelated distortion when the average channel gain is large and by the distortion of precoding vector when the average channel gain is small. Motivated by the analysis, we propose an iterative algorithm, which alternately updates the precoding and power allocation by resolving the complicated coupling factors in the SINR expressions of nonlinear system. Furthermore, the proposed algorithm implicitly controls the total transmit power so that the desired signal, interference, and distortion are properly balanced. By the simulation results, we verify the analysis results and show that the proposed scheme outperforms the conventional schemes and achieves a near-optimal sum rate.

Non-orthogonal multiple access (NOMA) is an important technology for the forthcoming 5 G and beyond. However, its privacy often suffers from adversarial eavesdropping, especially for the users with higher transmit power. In this paper, we propose a jamming-aided secure transmission scheme for cooperative NOMA networks with a full-duplex (FD) relay. In this scheme, two pairs of users perform secure transmission with the help of a decode-forward (DF) relay, which forwards information and generates artificial jamming to counteract eavesdropping. The precoding vectors are designed to zero-force the artificial jamming at legal receivers. Then, the channel statistics are calculated, based on which the secrecy rates of legitimate signals are analyzed and the expressions of secrecy outage probability (SOP) are derived. Furthermore, three benchmark schemes are also presented with their performance analyzed to verify the advantage of the proposed scheme. Simulation results show the accuracy of our analysis, and demonstrate that the proposed scheme can effectively reduce the SOP and improve the effective secrecy throughput via artificial jamming and FD relaying.

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral and energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the access point (AP), subject to the users' individual quality of service (QoS) constraints. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term problem and a family of short-term problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. The proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes., 16 pages, 10 figures, accepted by IEEE Transactions on Wireless communications

Passively overheard massive multiple-input multiple-output (MIMO) settings are capable of suppressing eavesdroppers via narrow beamforming towards legitimate receivers. This implies that secrecy is obtained almost for free in these settings. This study shows that this is a valid property for a large class of low-complexity massive MIMO transmitters. The investigations consider two dominant approaches for complexity reduction, namely antenna selection and hybrid analog-digital precoding . It is shown that using either approach, the information leakage per achievable sum-rate vanishes as the number of transmit antennas grows large. The results demonstrate that, as the transmit array size grows large, the normalized information leakage obtained by antenna selection and hybrid analog-digital precoding converges to zero double-logarithmically and logarithmically , respectively. The analytical results are confirmed for various benchmark architectures via numerical simulations.

Employing multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has emerged as a powerful transceiver strategy for multi-antenna networks. In this paper, we design RS precoders for an overloaded multicarrier multigroup multicast downlink system, and analyse the error performance. RS splits each group message into degraded and designated parts. The degraded parts are combined and encoded into a degraded stream, while the designated parts are encoded in designated streams. All streams are precoded and superimposed in a non-orthogonal fashion before being transmitted over the same time-frequency resource. We first derive the optimized RS-based precoder, where the design philosophy is to achieve a fair user group rate for the considered scenario by solving a joint max-min fairness and sum subcarrier rate optimization problem. Comparing with other precoding schemes including the state-of-the-art multicast transmission scheme, we show that the RS precoder outperforms its counterparts in terms of the fairness rate, with Gaussian signalling, i.e., idealistic assumptions. Then we integrate the optimized RS precoder into a practical transceiver design for link-level simulations (LLS), with realistic assumptions such as finite alphabet inputs and finite code block length. The performance metric becomes the coded bit error rate (BER). In the system under study, low-density parity-check (LDPC) encoding is applied at the transmitter, and iterative soft-input soft-output detection and decoding are employed at the successive interference cancellation based receiver, which completes the LLS processing chain and helps to generate the coded error performance results which validate the effectiveness of the proposed RS precoding scheme compared with benchmark schemes, in terms of the error performance. More importantly, we unveil the corresponding relations between the achievable rate in the idealistic case and coded BER in the realistic case, e.g., with finite alphabet input, for the RS precoded multicarrier multigroup multicast scenario.

Hybrid precoding architecture, as a cost-effective approach for millimeter-wave (mmWave) communications, can achieve an excellent tradeoff between spectrum efficiency and hardware implementation complexity. However, the design of a robust hybrid precoding for improving the physical layer security (PLS), which is insensitive to the uncertainty of eavesdropper’s channel state information (CSI), has not been well studied. This article for the first time designs a probabilistically robust hybrid precoding scheme for securing broadcast communications in Internet of Things (IoT) with eavesdropper’s imperfect CSI. Specifically, considering the Gaussian CSI error model, we maximize the minimum secrecy rate of multiple IoT devices (IoDs) by jointly designing analog and digital precoders under the constraints in terms of secrecy outage probability and per IoD’s information rate. The optimization problem is challenging due to the coupling of the analog and digital precoders, and the secrecy outage constraint. To handle these challenges, we first employ a conservative probability inequality to transform the secrecy outage probability constraint into a deterministic one. Then, by employing the penalty dual decomposition (PDD) method, we develop a novel iterative algorithm to convert the resultant nonconvex problem into a sequence of convex problems, which can guarantee the convergence to its Karush–Kuhn–Tucker (KKT) solution. Simulation results show that the proposed algorithm can achieve significant secrecy performance gains compared with the benchmark algorithm.

The multiple input multiple output (MIMO) mechanism improves radio data rate by performing spatial multiplexing of multiple signals. Beamforming is a complementary mechanism to MIMO. Beamforming logically reduces the beam opening angle and limits the level of interference between cells. Elevation beamforming allows a beam to be directed in a specific way, for example, a beam pointing either up or down. The transmission modes implementing beamforming and the multi user‐MIMO mechanism require good correlation between the antennas. The MIMO mechanism and the beamforming mechanism are based on knowledge of the propagation channel. Symbols produced by modulation are distributed over spatial layers, and then precoded before being assigned to resource elements by antenna port. Only the orthogonal frequency division multiplexing signal allows the MIMO mechanism to operate up to four spatial layers or the beamforming mechanism by digital precoding.

In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, the conventional singular value decomposition (SVD) based precoders may destroy the smoothness of the effective channel, – i.e., the concatenation of the precoders and the channel – in the frequency domain, causing difficulties to the channel estimation. This letter proposes a phase-rotated SVD (PR-SVD) via exploiting the phase non-uniqueness of SVD to make the effective channel as smooth as possible in the frequency domain to facilitate the channel estimation. The simulation results verify that the proposed PR-SVD based precoding method can improve the channel estimation and consequently can significantly outperform the state-of-the-art method in bit error rate (BER).

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

This paper investigates the security problem of a multi-eavesdrop multiple-input-single-output (MISO) wiretap channel, where an $N$ -antenna transmitter communicates with a single-antenna legitimate user in the presence of multiple single-antenna smart eavesdroppers. To overcome the security risk of the traditional secure constructive interference-based (CI-based) scheme when facing the smart eavesdroppers, we propose a novel deception scheme (DS) via a random transmission strategy, where the eavesdroppers are expected to decode the deception symbols correctly but unable to distinguish the authenticity of the decoded symbol. Then, an efficient algorithm is proposed for the deception signal-to interference-plus-noise (SINR)-balancing problem when perfect channel state information (CSI) is assumed. Furthermore, we consider a practical scenario where only imperfect CSI is available, and explore two different methods for the deception optimization problem, i.e., convexification relaxation approach (CRA) and Lagrangian relaxation approach (LRA), respectively. For both CSI cases, a closed-form solution to the considered CI-based deception scheme is obtained. Simulation results validate the superiority of the proposed approach over traditional secure precoding schemes, and also demonstrate the significant computation efficiency improvements for the proposed algorithms.

The analytical lower bound of Spectral Efficiency (SE) of downlink transmission of the Massive Multiple Input Multiple Output (Ma-MIMO) system is analyzed. In this paper, we derive some novel and approximate mathematical expressions for the lower bound of the SE of a Ma-MIMO with linear precoding schemes, i.e., Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF). For simulation analysis of the SE, we consider three joint users and antenna scheduling algorithms, namely, the semi-orthogonal, random, and distance-based user scheduling algorithms, whereas the antennas are selected based on Maximum Signal to Noise Ratio (MSNR) with scheduled users. The channel between the user and the transmitter is assumed to have characteristics of Small Scale Fading (SSF) and Large Scale Fading (LSF) with the Rayleigh fading model. We investigate the effect of the variation of transmitting SNR, the number of base station antennas (M), and the radius (R) of the cell area on the SE. We simulate the downlink transmission of Ma-MIMO and compare the simulation and analytical results. It is observed that the trends of variation of both results are similar to the variation of identical factors, and the difference between the simulated and analytical lower bounds of the SE is approximately 1–1.5 bits. The analytical lower bound is smaller than the simulation result.

We consider secure transmission over multi-input multi-output multi-antenna eavesdropper (MIMOME) wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits the function approximation abilities of deep neural networks (DNNs) for solving the required power allocation problem. It is demonstrated that the DNN learns the optimal power allocation without any performance degradation compared to the conventional technique with a significant reduction in complexity.

Hybrid precoding is used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems instead of full digital precoding to reduce the cost and power consumption. Conventional hybrid precoding however requires complex channel estimation before beamforming. In this paper, we propose a low complexity low-cost channel estimation-free deep direct beamforming method composed of two basic steps-beam impulse and beamforming for time division duplex (TDD). This method is built upon randomized singular value decomposition (RSVD) and can directly complete the process of beamforming. Furthermore, a realistic dataset of mmWave channel-DeepMIMO is adopted to evaluate and determine the practicability of this method. The simulation results demonstrate that the proposed deep direct beamforming achieves high performance while its complexity and power consumption are very low. Compared with other methods without perfect channel status information (CSI), the proposed method costs less and requires lower computational resources while achieves higher spectral efficiency. Furthermore, the RSVD can be easily combined with conventional beamforming methods hence the proposed method is easily extendable to other applications.

In this letter, we study a multiuser multi-input single-output (MU-MISO) visible light communication (VLC) broadcast system under the peak-power constraint and dimming control. The space-time modem is designed to maximize the worst-case minimum Euclidean distance of received signals for all users. We prove that the optimal space-time constellation for our considered scenario can be realized by sending identical signals, which is optimal in terms of the SISO intensity channel with peak- and average-power constraints, across all transmitters. Simulation results indicate that our proposed scheme has better bit-error ratio (BER) performance compared with the precoding design and joint detection (PDJD) method and minimum mean-squared error (MMSE) precoder design.

This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS. A key observation is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem. In contrast to the traditional approach where the channel state information (CSI) is estimated and quantized at each user independently, this paper shows that a joint design of pilots and a new DNN architecture, which maps the received pilots directly into feedback bits at the user side then maps the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall performance. This paper further proposes robust design strategies with respect to channel parameters and also a generalizable DNN architecture for varying number of users and number of feedback bits. Numerical results show that the DNN-based approach with short pilot sequences and very limited feedback overhead can already approach the performance of conventional linear precoding schemes with full CSI., 15 pages, 10 figures, This is the final version to be published in IEEE Transactions on Wireless Communications, The source code for this paper are available at: https://github.com/foadsohrabi/DL-DSC-FDD-Massive-MIMO

In this article, we present high energy-efficiency (EE) operation schemes for downlink orthogonal frequency division multiplexing (OFDM)-based massive multiple-input multiple-output (MIMO) systems in industrial Internet of Things (IIoT) networks. We derive the exact closed-form expressions for some important downlink operation parameters, and based on the parameters, we propose the methods of systematic operation to satisfy the required EE metric with low latency. The operation parameters include the number of service antennas, downlink transmission power, and related coverage. To increase the EE of downlink OFDM signal transmission, it is also well known that peak-to-average power ratio (PAPR) reduction is particularly important, and we apply the clipping PAPR reduction technique for the purpose to increase the power amplifier efficiency, and thus we also determine the level of clipping to satisfy the EE requirement. We use two representative linear precoding schemes for massive MIMO, such as maximum ratio (MR) precoding and zero-forcing (ZF) precoding, and show that the amount of clipping distortion of both MR precoding and ZF precoding is proportional to the distortion from the corresponding IIoT device's signal and distortions from other IIoT devices’ signals. Simulation results are provided to validate the analysis and related schemes.

In this paper, we propose the three sub-optimal precoded single-mode joint generalized spatial modulation (SM-JGSM) constellation designs for high correlated indoor multi-input multi-output visible light communication (MIMO-VLC) channels. SM-JGSM constellation sets obtained using these computationally efficient iterative design algorithms offer different performance gains which depend on their maximum achievable minimum Euclidean distance (ED). Through the simulation results, we finally highlight the symbol error ratio (SER) performance improvement achieved by the proposed precoded SM-JGSM constellations over SM-JGSM constellations with no precoding for high correlated indoor MIMO-VLC channel. The results also provide the potential application of VLC in the areas of radio and satellite communication networks.

Summary Digital beamforming with an active array antenna has become, these past years, the holy grail for flexible satellite payloads. This architecture enables a power, frequency and beam steering...

Vector perturbation (VP) precoding is a promising technique to achieve near optimal capacity by exploring the channel state information (CSI) at the multiple-input multiple-output (MIMO) transmitter. To further enhance the bit error rate (BER) performance associated with forward error correction (FEC) coding, soft decision should be invoked in VP. However, the modulo operation in VP may influence the accuracy of soft decision. For alleviating this problem, the calculation of log-likelihood ratio (LLR) for VP is discussed and we further propose a sub-constellation shifted (SCS) scheme for VP precoding toward more accurate LLR. Simulation results demonstrate that the proposed scheme is capable of improving the BER performance of VP systems by accurate LLR calculation with moderate computational complexity.

In this paper, we investigate the physical layer security (PLS) for a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) visible light communication (VLC) system, whose information is intercepted by collusive eavesdroppers. For the case of known eavesdroppers’ locations, a precoding scheme is proposed to maximize the secrecy capacity of the system by suppressing the rate of collusive eavesdroppers. Then we extend the scheme to the case of unknown eavesdroppers’ locations. Specifically, the artificial noise is introduced in the precoding scheme to further suppress the average rate of eavesdroppers. To solve the non-convex optimization problem, we propose an iterative algorithm based on concave-convex procedure. Simulation results illustrate the outperformance of the proposed schemes in guaranteeing the PLS for the MISO NOMA VLC system under the interception of collusive eavesdroppers.

In this paper, the full-duplex (FD) relay transmission with simultaneous wireless information and power transfer in the presence of interference signals is investigated. A multiple-input multiple-output (MIMO) transmission in a small cell is considered, in which a half-duplex (HD) base station (BS) transmits data to the HD cell-edge user over an FD wireless powered relay. At the same time, BS transmits data to its near users. This concurrent data transmission to all nodes by BS causes signal interference at near users and relay. Relay harvests power from its received signals, including interference signals and the self-interference (SI) signal induced by the FD transmission, to forward the cell-edge user’s data. Power splitting (PS) and interference alignment (IA) techniques are used at the relay to jointly enable wireless energy transfer and interference management. The method introduced here is called the FD PS IA scheme. This method aims to find the PS ratio and beamforming matrices to maximize the cell-edge user and near users’ transmission rate. With this purpose, a closed-form expression for relay optimal PS ratio is derived. Furthermore, a method for choosing the proper precoding and decoding matrix at the relay and edge user, respectively, is proposed. Also, the IA matrices of BS, relay, and near users are optimized based on minimizing the mean square error iterative algorithm. Finally, numerical results demonstrate the rate gain from the combination of IA and PS at FD relay. Besides that, the performance improvement of information processing and energy harvesting by applying multiple antennas is presented. The significant benefits of harvesting the interference and SI signal power at the FD relay are exposed through analysis and simulations.

Wireless communication over Multiple Input and Multiple Output (MIMO) channel achieve increased transmission rate by dividing the input stream into a multitude of parallel data streams which are tr...

We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with single-antenna access points (APs) and single-antenna users. An iterative robust minimum mean-square error (RMMSE) precoder based on generalized loading is developed to mitigate interference in the presence of imperfect channel state information (CSI). An achievable rate analysis is carried out and optimal and uniform power allocation schemes are developed based on the signal-to-interference-plus-noise ratio. An analysis of the computational costs of the proposed RMMSE and existing schemes is also presented. Numerical results show the improvement provided by the proposed RMMSE precoder against linear minimum mean-square error, zero-forcing and conjugate beamforming precoders in the presence of imperfect CSI.

In this paper, we investigate the potential of a novel precoder for a massive multiple-input multiple-output (MIMO) system with multiple-antenna users under the influence of both Rayleigh and keyhole channel models. The proposed precoder is based on maximum-ratio processing and aims to enhance the channel hardening effect at the users. This is achieved through dividing each channel vector by its norm square. Closed-form expressions for the achievable spectral efficiency of the conventional and proposed precoders are derived. If modeled with Rayleigh fading, due to the physical hardening property of this channel, a moderate gain in performance is produced by the proposed precoding method, where the highest spectral efficiency gain occurs for a small number of users and user antennas. Under Rayleigh fading, the results also demonstrate that similar performance is returned by increasing the number of users or user antennas. By comparison, the proposed precoder is shown to produce significant performance gains in the keyhole channels under the condition that the system is limited to single-antenna users. Corresponding to this, the results showcase how the performance of massive MIMO under keyhole channels is heavily dependent on increasing the number of served users over user antennas.

Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtained. In order to achieve the aforementioned objectives, this paper presents a feasible DL training sequence design based on a partial CSI estimation approach for an FDD massive-MIMO system with a shorter coherence time. To this end, a threshold-based approach is proposed for a suitable DL pilot selection by exploring the statistical information of the channel covariance matrix. The mean square error of the proposed design is derived, and the achievable sum rate and bit-error-rate for maximum ratio transmitter and regularized zero forcing precoding is investigated over different BTS topologies with uniform linear array and uniform rectangular array. The results show that a feasible performance in the DL FDD massive-MIMO systems can be achieved even when a large number of antenna elements are deployed by the BTS and a shorter coherence time is considered.

A major source of difficulty when operating with large arrays at millimeter wave (mmWave) frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. One of them exploits the sparsity of the mmWave channel, while the other one leverages prior statistical information about the channel parameters. We also propose a hybrid precoder and combiner design method to increase the received signal-to-noise ratio (SNR) during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that our proposed channel tracking algorithms are able to achieve near-optimum data rates outperforming state-of-the-art methods.

For multi-user multiple-input-multiple-output (MU-MIMO) system, transmitter utilizes a pre-equalizer based precoding to cancel inter-stream interference for parallel transmission with the aid of accurate feedback of channel state information (CSI) from all receivers. The correct power scaling factor, which normalizes the symbols power of precoding to be a constant one, is required at each receiver. Due to CSI error and limitation of feed-forward link, the received inaccurate power scaling factor will shrink or expand the received constellation points which severely degrades the performance of MIMO system. In this paper, using nonlinear vector perturbation (VP) and linear zero-forcing (ZF) precoding, we analyze the impact of inaccurate power scaling factor on the performance of a MIMO precoding system. The analyzed results show that the mismatched modulo size from inaccurate power scaling factor severely degrades the performance of the VP system, especially for high order M-QAM modulation. In addition, the performance degradation is strongly related with the distribution of representation points for each M-QAM symbol. For linear ZF precoding, the performance loss from the shrinked or expanded constellation points is severe for system using high order M-QAM. In addition, to alleviate the performance degradation, we propose a VP system using partial perturbation points (PPP). By limiting the region of redundant points with vector perturbation for partial M-QAM symbols, the performance degradation due to the inaccurate power scaling factor can be alleviated.

In this paper, we investigate the problem of transceiver beamforming and power allocation in the downlink of a multiple input multiple output Cognitive radio system. In this system, a base station (BS) services multiple primary users (PU) and multiple secondary users (SU). In order to perform transceiver beamforming and power allocation, three new algorithms are proposed. The first one is an iterative single-objective constrained algorithm, in which the signal to interference plus noise (SINR) of SUs are improved at each iteration subject to constraints on quality of services of PUs (PUs SINR should be above a predefined threshold) and total transmit power of BS. The second and third algorithms, are based on a multi-objective constraint optimization problem, in which the goal is to improve both PUs and SUs SINR at each iteration subject to previous constraints of the first algorithm. The difference between the second and third algorithms is that in the joint maximization of the second algorithm, the cross-influence of each user on the others is ignored, but in the third algorithm, the cross effect of each users SINR on the others is considered. The novelty of these algorithms is the independency of the transmit precoding vectors from the receive beamforming vectors. Therefore, the feedback of receive beamforming to the BS is not needed. The performances of three proposed algorithms are evaluated in simulations vis bit error rate of PUs and SUs, SINR of PUs and SUs and the percent of BS power allocation to SUs.

In this work, we study the linear precoding and decoding design problem for a full-duplex (FD) multi-carrier (MC) decode and forward (DF) relaying system, under multiple sources of impairments. In particular, the impact of non-linear hardware distortions at the transmit and receiver chains, leading to residual self-interference (SI) and inter-carrier leakage (ICL), as well as the imperfect channel state information (CSI) are taken into account. In the first step, the known time-domain characterization of hardware impairments is transformed over a general orthonormal MC basis. As a result, the problem of linear precoding and decoding design is formulated to maximize the MC system sum-rate, however, leading to a non-convex mathematical structure. An alternating quadratic convex program (AQCP) is consequently proposed, with a monotonic improvement at each iteration, leading to a guaranteed convergence. The proposed AQCP framework is then extended, employing the Dinkelbach algorithm, in order to maximize the system energy efficiency in terms of bits-per-Joule. The proposed design strategies are considered both for per-subcarrier as well as the joint-subcarrier coding strategies, wherein the latter case the coding is performed jointly over all subcarriers. Numerical simulations show a significant gain in the performance of the proposed algorithms compared to the half-duplex (HD) counterparts or to the solutions where the impact of impairments are not considered, particularly when the hardware accuracy is not very high.

The capacity of a massive MIMO cellular network depends on user and antenna selection algorithms, and also on the acquisition of perfect Channel State Information (CSI). Low computational cost algorithms for user and antenna selection significantly may enhance the system capacity, as it would consume a smaller bandwidth out of the total bandwidth for downlink transmission. The objective of this paper is to maximize the system sum-rate capacity with efficient user and antenna selection algorithms and linear precoding. We consider in this paper, a slowly fading Rayleigh channel with perfect acquisition of CSI to explore the system sum-rate capacity of a massive MIMO network. For user selection, we apply three algorithms, namely Semi-orthogonal user selection (SUS), Descending Order of SNR-based User Scheduling (DOSUS), and Random User Selection (RUS) algorithm. In all the user selection algorithms, the selection of Base Station (BS) antenna is based on the maximum Signal-to-Noise Ratio (SNR) to the selected users. Hence users are characterized by having both Small Scale Fading (SSF) due to slowly fading Rayleigh channel and Large-Scale Fading (LSF) due to distances from the base station. Further, we use linear precoding techniques, such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Ratio Transmission (MRT) to reduce interferences, thereby improving average system sum-rate capacity. Results using SUS, DOSUS, and RUS user selection algorithms with ZF, MMSE, and MRT precoding techniques are compared. We also analyzed and compared the computational complexity of all the three user selection algorithms. The computational complexities of the three algorithms that we achieved in this paper are O(1) for RUS and DOSUS, and O(M2N) for SUS which are less than the other conventional user selection methods.

This work focuses on the promising Rate-Splitting Multiple Access (RSMA) and its beamforming design problem to achieve max-min fairness (MMF) among multiple co-channel multicast groups with imperfect channel state information at the transmitter (CSIT). Contrary to the conventional linear precoding (NoRS) that relies on fully treating any residual interference as noise, we consider a novel multigroup multicast beamforming strategy based on RSMA. RSMA relies on linearly precoded Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers, and has recently been shown to enable a flexible framework for non-orthogonal transmission and robust interference management in multi-antenna wireless networks. In this work, we characterize the MMF Degrees-of-Freedom (DoF) achieved by RS and NoRS in multigroup multicast with imperfect CSIT and demonstrate the benefits of RS strategies for both underloaded and overloaded scenarios. Motivated by the DoF analysis, we then formulate a generic transmit power constrained optimization problem to achieve MMF rate performance. The superiority of RS-based multigroup multicast beamforming compared with NoRS is demonstrated via simulations in both terrestrial and multibeam satellite systems. In particular, due to the characteristics and challenges of multibeam satellite communications, our proposed RS strategy is shown promising to manage its interbeam interference., Comment: Submitted for publication. arXiv admin note: text overlap with arXiv:2002.01731

In this paper, we investigate the downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA). We propose a novel user-assisted (UA) simultaneous diagonalization (SD) based MIMO-NOMA scheme that achieves SD of the MIMO channels of both users through a combination of precoder design and low-complexity self-interference cancellation at the users, thereby considerably lowering the overall decoding complexity compared to joint decoding. The achievable ergodic user rates of the proposed scheme are analyzed for Rayleigh fading channels based on a finite-size random matrix theory framework, which is further exploited to develop a statistical power allocation algorithm. Simulation and numerical results show that the proposed UA-SD MIMO-NOMA scheme significantly outperforms orthogonal multiple access and a benchmark precoder design performing SD via generalized singular value decomposition in terms of the achievable ergodic rate region for most user rates. The ergodic rate region is further enhanced by a hybrid scheme which performs time sharing between the proposed UA-SD MIMO-NOMA scheme and single-user MIMO., Comment: Accepted by the IEEE Transactions on Communications, see DOI. 32 pages, 8 figures, for associated code, see https://gitlab.com/aravindh.krishnamoorthy/mimo-noma

Targeting at a better design of the analogue network coding (ANC)-assisted two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) multi-relay communication systems, we bring in the nonlinear minimal mean-squared error (MMSE)-decision feedback equalization (DFE) receiving technique to jointly optimize the source precoding, relay amplifying, feed-forward and feedback matrices. Under the transmission power constraints at both source nodes and each relay node, the two-way sum mean-squared error (MSE) of the signal waveform estimation of all data streams is minimized. To solve the complicated nonconvex optimization problem with four groups of system parameters, this paper develops an iterative block coordinate descent (BCD) algorithm, which converges to at least a Nash point. On the basis of it, for mitigating the error propagation in MMSE-DFE receivers, a group of permutation matrix variables, determining the detection orders of all data streams, are further introduced in our system optimization. Moreover, in case there is no sufficiently precise channel state information (CSI), we also make an extension of the developed algorithms, yielding a robust design scheme, to handle the channel uncertainties. Numerical simulation results show that, compared with the existing linear MMSE receiving-based algorithm, our proposed nonlinear ones provide improved MSE and bit-error-rate (BER) performance as well as good robustness against the imperfect CSI, indicating a promising application prospect of this research.

The deployment of the Millimeter-Wave (mm-Wave) band in 5G and beyond wireless communications networks is one of the emerging fields owing to its potential of providing extensive bandwidth. Frequency Hopping (FH) has a high potential for use in wireless networks due to its key advantages of spreading the interference over wide bandwidth and of providing diversity gain in counteracting frequency-selective fading. Furthermore, Fast Frequency Hopping (FFH) intrinsically amalgamated with directional Beamforming (BF) may overcome the impairments because of the path-loss of mm-Wave communications. Thus, we propose FFH assisted base-band precoding aided BF for mitigating the mm-wave channel impairments imposed by both fading as well as path loss, whilst relying on a minimal range of radio frequency chains. The mathematical analysis and simulation results demonstrate that hybrid precoded FFH is indeed a promising high-capacity technique of attaining both time- and frequency-domain diversity gains for the mm-Wave communications.

In this paper, we consider a single-cell downlink multi-user multi-input multi-output (MU-MIMO) system in which multiple eavesdroppers coexist. The eavesdroppers overhear confidential information sent from a base station (BS). In particular, we assume a scenario of colluding eavesdroppers in which they cooperate with each other to decode the information. We develop a secure linear precoding method that maximizes the sum secrecy spectral efficiency. To this end, we formulate an optimization problem and propose a computationally-efficient algorithm that achieves a sub-optimal stationary point of the problem. Simulation results validate the proposed algorithm. The proposed method possesses several benefits: $i)$ it is applicable in general systems, for example, an arbitrary number of eavesdroppers and confidential information set, $ii)$ the associated complexity is relatively low, and $iii)$ it is also applicable when only channel covariance of the eavesdroppers is available.

In satellite communication downlinks, physical layer security is challenging to achieve due to their broadcasting nature and Line-of-Sight channel characteristics. This paper provides a precoding algorithm to secure the downlinks of multiple users against multiple eavesdroppers with optimization of the minimum secrecy capacity. By the use of artificial noise, a positive secrecy capacity is achievable even if the number of eavesdroppers is higher than the number of beams. We demonstrate that a multiple-reflector antenna design provides a significantly higher secrecy and throughput performance when compared to a single-reflector design due to additional degrees of freedom, exhibited by the signal phases. The total vulnerability region is introduced as a new figure of merit with respect to unidentified eavesdroppers.

Resource allocation strategies for simultaneous wireless information and power transfer (SWIPT) employing orthogonal frequency division multiplexing (OFDM) typically rely on subcarrier assignment for multiple access. For information transfer, however, subcarrier assignment is only sum-rate optimal when the network is interference limited; otherwise, it is optimal to reuse subcarriers. Beamforming-based space-division multiple access (SDMA) is a multiple access technique that enables subcarrier reuse by managing interference. Although beamforming is considered a key SWIPT component, OFDM-based SWIPT has yet to utilize beamforming for SDMA. As such, this paper seeks to develop a low-complexity resource allocation strategy to jointly allocate power and design beamforming vectors for an OFDM-based multiuser (MU) multiple-input single-output (MISO) broadcast channel (BC) SWIPT system capable of utilizing SDMA. To this end, a non-convex optimization problem is formulated in which a weighted sum of user data rates is maximized subject to meeting receive and transmit power constraints. The time-sharing property of multicarrier systems is observed to justify utilizing Lagrangian decomposition despite the non-convexity. Whereas previous SWIPT research used Lagrangian decomposition to simplify subcarrier assignment, this paper instead uses it to enable SDMA through beamforming. Beamforming is achieved via a medium access channel (MAC)-BC duality, which enables a closed-form solution for the beamforming vectors given the dual MAC power allocation. The dual MAC power allocation is approximated using a successive convex approximation (SCA) technique. The result is a resource allocation algorithm that provides close-to-optimal performance with low computational complexity.