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A variable-phase-shifter (VPS) architecture with hybrid precoding for mixture use of phase shifters and switches, is proposed for millimeter wave massive multiple-input multiple-output communications. For the VPS architecture, a hybrid precoding design (HPD) scheme, called VPS-HPD, is proposed to optimize the phases according to the channel state information by alternately optimizing the analog precoder and digital precoder. To reduce the computational complexity of the VPS-HPD scheme, a low-complexity HPD scheme for the VPS architecture (VPS-LC-HPD) including alternating optimization in three stages is then proposed, where each stage has a closed-form solution and can be efficiently implemented. To reduce the hardware complexity introduced by the large number of switches, we consider a group-connected VPS architecture and propose a HPD scheme, where the HPD problem is divided into multiple independent subproblems with each subproblem flexibly solved by the VPS-HPD or VPS-LC-HPD scheme. Simulation results verify the effectiveness of the propose schemes and show that the proposed schemes can achieve satisfactory spectral efficiency performance with reduced computational complexity or hardware complexity. [ABSTRACT FROM AUTHOR]

Inter-cell interference (ICI) and inter-user interference (IUI) constitute a major issue towards achieving the optimum spectral efficiency (SE) and energy efficiency (EE) performance in multi-cell visible light communication (VLC) networks. Hence, advanced multiple access techniques need to be leveraged in order to improve the provided service to the users of such interfering networks. To this end, the present contribution proposes the integration of coordinated beamforming (CB) with rate-splitting multiple access (RSMA) in multi-cell VLC systems. Specifically, we consider the design of beamformers for the common and private streams in a coordinated manner between different attocells, which is shown to provide efficient mitigation of the incurred interference. Additionally, the formulated optimization problem aims to minimize the sum of the mean squared error across all attocells in order to jointly determine the optimum receive filters and coordinated transmit beamformers for RSMA streams. In this context, we illustrate through extensive computer simulations, which are carried out in a realistic setup that assumes noisy channel state information acquisition, the distinct flexibility and robustness of CB-based RSMA in mitigating the incurred interference. Finally, the offered results demonstrate the superiority of CB-based RSMA in terms of achievable SE and EE performance in multi-cell VLC networks compared to the conventional CB-based space division multiple access counterpart. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) building on the existing illumination infrastructure utilizes light emitting diode (LED) luminaries as wireless transmitters. To achieve data rates on the order of gigabits per second in VLC systems, multiple-input multiple-output (MIMO) transmission has been identified as a key technology. In this paper, we consider a centralized network architecture where the ceiling LED luminaries act as transmitter elements of a distributed massive MIMO VLC system. To obtain the full benefits of massive MIMO, channel state information must be available at the transmitter side. Downlink (DL) channel estimation in a massive MIMO system requires huge pilot overhead in DL as well as feedback overhead in uplink. This makes the implementation of massive MIMO system infeasible if not properly designed. In our study, we address the design of massive MIMO VLC systems with a limited number of DL pilot signals. We explore different DL pilot patterns (PPs) in frequency, time, and spatial domains where interpolation is performed to obtain the global channel matrix taking advantage of the indoor environment geometry and layout of luminaries. We then present data rate results for different DL PPs under the consideration of varying number of active user equipments through Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment. [ABSTRACT FROM AUTHOR]

In this paper, we consider an intelligent reflecting surface (IRS)-assisted downlink cognitive radio (CR) system, in which a secondary access point (SAP) communicates with multiple secondary users (SUs) without affecting multiple primary users (PUs) in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only a single PU and multiple SUs is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the precoder design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system with one-bit digital-to-analog converters (DACs). In order to form the dual-functional beam-pattern, we formulate the precoding problem as a weighted optimization problem with the constant modulus constraint, which aims at minimizing the average error power and guaranteeing radar waveform similarity. The problem is divided into three sub-problems corresponding to the multiple variables, i.e., the precoding factor, transmit signal matrix, and radar waveform matrix. Due to the discrete and non-convex properties of the optimization problem, we propose a multi-variable alternating minimization (MVAM) framework to achieve the near-optimal solutions. The precoding factor and radar waveform can be solved in closed-forms. For the transmit signal matrix, we devise a binary particle swarm optimization-simulated annealing (BPSO-SA) algorithm to obtain it under the MVAM framework. Extensive simulations validate the effectiveness of the proposed approach under various scenarios, including the case without perfect channel state information. The simulation results show that, compared with existing non-linear precoders, the proposed approach achieves 7dB SNR gain at the bit error rate of 10−4 in the 8-antenna system, and the gain of SNR is 0.2dB in the massive MIMO system with 128 antennas. [ABSTRACT FROM AUTHOR]

In this article, a generalized space–time line code (GSTLC) is proposed. The proposed GSTLC concurrently transmits multiple data streams obtained by space–time line code encoding and precoding to provide spatial multiplexing and diversity. When full channel state information (CSI) is available to the transmitter and limited CSI is available to the receiver, an optimal precoding matrix is derived for GSTLC with linear combining that is based on the minimum mean square error (MMSE) criterion. Moreover, the detection signal-to-interference-plus-noise ratio (SINR) is derived corresponding to the optimal MMSE precoder. Using the SINR, we propose a receiver combining scheme that selects the best combining matrix, thereby minimizing the bit error rate (BER) from a codebook that includes candidate combining matrices. Through numerical simulations, we demonstrate that the proposed GSTLC with the optimal combiner achieves the theoretical lower bound of BER. In addition, we show that the GSTLC with combiner selection performs better than conventional precoded spatial multiplexing methods in terms of the BER. [ABSTRACT FROM AUTHOR]

Opportunistic beamforming (OBF) is a promising technique in multiuser multiple-input-multiple-output (MIMO) communications, which attracts many attentions. Because of opportunistic precoding and multiuser selection, OBF can achieve high spectrum efficiency under the limited channel status information (CSI) condition with low complexity. Therefore, in this article, we introduce the state-of-the-art researches of multiuser MIMO OBF, including fundamental architecture, physical layer techniques and multiple access schemes. Due to the complex user scheduling strategy, artificial intelligence OBF systems are proposed, which can simply and adaptively modulate the user scheduling strategy. Moreover, in order to provide the communication and Computing services with low latency and high reliability for users, OBF-based computing systems are presented. Then, some open issues are given for the future researches. Finally, we conclude this article. [ABSTRACT FROM AUTHOR]

In this letter, we consider the covert communications under precoded faster-than-Nyquist (FTN) signaling. We first propose a general framework to analyze the detection performance of an illegal adversary for the precoded FTN signaling, and then derive a general principle for the optimal precoding scheme maximizing the covert throughput under the Kullback-Leibler (KL) divergence-based constraint. The covert throughput of the optimal precoding scheme is obtained based on the proposed framework, and it is theoretically proved to be higher than that of the non-precoded FTN as well as Nyquist ones. Numerical results also verify the superiority of the optimal precoded FTN signaling in terms of the covert throughput. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the performance of various three-dimensional (3D) array structures in the design of hybrid precoder (HP) for millimeter wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems. The proposed HP scheme involves two stages: (i) RF beamforming based on singular value decomposition (SVD) of channel matrix, and baseband MU precoding based on the instantaneous effective baseband channel to mitigate MU-interference by a regularized zero-forcing (RZF) technique. Illustrative results show that, in comparison to two-dimensional (2D) arrays, 3D array structures can provide high spectral and energy efficiencies as well as non-varying achievable rate independent from the user angular location. [ABSTRACT FROM AUTHOR]

To reduce the computational complexity and improve the achievable rate of hybrid precoding, we propose a deep constrained and synchronous training framework, which enables effective learning from the fully digital precoders and approaches the upper bound performance. The key innovation focuses on integrating a constrained precoding network with synchronous loss to predict hybrid precoders that approximate the corresponding fully digital precoders, where the constrained neural network guarantees the unit module and total transmission power while the synchronous loss maximizes the achievable rate. Experimental results show that the hybrid precoder generated from the proposed framework outperforms the benchmark hybrid precoders and could save more than half the inference time. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the blindness compensation for millimeter wave (mmWave) wireless networks by appropriately deploying the reconfigurable intelligent surface (RIS). Specifically, to maximize the average achievable rate, the precoder of base station, RISs’ coefficients and RISs’ deployment are jointly optimized, while taking into account the users’ stochastic geographical distribution. By leveraging stochastic gradient descent (SGD)-based algorithm, we investigate the optimal deployment issue for the single- and multi-RIS cases, respectively, in which the straightforward solutions for joint precoding and RIS phase shifts are established, demonstrating a beneficial performance while maintaining a favorable computational complexity. Finally, simulation results are provided to validate our proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, a new receive antenna subset (RAS) selection scheme is proposed for precoding-aided spatial modulation (PSM). First, a lattice reduction (LR)-based precoder is employed instead of a conventional zero-forcing (ZF) precoder. It is analytically shown that a full diversity gain can be achieved by the LR-based ZF precoder without RAS selection. Then, an optimal LR-based RAS selection criterion is derived for the over-determined LR-based PSM systems, and a suboptimal selection algorithm is additionally presented. It is also shown that optimal and suboptimal RAS selection algorithms based on LR improve the BER performance of the LR-based PSM system. Further, the overall diversity order of the over-determined LR-based PSM systems with optimal LR-based RAS selection is analyzed. Finally, diversity analysis and simulation results show that the LR-ZF-based PSM system with optimal LR-based RAS selection outperforms the conventional ZF-based PSM system with conventional optimal RAS selection. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRSs) can enhance the system performance of millimeter wave (mmWave) networks. In practice, phase shifts at the base station and the IRSs are digitally controlled and have discrete values. The channel state information (CSI) feedback also has a limited rate. This work analyzes the spectral efficiency performance of an IRS based mmWave network in which the beamsteering codebooks are used for the passive precoding and analog precoding, and the zero-forcing precoder is used for digital precoding. To capture the features of mmWave channels and reflect the impact of passive and analog precoding, a channel correlation based codebook is employed to quantize the effective channels. Upper-bounds are derived for the spectral efficiency losses due to finite-resolution beamsteering codebooks and limited CSI feedback. It is found that the spectral efficiency loss due to finite-resolution beamsteering codebooks does not depend on the transmit power in large signal-to-noise ratio regimes, while the spectral efficiency loss due to limited CSI feedback increases with the transmit power. Then, the asymptotic performance is analyzed when the numbers of transmit antenna elements and reflecting elements go to infinity. Simulations verify the derivations and findings. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We present a general framework in which the RIS, referred to as modulating intelligent surface (MIS) in this paper, is used to: $i$) beamform the signals for a set of users whose data modulation is already performed by the base station (BS), and at the same time, $ii$) embed the data of a different set of users by passively modulating the deliberately sent carrier signals from the BS to the RIS. To maximize each user’s spectral efficiency, a joint non-convex optimization problem is formulated under the sum minimum mean-square error (MMSE) criterion. Alternating optimization is used to divide the original joint problem into two tasks of: $i$) separately optimizing the MIS phase-shifts for passive beamforming along with data embedding for the BS- and MIS-served users, respectively, and $ii$) jointly optimizing the active precoder and the receive scaling factor for the BS- and MIS-served users, respectively. While the solution to the latter joint problem is found in closed-form using traditional optimization techniques, the optimal phase-shifts at the MIS are obtained by deriving the appropriate optimization-oriented vector approximate message passing (OOVAMP) algorithm. Moreover, the original joint problem is solved under both ideal and practical constraints on the MIS phase shifts, namely, the unimodular constraint and assuming each MIS element to be terminated by a variable reactive load. The proposed MIS-assisted scheme is compared against state-of-the-art RIS-assisted wireless communication schemes and simulation results reveal that it brings substantial improvements in terms of system throughput while supporting a much higher number of users. [ABSTRACT FROM AUTHOR]

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting with a posteriori channel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we design an iterative algorithm and present its convergence analysis. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm. [ABSTRACT FROM AUTHOR]

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling overheads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Large-scale antenna array techniques are key enablers for modern wireless communication systems. Channel state information (CSI) is indispensable for large-scale multi-antenna systems, but is challenging to obtain. To tackle this issue, in this paper we propose a unified precoding and pilot design framework, that allows minimal and precoding-sensitive modified CSI (mCSI) to be collected. This results in a significant reduction in the CSI overheads and complexity compared to classical physical CSI (pCSI) estimation. Based on this unified framework, we further propose an intelligent pilot (IP) approach that senses and selects the mCSI to be collected. The IP approach utilizes a compressive sensing formulation to attach sensing and selection of significant mCSI to precoding optimization. We apply the above techniques to the multi-user frequency division duplexing (FDD) downlink as an example. Our study shows that the advantages of the IP approach are three-fold. First, in contrast to the pCSI, precoding-sensitive information is only captured, which reduces the training and feedback overheads. Second, the precoders are optimized directly based on the mCSI, which avoids recovering the pCSI of high-dimension. Third, since the mCSI of reduced dimension is utilized, the scale of the problem to optimize the precoder is also reduced and thus it is much easier to solve. [ABSTRACT FROM AUTHOR]

With the explosive proliferation of connected devices and mobile users in the Internet-of-things, multiple access techniques are urged to be developed for the next generation wireless communications. Recently, rate-splitting multiple access (RSMA) has been a promising communication technology that holds advantages of strong robustness, low complexity, and high spectral efficiency, which can be integrated with the indoor visible light communication (VLC) broadcast system to compensate for the shortcomings of limited modulation bandwidth of LEDs. However, the research on the RSMA-based VLC systems is still in its infancy and there exist various problems to be explored. To benefit from the RSMA technique, this paper investigates the energy efficiency optimizations for both single-cell and multi-cell RSMA-based VLC broadcast systems. Specifically, these two systems are modeled, where the VLC broadcast channel follows Lambertian radiation model, and the splitting design and successive interference cancellation of RSMA are employed to mitigate the multi-user interference. Especially for multi-cell networks, the zero-forcing approach is adopted to eliminate the inter-cell interference. To maximize the energy efficiency, the precoding and power allocation problems are formulated for single-cell and multi-cell networks while accommodating multiple constraints including dynamic operation ranges of LEDs, QoS requirements, and interference elimination. For solving these non-convex fractional problems, two pieces of successive convex approximation (SCA)-based algorithms are proposed, in which the variable transformation and linear approximation are adopted. Simulation results indicate that the proposed schemes can achieve superior energy efficiency with fast convergence for various network loads and user deployments. [ABSTRACT FROM AUTHOR]

Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this letter, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches. [ABSTRACT FROM AUTHOR]

Lens antenna arrays based beamspace millimeter-wave multi-user massive MIMO (B-MIMO) can significantly improve energy efficiency without significant loss of spectral efficiency (SE), which is considered as a highly expected technology for future wireless communication networks. Motivated by the requirement of reducing SE loss by efficient user grouping (UG) and beam selection (BS), a joint user grouping and beam selection algorithm is studied. Specifically, a local search based BS algorithm is proposed by proving the convexity of the BS problem after relaxing the constraints. Moreover, a deep learning based UG algorithm is proposed by equivalently transforming the UG problem into a classification problem. The results of theoretical analysis and numerical simulation show that the proposed algorithms can be more closely with the performance of the exhaustive search algorithm and the complexity is lower than that of the traditional algorithms. [ABSTRACT FROM AUTHOR]

Symbol-level precoding (SLP) has recently emerged as a new paradigm for physical-layer transmit precoding in multiuser multi-input-multi-output (MIMO) channels. It exploits the underlying symbol constellation structure, which the conventional paradigm of linear precoding does not, to enhance symbol-level performance such as symbol error probability (SEP). This paper aims to better understand the relationships between SLP and linear precoding, subsequent design implications, and further connections beyond the existing SLP scope. Focused on the quadrature amplitude modulation (QAM) constellations, our study is built on a basic signal observation, namely, that SLP can be equivalently represented by a zero-forcing (ZF) linear precoding scheme augmented with some appropriately chosen symbol-dependent perturbation terms, and that some extended form of SLP is equivalent to a vector perturbation (VP) nonlinear precoding scheme augmented with the above-noted perturbation terms. We examine how insights arising from this perturbed ZF and VP interpretations can be leveraged to i) substantially simplify the optimization of certain SLP design criteria, namely, total or peak power minimization subject to SEP quality guarantees; and ii) draw connections with some existing SLP designs. We also touch on the analysis side by showing that, under total power minimization, the basic ZF scheme is a near-optimal SLP scheme when the QAM order is very high—which gives a vital implication that SLP is more useful for lower-order QAM cases. Numerical results further indicate the merits and limitations of the different SLP designs derived from the perturbed ZF and VP interpretations. [ABSTRACT FROM AUTHOR]

Underwater visible light communication (UVLC) based on light-emitting diodes (LEDs) is considered a potential candidate for underwater wireless data transmission. However, the implementation of high-speed UVLC remains a challenge due to bandwidth limitation and nonlinear effects. In this paper, we investigate the performance of Tomlinson-Harashima precoding (THP) in the bandwidth-limited UVLC system for the first time. Apart from research on linear and Volterra series-based nonlinear THP, neural network (NN)-based THP is first proposed and conducted. At the transmitter, the intersymbol interference (ISI) and nonlinearity can be partially mitigated through a feedback neural network (FBN) without error propagation. A modulo operator is employed to eliminate instability. In addition, for the precoded signals, the power spectral density (PSD) is near-white. Therefore, it is possible to avoid the spectrum-shaping-induced signal-to-noise ratio (SNR) loss problem that is common in pre-emphasis methods. At the receiver, another modulo operator is used for decoding. An adaptive feedforward neural network (FFN) is applied to compensate for the channel state information (CSI) mismatch of the FBN and mitigate the remaining ISI and nonlinear impairments. In the demonstrated UVLC system, the experimental results prove the feasibility of the proposed method. The Q factor is increased by 3.39 dB at a data rate of 2.2 Gbps. 630 MBd CAP-16 transmission under a 7% HD-FEC threshold is realized, which is 90 MBd higher than that when employing linear postequalization only. [ABSTRACT FROM AUTHOR]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist signaling (FTNS) has emerged as a promising technique to increase communication capacity in bandwidth-limited channels. However, the presence of FTN-induced inter-symbol interference (FTN-ISI) in the received observations, is detrimental to channel estimation (CE) and data detection in terms of computational complexity and performance. This paper copes with these problems by incorporating linear pre-equalization (LPE) and composite precoding formed by linear spectral precoding and Tomlinson-Harashima precoding (THP), into the FTNS. Specifically, LPE completely pre-equalizes the FTN-ISI, while spectral precoding resolves the LPE-caused signal spectral broadening by introducing proper artificial ISI, which is pre-equalized by THP. Channel-induced ISI, as the only ISI component in the observations, is estimated and equalized using the classical frequency-domain low-complexity schemes. We show that there are four advantages of the LPE-aided CE over the CE designed for the FTN transmissions without FTN-ISI pre-equalization, namely lower pilot overhead, simpler yet optimal pilot sequence design, lower mean-squared error of CE, and more robust against the FTN-ISI. Simulation results show that our scheme improves the performance of CE and detection, compared to existing FTN frequency-domain CE and equalization schemes. [ABSTRACT FROM AUTHOR]

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR. [ABSTRACT FROM AUTHOR]

We propose a Rate Splitting (RS) transmit scheme for Device-to-Device (D2D) underlaid Cache-enabled Cloud Radio Access Networks (C $^2$ -RANs). To this end, we jointly design Cellular User (CU) grouping, dynamic Remote Radio Head (RRH) clustering, beamforming, RS ratio, and subcarrier allocation to maximize the sum-rate and ensure transmit power and fronthaul cost constraints. However, the formulated problem is discrete, non-smooth, and non-convex. We thus decouple it into three subproblems, namely - (1) CU grouping, (2) D2D subcarrier allocation, and (3) joint sparse beamforming, RS ratio, and power control. We then develop a low-complexity greedy searching algorithm and a fairness-ensuring accelerated bisection searching algorithm via graph theory for the first subproblem. For the second subproblem, we use a many-to-one matching game with peer effect. We adopt the Gale-Sharply algorithm and swap operations to reach a stable matching state and propose a Two-sided Stable Subcarrier Allocation ($\text{TS}^2$ A) algorithm. For the third subproblem, we develop a Quadratic Transform-based Two-Tier Alternating ($\text{QT}^3$ A) algorithm. It constructs a series of accurate surrogate functions for the objective function and constraints via a Quadratic Transform (QT) technique. We then recast it as a two-tier alternating problem. We tackle the outer and inner tiers with closed-form expressions and the convex framework, respectively. By integrating these algorithms, the overall algorithm can be proved to converge to a stationary point. Simulation results show that it achieves considerable performance gains over several benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the energy-efficient power allocation strategy for multi-user mmWave non-orthogonal multiple access (NOMA) system with different hybrid precoding (HP) structures. Particularly, we consider the fully-access HP structure and the subarray HP structure, respectively. For these two structures, the finite resolution analog precoder is realized through a low-power switches and inverters (SI) network. In the NOMA system, multiple users are first paired according to their channel correlation and gain difference. Following this, we propose hybrid precoding schemes for the low-power SI-based HP structures. Then, a power allocation (PA) problem is formulated to maximize the energy efficiency of the system, subject to per-cluster’ power constraints and per-user’ quality of service constraints. To solve the non-convex PA optimization problem, we devise a suboptimal solution with two-stage PA strategies. Furthermore, the PA problem aiming at conditionally maximizing the spectrum efficiency is also analyzed. Simulation results show that the proposed mmWave-NOMA can achieve higher energy efficiency than the existing mmWave-NOMA scheme and the conventional mmWave orthogonal multiple access scheme. [ABSTRACT FROM AUTHOR]

The existing literature on cellular multi-user mmWave communication focus on joint baseband and RF precoding designs to enable spatial multiple access with minimum interference. These studies either assume the number of users $M$ is less than the number of RF units $N_{RF}$ or schedule the users in time domain by dedicating one RF unit to each user if $M >N_{RF}$. It is expected that serving multiple users over OFDMA in each analog beam will offer better utilization of the wideband channel. To this end, for the scenario with $M \gg N_{RF}$ we propose a sectored-cell model that is supported by multi-RF chains over the wideband mmWave channel, with each beam serving multiple users within a sector and the sectors being scheduled in round-robin fashion. We also propose a variable time frame structure that conducts sector-wise initial access, with simultaneous access to all users within a sector. It provides improved spectrum efficiency and decreased initial access delay as compared to the initial access using exhaustive beam search method. We then jointly estimate the optimum beamwidth and optimum $N_{RF}$ that offer maximum average long-run user rate. Further, we introduce a reduced-complexity sector sojourn time optimization for non-homogeneously distributed users, that improves fairness of long-run user rates leveraging the variable time frame structure. The numerical results show that, while a high value of $N_{RF}$ causes more interference to peak data rate, the average long-run user rate improves. Additionally, using a very narrow beam is also not optimal for providing maximum rate support. The proposed beamforming method offers a higher average long-run user rate over the competitive beamforming schemes while the complexity of user scheduling is independent of $M$. [ABSTRACT FROM AUTHOR]

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is promising to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel state information is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in the angular domain, which is achieved by the classical far-field planar-wavefront assumption. However, due to the non-negligible near-field spherical-wavefront property in XL-MIMO, this channel sparsity in the angular domain is not achievable. Therefore, existing far-field channel estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angular-domain representation that only considers the angular information, we propose a polar-domain representation, which simultaneously accounts for both the angular and distance information. In this way, the near-field channel also exhibits sparsity in the polar domain, based on which, we propose on-grid and off-grid near-field XL-MIMO channel estimation schemes. Firstly, an on-grid polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm is proposed to efficiently estimate the near-field channel. Furthermore, an off-grid polar-domain simultaneous iterative gridless weighted (P-SIGW) algorithm is proposed to improve the estimation accuracy. Finally, simulations are provided to verify the effectiveness of our schemes. [ABSTRACT FROM AUTHOR]

Downlink precoding is considered for multi-path multi-input single-output channels where the base station uses orthogonal frequency-division multiplexing and low-resolution signaling. A quantized coordinate minimization (QCM) algorithm is proposed and its performance is compared to other precoding algorithms including squared infinity-norm relaxation (SQUID), multi-antenna greedy iterative quantization (MAGIQ), and maximum safety margin precoding. MAGIQ and QCM achieve the highest information rates and QCM has the lowest complexity measured in the number of multiplications. The information rates are computed for pilot-aided channel estimation and a blind detector that performs joint data and channel estimation. Bit error rates for a 5G low-density parity-check code confirm the information-theoretic calculations. Simulations with imperfect channel knowledge at the transmitter show that the performance of QCM and SQUID degrades in a similar fashion as zero-forcing precoding with high resolution quantizers. [ABSTRACT FROM AUTHOR]

The downlink multi-user precoding of the multiple-input multiple-output (MIMO) method includes optimal channel state information at the base station and a variety of linear precoding (LP) schemes. Maximum ratio transmission (MRT) is among the common precoding schemes but does not provide good performance with massive MIMO, such as high bit error rate (BER) and low throughput. The orthogonal frequency division multiplexing (OFDM) and precoding schemes used in 5G have a flaw in high-speed environments. Given that the Doppler effect induces frequency changes, orthogonality between OFDM subcarriers is disrupted and their throughput output is decreased and BER is decreased. This study focuses on solving this problem by improving the performance of a 5G system with MRT, specifically by using a new design that includes weighted overlap and add (WOLA) with MRT. The current research also compares the standard system MRT with OFDM with the proposed design (WOLA-MRT) to find the best performance on throughput and BER. Improved system results show outstanding performance enhancement over a standard system, and numerous improvements with massive MIMO, such as best BER and throughput. Its approximately 60% more throughput than the traditional systems. Lastly, the proposed system improves BER by approximately 2% compared with the traditional system. [ABSTRACT FROM AUTHOR]

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices. [ABSTRACT FROM AUTHOR]

This work studies the application of nonorthogonal transmission in beamforming (BF) based forward links for next-generation satellite communication (SATCOM) with multiple gateways. With the aim of enhancing the throughput of BF SATCOM systems, the state-of-the-art nonorthogonal multiple access (NOMA) technique is exploited by serving multiple users per beam in the same time slot. In this regard, the feeder link limitations and multibeam satellite payload constraints must be considered for BF design and power allocation (PA) optimization in nonorthogonal SATCOM. To address these challenges, distributed resource optimization strategies are investigated for BF and flexible payload power resource allocation in multigateway (multi-GW) nonorthogonal SATCOM systems. Specifically, a per-feed available power-constrained BF strategy via maximization of the worst-user signal-to-leakage-and-noise ratio (SLNR) is explored with local channel state information (CSI) for a distributed operation of GWs. As an upper-bound performance limit, a centralized multilayer BF strategy is processed in a central unit with full global CSI and data sharing. After the BF direction optimization, a weighted sum-rate maximization-based (WSRM-based) power resource optimization strategy is locally applied at each GW to efficiently use the power resources for higher performance increment. The nonconvex WSRM problem, under the constraints of the practical satellite payload power budget, successful successive interference cancellation (SIC) decoding, and minimum data rate, is recast into an equivalent weighted sum-MSE minimization (WMMSE) counterpart for a tractable solution. Finally, an efficient user scheduling is designed to enable the operator to capture a substantial system-throughput gain. Accurate simulations are conducted with the near-to-real coverage area (footprints), the random distributions of users, and interference, relying on geographical locations of users. The results over a realistic simulation environment show the efficiency of our strategies. [ABSTRACT FROM AUTHOR]

Next-generation systems aim to increase both the speed and responsiveness of wireless communications while supporting compelling applications such as edge-cloud computing, remote health, vehicle-to-infrastructure communications, etc. As these applications are expected to carry confidential personal data, ensuring user privacy becomes a critical issue. In contrast to traditional security and privacy designs that aim to prevent confidential information from being eavesdropped upon by adversaries, or learned by unauthorized parties, in this article we consider designs that mask the user's identity during communication, resulting in anonymous communications. In particular, we examine the recent interest in physical layer (PHY) anonymous solutions. This line of research departs from conventional higher layer anonymous authentication/encryption and routing protocols, and judiciously manipulates the signaling pattern of transmitted signals in order to mask the sender's PHY characteristics. We first discuss the concept of anonymity at the PHY, then illustrate a strategy that is able to unmask the sender's identity by analyzing his/her PHY information only – that is, signalling patterns and the inherent fading characteristics. Subsequently, we review the emerging area of anonymous precoding to preserve the sender's anonymity while ensuring high receiver-side signal-to-interference-plus-noise ratio for communication. This family of anonymous precoding designs represents a new approach to providing anonymity at the PHY, introducing a new dimension for privacy-preserving techniques. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is an important aspect of satellite communications. Different with the existing algorithms that typically use the first-order Taylor lower bound approximation to convert non-convex EE maximization (EEM) problems into convex ones, in this letter a two-step quadratic transformation method is presented. In the first step, the fractional form of the achievable rate over the total power consumption is converted into a non-fractional form based on quadratic transformation. In the second step, the fractional form of the signal power over the interference-and-noise power is further converted into a non-fractional form, still based on quadratic transformation. After the two-step quadratic transformation, the original EEM problem is converted into an equivalent convex one. Then an alternating optimization algorithm is presented to solve it by iteratively performing two stages until a stop condition is satisfied. Simulation results show that the presented algorithm can fast converge and its performance is better than that of the sequential convex approximation algorithm and the multibeam interference mitigation algorithm. [ABSTRACT FROM AUTHOR]

The promising solution for short-reach optical communication, discrete multitone (DMT) system suffer from high peak-to-average power ratio (PAPR). It is easy for the DMT signal to enter the nonlinear region of the components in the systems, which distorts the signal and limits the modulation efficiency. It is well-known that the precoding technique can suppress the PAPR of DMT without loss of spectral efficiency. We found that, in the I/Q plane, the nonlinear distortion clusters in the constellation points with higher power due to the joint precoding and inverse fast Fourier transform. The principle of this phenomenon has been studied. To cope with the specially distributed nonlinear distortions induced by precoding, we adopted the probabilistic constellation shaping (PCS) technique to reduce the probability of constellation points with high power, resulting in the overall performance improvement. Joint precoding and PCS significantly improve the resistance to nonlinear distortions for DMT systems. Simulations and experiments of 16-QAM and 64-QAM DMT transmission systems were conducted to evaluate the performance. The experimental results show that at the NGMI threshold of 0.92, up to 4 dB enhancement of the receiver sensitivity has been achieved by the proposed scheme when 4-GBd 64-QAM signals are transmitted.

Uniform Circular Antenna (UCA) is a convenient way to generate multiple Orbital Angular Momentum (OAM) beams simultaneously, and OAM-MIMO is promised to be an augmentation to enhance capacity. In this paper, concentric UCA which is an elaborate arrangement of MIMO antennas is adopted. OAM-MIMO transceiver mode selection and precoding architectures are proposed to improve the coaxial capacity performance. Non-coaxial parameters and their effect on mathematical channel matrix are analyzed, and modified non-coaxial parameters are further introduced to simplify the channel expression. Using the central antenna to separate transmitting (TX) misalignment and receiving (RX) misalignment, modified non-coaxial parameters and deviation matrices can be obtained with reduced complexity, dealing with the lack of non-coaxial parameters in practice. Moreover, to make for the orthogonality between different OAM modes, pre-transmitting and pre-receiving matrices are designed employing estimated modified non-coaxial parameters or deviation matrices to transfer the non-coaxial channel into a block circulant one. The provided simulation results verify the superiority of the proposed mode-selection and precoding design as well as the effectiveness of the proposed non-coaxial pre-processing scheme.

This paper proposes an optimal joint transmit and receive antenna subsets (TRASs) selection scheme for linear precoding-aided spatial modulation (PSM) systems. The optimal joint TRASs selection is performed by exhaustively searching, so that it is difficult to analyze an achievable diversity gain and it has a huge complexity. To tackle this problem, we propose a decoupled TRAS selection method which selects receive antenna subset (RAS) and transmit antenna subset (TAS) in a two-step serial manner. By computing a lower bound on the pairwise error probability and conducting extensive simulations, it is shown that the zero-forcing (ZF)-based PSM system with $N_{T} $ transmit antennas, $N_{S} $ selected transmit antennas, $N_{R} $ receive antennas, and $N_{D} $ selected receive antennas achieves diversity order of $(N_{T} -N_{D} +1)(N_{R} -N_{D} +1)$ even with TAS selection. Furthermore, decreasing the number of active transmit antennas by TAS selection after RAS selection is analytically shown to always degrade the bit error rate performance. The analysis results are validated by simulations. These analytical and simulation results can be regarded as natural extensions of earlier works on receive antenna selection and transmit antenna selection for the PSM systems. In addition, we study and compare two efficient algorithms for TRAS selection. First, incremental and decremental algorithms are employed for separable RAS and TAS successive selection, respectively, which have an excellent performance. It is analytically shown that the computational complexity of the first proposed decoupled suboptimal TRAS selection scheme is enormously reduced compared to the joint optimal and decoupled optimal algorithms. Second, an incremental TAS selection approach replaces the decremental strategy in the first TRAS selection algorithm to further reduce the complexity.

Hybrid beamforming (HB) is a solution for reducing radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems. By reducing RF chains, HB can achieve energy efficient transmission compared to fully digital beamforming of massive MIMO system. However, if the resolution of RF precoder is not quantized, the energy consumption of HB may become similar to fully digital beamforming. And, if the resolution of RF precoder is heavily quantized, the spectral efficiency of HB is decreased significantly. Non-orthogonal multiple access (NOMA) is one of the emerging technologies for the fifth generation (5G) or beyond fifth-generation (B5G) wireless communication to support high spectral efficiency. Especially, power domain NOMA (PD-NOMA) can share same resources to transmit signals and divide the signals in power domain by using successive interference cancellation (SIC). In this paper, the adoption of NOMA shows increase of spectral efficiency in a low-resolution RF precoder. To apply NOMA, two users with high channel correlation are selected according to the proposed criterion. Then, the RF precoder and digital precoder that are applied to both users at the same time are determined. Finally, intra-pair user fairness is achieved by power allocation. The ratio of channel power is compared for each user in each pair. Then, users with high channel power are assigned with low power. It is shown that the spectral efficiency of the proposed scheme is higher than the conventional scheme in heavily quantized RF precoder.

MIMO mobile systems, with a large number of antennas at the base-station side, enable the concurrent transmission of multiple, spatially separated information streams, and therefore, enable improved network throughput and connectivity both in uplink and downlink transmissions. Traditionally, such MIMO transmissions adopt linear base-station processing, that translates the MIMO channel into several single-antenna channels. While such approaches are relatively easy to implement, they can leave on the table a significant amount of unexploited MIMO capacity and connectivity capabilities. Recently-proposed non-linear base-station processing methods claim this unexplored capacity and promise substantially increased network throughput and connectivity capabilities. Still, to the best of the authors’ knowledge, non-linear base-station processing methods not only have not yet been adopted by actual systems, but have not even been evaluated in a standard-compliant framework, involving all the necessary algorithmic modules required by a practical system. In this work, for the first time, we incorporate and evaluate non-linear base-station processing in a 3GPP standard environment. We outline the required research platform modifications and we verify that significant throughput gains can be achieved, both in indoor and outdoor settings, even when the number of base-station antennas is much larger than the number of transmitted information streams. Then, we identify missing algorithmic components that need to be developed to make non-linear base-station practical, and discuss future research directions towards potentially transformative next-generation mobile systems and base-stations (i.e., 6G) that explore currently unexploited non-linear processing gains.

The fifty-year progress of faster-than-Nyquist (FTN) signaling is surveyed. FTN signaling exploits non-orthogonal dense symbol packing in the time domain for the sake of increasing the data rate attained. After reviewing the system models of both the conventional Nyquist-based and FTN signaling transceivers, we survey the evolution of FTN techniques, including their low-complexity detection and channel estimation. Furthermore, in addition to the classic FTN signaling philosophy, we introduce the recent frequency-domain filtering and precoding aided schemes. When relying on precoding, the information rate of FTN signaling becomes related to the eigenvalues of an FTN-specific intersymbol interference matrix, which provides a unified framework for the associated information-theoretic analysis and simplifies the associated power allocation specifically designed for increasing the information rate attained. We show that the FTN signaling scheme combined with bespoke power allocation employing a realistic raised-cosine shaping filter achieves the Shannon capacity associated with ideal rectangular shaping filters.

The current broadband coverage area requisites and the expected user demand is satisfied by the state of the art satellite industry by using multiple spot beams of high throughput satellites with fixed multi-beam pattern and footprint planning. However, in recent years, new mobile broadband users with dynamic traffic demand are requesting for services in remote geographical locations such as air (aeroplanes) and water (ships). Furthermore, the expected demand varies with time and geographical location of the users. Hence, a practical approach to meet such heterogeneous demand is to plan adaptive beams to the satellites equipped with beamforming capabilities. In this paper, we study the state of the art fixed multi-beam pattern and footprint plan and show its drawbacks to support the non-uniformly distributed user terminals and varying traffic demands. To end this, we propose an adaptive multi-beam pattern and footprint plan where we design spot beams with flexible size and position based on the spatial clustering of the users in order to increase the flexibility of the high throughput satellite system. Numerical simulations demonstrate the high system performance of the proposed methodology.

We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the receiver. In this context, we investigate the impact of imperfect CSI at the transmitter (CSIT) on the detection performance. We first model the CSIT degradation based on channel estimation errors to investigate its impact on the detection performance at the receiver. To mitigate the effect of CSIT deterioration at the latter, we propose learning-based techniques for hard and soft detection that use downlink precoded pilot symbols as training data. We note that these pilots are originally intended for signal-to-interference-plus-noise ratio (SINR) estimation. We validate the approach by proposing a lightweight implementation that is suitable for online training using several state-of-the-art classifiers. We compare the bit-error rate (BER) and the runtime complexity of the proposed approaches where we achieve superior detection performance in harsh channel conditions while maintaining low computational requirements. Specifically, numerical results show that severe CSIT degradation impedes the correct detection when a conventional detector is used. However, the proposed learning-based detectors can achieve good detection performance even under severe CSIT deterioration, and can yield 4–8 dB power gain for BER values lower than 10−4 when compared to the classic linear minimum mean square error (MMSE) detector.