Multiuser Multiple-input Multiple-output Systems Research Articles

Wireless powered NOMA-based cognitive radio for 6G networks

The need for physically charging mobile devices is anticipated to become a thing of the past in the not-too-distant future. In this paper, a novel approach for wireless-powered mobile devices is presented, which is based on non-orthogonal multiple access (NOMA) and multi-user multiple-input multiple-output (MU-MIMO) antenna systems. The proposed method is specifically designed for use in a cognitive underlay radio (CR) scenario, where the primary network requires a certain quality of service (QoS). Meanwhile, secondary network users can download their data using the same spectrum. The main objective of this paper is to maximize the sum rate of the secondary network. We have optimized a joint beamforming vector for both primary and secondary networks and a time-switching coefficient for energy harvesting and information transfer to achieve this objective. The initial problem formulation was non-convex, requiring the implementation of various techniques to ensure an efficient and low-complexity algorithm. To this end, we employed semi-definite programming, successive convex approximation, and alternating optimization. The proposed NOMA-based solution outperforms the TDMA-based benchmark scheme regarding sum data rate, specifically in the low transmit power region.

Product manifold optimization-based joint beamforming for RIS-aided MIMO systems: A fairness based approach

Reconfigurable intelligent Surface (RIS) can effectively improve the communication quality and extend the signal coverage range by reconfiguring the wireless communication transmission environment, which is very suitable for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, a product manifold optimization-based beamforming design (PMO-BD) algorithm for a RIS-assisted multi-user MIMO system is proposed to enhance fairness among users. We establish a lower bound for the minimum signal-to-interference-plus-noise ratio (SINR) among users as the optimization objective, which is subjected to multiple constraints. By maximizing the lower bound to ensure the quality of service (QoS) for each user, fairness among users get improved. The PMO-BD algorithm exploits the internal structure of the product manifold to deal with the high dimension and nonlinear characteristics of the problem effectively, thereby reducing algorithm complexity and enhancing convergence rates. Simulation results verify the advantages of the proposed PMO-BD algorithm in comparison to other algorithms.

Asymptotic performance of reconfigurable intelligent surface assisted MIMO communication for large systems using random matrix theory

AbstractReconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS‐assisted multiuser multiple‐input multiple‐output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum‐rate. For the large dimension scenario and high‐rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS‐assisted MIMO systems. The asymptotical signal‐to‐interference‐plus‐noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second‐order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large‐scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods.

Spectral decomposition-based optimization for MMSE receiver design in an uplink MU-MIMO cooperative communication system with imperfect CSI

The receiver’s architecture is always a critical issue in multiple-input multiple-output (MIMO) systems, as it has a great impact on the system’s performance and complexity. Accordingly, in this paper, a spectral decomposition-based minimum mean squared error (MMSE) receiver is proposed in an uplink multi-user MIMO (MU-MIMO) system with a full-duplex decode and forward relay. Imperfect channel state information is assumed at the relay and base station where, least-squares channel estimation technique is applied due to its simplicity and low complexity. An equivalent relay is applied for self-interference cancellation. While, multi-user interference is mitigated by using the block diagonalization concept. A closed-form expression for the mean squared error (MSE) of the estimated data symbol is derived in the presence of channel estimation error. Moreover, an optimization problem is solved to derive an expression for the proposed MMSE receiver’s matrix. Finally, the performance of the proposed receiver is compared to that of the conventional receiver and Zero-forcing (ZF) receiver. Results show the superiority of the proposed receiver over both receivers.

Hybrid Wideband Beamforming for Sum Spectral Efficiency Maximization in Millimeter-Wave Relay-Assisted Multiuser MIMO Cognitive Radio Networks

Relay-assisted hybrid beamforming plays an inevitable role in enhancing network coverage, transmission range, and spectral efficiency while simultaneously reducing hardware cost, power consumption, and hardware implementation complexity. This study investigates a cognitive radio network (CRN)-based hybrid wideband transceiver for millimeter-wave (mm-wave) decode-and-forward (DF) relay-assisted multiuser (MU) multiple-input multiple-output (MIMO) systems. It is worth mentioning that the underlying problem has not been addressed so far, which is a real motivation behind the proposed algorithm. The joint optimization of hybrid processing components and the constant amplitude constraints imposed by the analog beamforming solution make this problem non-convex and NP-hard. Furthermore, the analog beamformer common to all sub-carriers is another challenging aspect of the underlying problem. To derive the frequency-flat analog processing component in the radio frequency (RF) domain and frequency-dependent baseband processing matrices in the baseband domain, the original complicated problem is reformulated as two single-hop sum-rate maximization sub-problems. Taking advantage of this decomposition, the sum spectral efficiency is maximized through RF precoding and combining. On the other hand, the impact of interference among transmitted data streams and inter-user interference (IUI) is minimized via baseband processing matrices. Finally, computer simulations are conducted by changing system parameters, considering both perfect and imperfect channel state information (CSI). Simulation results demonstrate that the proposed algorithm achieves performance close to full-complexity precoding and outperforms other well-known hybrid beamforming techniques. Specifically, more than 95% efficiency is achieved with perfect CSI, and more than 90% efficiency is attained under the assumption of 30% error in the estimated channels.

Optimized Precoding for MU-MIMO With Fronthaul Quantization

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information (CSI).

Rate-Splitting Multiple Access for Quantized Multiuser MIMO Communications

This paper investigates the sum spectral efficiency maximization problem in downlink multiuser multiple-input multiple-output systems with low-resolution quantizers at an access point (AP) and users. We consider rate-splitting multiple access (RSMA) to enhance spectral efficiency by offering opportunities to boost achievable degree-of-freedom. Optimizing RSMA precoders, however, is highly challenging due to the minimum rate constraint when determining the common rate. The quantization errors coupled with the precoders make the problem more complicated. In this paper, we develop a novel RSMA precoding algorithm incorporating quantization errors for maximizing the sum spectral efficiency. To this end, we first obtain an approximate spectral efficiency in a smooth function. Subsequently, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We propose a computationally efficient algorithm to find the principal eigenvector of the NEP as a sub-optimal solution. We also extend the weighted minimum mean square error-based RSMA precoding to the considered quantization system. Simulation results validate the proposed methods. The key benefit of using RSMA over spatial division multiple access (SDMA) comes from the ability of the common stream to balance between the channel gain and quantization error in multiuser MIMO systems with different quantization resolutions.

Wireless communications empowered by reconfigurable intelligent surfaces: Model-based vs model-free channel estimation

Reconfigurable intelligent surfaces (RISs) are lately being attractive for their great potential in future sixth generation wireless communications (6G), which is attributed to their affordable energy consumption and easy integration. However, the large numbers of low-cost reflecting elements comprising RISs impose challenges for channel acquisition in various RIS-based wireless applications, such as RIS-enhanced orthogonal frequency-division multiplexing and multi-user multiple-input multiple-output systems. In this article, we first overview the state-of-the-art RIS hardware architectures designed to assist channel estimation for RIS-empowered wireless communication systems. We also overview existing channel estimation approaches, which are categorized into model-based and model-free techniques, and discuss their advantages and limitations depending on the RIS deployment. Design challenges with RIS-empowered systems in terms of hardware and other parameter limitations are presented, together with future research directions for channel estimation in RIS-based wireless systems, such as RISs with extremely large numbers of elements, multi-hop communications with RISs, and frequency division duplexing for high mobility systems.

Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.

A Scalable Precoding Processor for Large-Scale MU-MIMO Systems

The number of devices served by baseband stations is constantly increasing due to the rising data traffic in modern communication systems. In order to support large-scale multi-user multiple-input multiple-output (MU-MIMO) systems and achieve their capacity, it is necessary to consider power allocation and user selection along with precoding. This paper introduces a scalable MU-MIMO precoding processor that solves the joint optimization problem for precoding, power allocation, and user selection. We define custom vector instructions and dedicate vector arithmetic operators based on the RV32IM instruction set architecture to efficiently support various MU-MIMO baseband processing scenarios. The proposed vector operators include parallel dual-precision multipliers to enable energy-efficient processing by adjusting the computing resolution of each step without degrading the algorithm-level quality. The proposed processor is fabricated using 28nm CMOS technology and is capable of solving the state-of-the-art joint optimization problem in only 0.51ms for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$64\times 64$</tex-math> </inline-formula> large-scale MU-MIMO configuration. Our processor achieves up to 17.4 times higher processing efficiency compared to previous precoder design, even when supporting the largest number of users and the most complicated algorithm.

Sum-Rate Maximization of RIS-Aided Multi-User MIMO Systems With Statistical CSI

This paper investigates a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MIMO) system by considering only the statistical channel state information (CSI) at the base station (BS). We aim to maximize its sum-rate via the joint optimization of precoding matrix at the BS and phase shifts vector at the RIS. However, the multi-user MIMO transmissions and the spatial correlations make the optimization cumbersome. For tractability, an asymptotic sum-rate is derived under a large number of the reflecting elements. By adopting the asymptotic sum-rate as the objective function, optimal designs of the transmit precoding matrix and the phase shifts vector can be decoupled and solved individually. More specifically, a high-quality suboptimal solution of the transmit precoding matrix and phase shifts vectors can be obtained by utilizing the water-filling algorithm and the projected gradient ascent (PGA) algorithm, respectively. Comparing to the case of the instantaneous CSI assumed at the BS, the proposed algorithm based on the statistical CSI can achieve comparable performance but with much lower channel estimation overhead, information feedback overhead, and computational complexity, which is more affordable and appealing for practical applications. Moreover, the impact of spatial correlation on the asymptotic sum-rate is examined by using majorization theory.

Full-Wave Simulation and Scattering Modeling for Terahertz Communications

This paper presents a comprehensive study for analyzing and modeling the scattering phenomenon of terahertz (THz) waves bouncing on rough surfaces. More specifically, a generic parametric methodology to accurately model the rough characteristics focusing on the root-mean-square (RMS) height and correlation length of a surface is proposed. Firstly, a Monte Carlo-based approach is developed for the efficient and accurate software realization of such rough surfaces. Then, the full-wave simulator FEKO is used to simulate at 300 GHz the far-field behavior of scattering waves on 30 distinct surfaces having 6 RMS heights and 5 correlation lengths. By employing the directive scattering (DS) model, the variation of the scattering coefficient, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> , and its equivalent roughness, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha _{R}$</tex-math></inline-formula> , are studied for the scattering amplitude aspect. Furthermore, the statistical characteristics of the phase and polarization of the scattered signals are obtained and compared with those available in existing 3GPP channel model standards which are valid for lower frequency bands. These comparisons show that although the phases follow the normal distribution, as is the case with equivalent 3GPP channel models, the cross-polarization ratios (XPRs) follow the logistic distribution. This is an important difference which could have a great impact on the standardization activities of the current 3GPP-related working groups. Finally, the proposed scattering model at the THz band is realized through an algorithmic implementation. Compared with the classical two-dimensional DS model which characterizes only the amplitude of the scattered signal, the proposed three-dimensional scattering model can be efficiently used to accurately characterize all the important parameters of the scattered signals, namely, amplitude, phase, and polarization at the THz band. Additionally, the proposed scattering model can effectively work in conjunction with ray-tracing (RT) schemes leading to precise scattering and channel modeling for new applications, such as holographic radios and multi-user multiple-input multiple-output (MU-MIMO) systems.

FPLA: A Flexible Physical Layer Authentication Mechanism for Distributing Quantum Keys Securely via Wireless 5G Channels

Quantum Key Distribution (QKD) is popular for establishing a native secure quantum communication network. However, existing QKD networks are built via classical wired fiber channels; it is difficult to distribute quantum keys directly into mobile phones, and no effective candidate solution is available yet. This paper presents a novel Flexible Physical Layer Authentication (FPLA) mechanism that exploits the unique characteristic of wireless signals from mobile phones to securely distribute quantum keys via wireless 5G channels. In particular, a 5G Up-Link Sounding Reference Signal (SRS)-based transmission model is developed to capture and extract the unique characteristic, which is then used to distribute quantum keys. Moreover, the model could lose accuracy due to SRS variations introduced by 5G Multiuser Multiple-Input Multiple-Output (MU-MIMO), so a dimensional transformation residual network is designed to classify legitimate and malicious user equipment (UE). An average authentication accuracy of 96.8% is proved by FPLA in multiple experiments in a 3 dB Signal-to-Noise Ratio (SNR) test environment with a training dataset of 300 samples per malicious UE. Simulation results show that FPLA is able to adapt to antenna diversity in 5G MU-MIMO systems.

Optimizing Pilot Spacing in MU-MIMO Systems Operating Over Aging Channels

In the uplink of multiuser multiple input multiple output (MU-MIMO) systems operating over aging channels, pilot spacing is crucial for acquiring channel state information and achieving high signal-to-interference-plus-noise ratio (SINR). Somewhat surprisingly, very few works examine the impact of pilot spacing on the correlation structure of subsequent channel estimates and the resulting quality of channel state information considering channel aging. In this paper, we consider a fast-fading environment characterized by its exponentially decaying autocorrelation function, and model pilot spacing as a sampling problem to capture the inherent trade-off between the quality of channel state information and the number of symbols available for information carrying data symbols. We first establish a quasi-closed form for the achievable deterministic equivalent SINR when the channel estimation algorithm utilizes multiple pilot signals. Next, we establish upper bounds on the achievable SINR and spectral efficiency, as a function of pilot spacing, which helps to find the optimum pilot spacing within a limited search space. Our key insight is that to maximize the achievable SINR and the spectral efficiency of MU-MIMO systems, proper pilot spacing must be applied to control the impact of the aging channel and to tune the trade-off between pilot and data symbols.

Downlink channel estimation of intelligent reflective surface aided MU-MIMO system

Obtaining channel status information (CSI) is one of the main problems of the intelligent reflective surface (IRS) assisted communication system. This is because IRS has only a large number of passive reflective components and cannot process signals. In this paper, we aim to estimate downlink channel of IRS assisted multi-user multiple-input multiple-output (MU-MIMO) communication system. In time division duplex (TDD) mode, the downlink channel is estimated according to the uplink pilot by using channel reciprocity. We propose a parallel factor analysis based on projection subspace (PSS-PARAFAC) channel estimation scheme. In the first step, the common sparsity among multiple users is used for signal subspace projection to reduce noise energy and complexity. The second step is to convert each user signal received of base station (BS) into the PARAFAC problem. The iterative method of alternating least squares (ALS) is used to solve this problem. From the analysis of simulation results, the channel estimation performance of the proposed algorithm is better.

Low-Resolution MMSE Equalization for Massive MU-MIMO Systems

Finite-alphabet equalization that represents a spatial equalization matrix with low-resolution coefficients is a promising technique to reduce the power consumption, processing delay, and circuit area of baseband processing in the context of all-digital massive multiuser multiple-input multiple-output uplink systems. However, to minimize the performance loss caused by a coarse-resolution spatial equalization matrix, its coefficients must be carefully designed in the minimum mean-square error sense to achieve the desired bit error rate (BER) performance, which unfortunately constitutes an NP-hard optimization problem. To tackle this problem, we first reformulate the finite-alphabet equalization design problem as an unconstrained optimization on a smooth Riemannian manifold. Then we propose an algorithm based on Riemannian manifold optimization (RMO) to solve the reformulated problem. Based on simulation results, the proposed 2-bit RMO-assisted equalizer outperforms its state-of-the-art counterparts while maintaining the same asymptotic complexity. In addition, the proposed 2-bit RMOassisted equalizer exhibits a loss of only 1.47 dB at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> compared to an unquantized linear minimum mean squared error equalizer.

Opportunistic Schedule Selection for Multiuser MIMO FSO Communications: A Security- Reliability Trade-Off Perspective

The security and reliability of free space optical (FSO) communications and the trade-off between the two are the most critical characteristics to emphasize in FSO systems, especially as optical wireless communications continue to evolve. In this work, we investigate the impact of opportunistic transmit aperture selection (TAS) on the security-reliability trade-off (SRT) of the multiuser multiple-input multiple-output (MU-MIMO) free space optical system. We consider a general scenario in which the FSO system consists of a transmitter side with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> users and a single receiver with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> apertures communicating over generalized Malaga-M turbulence channels. We derive the TAS combined signal-to-noise ratio (SNR) distributions for the FSO MIMO channel between the transmitting users and the receiver. A statistical analysis of the TAS scheme is performed, where the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> th best user with the highest SNR is selected. Based on this analysis, the closed-form expressions for the outage probability, average bit error rate, intercept probability, and SRT are derived. Then, the diversity advantages provided by the channel variations considering the atmospheric turbulence and generalized nonzero boresight pointing errors are further discussed. The results show that the considered TAS significantly improves the security, reliability, and SRT performance and has the potential to solve existing FSO communication problems. Monte Carlo simulations are used to verify the correctness of the numerical results.

Link Adaptation for Rate Splitting Systems With Partial CSIT

Rate Splitting (RS) has emerged as a flexible multiple-access scheme for Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems, generalizing more conventional approaches such as linear precoding or Non-Orthogonal Multiple Access (NOMA), and providing additional robustness against imperfect channel state information at the transmitter. Actual achievable rates cannot be pre-calculated when facing imperfect and/or outdated channel state information at the transmitter (CSIT), so practical real-time mechanisms in the form of link adaptation (LA) are required. The available CSIT is used to design the RS precoders, which can accommodate some degree of robustness, whereas the physical layer feedback reports the outcome of the decoding of the codewords, in the form of acknowledgement (ACK) or negative ACK (NACK), providing the additional meta-robustness needed when the statistical model of CSIT errors is not accurate, and/or the precoders cannot fully track the channel dynamics. We characterize the outage rate region of RS and analyze the convergence of the complete LA-RS system under partial CSIT (non-accurate and non-permanent), providing relevant insights for addressing the adaptation of private and common rates, designing robust precoders with throughput as relevant metric, and handling the time-variant CSIT quality.

Wireless Beacon Enabled Hybrid Sparse Channel Estimation for RIS-Aided mmWave Communications

The inability of reconfigurable intelligent surfaces (RISs) to signal processing has become a critical bottleneck in terms of the precise capture of cascaded channels. There exists a pair of critical issues: i) the high-dimensional cascaded channel to be estimated, which always entails substantial pilot overhead typically proportional to RIS elements, and ii) the high possibilities of false alarms and unfaithful angle detection during cascaded channel estimates, referred to as the angular distortion, particularly in the case of conventional fully-passive RIS systems. To circumvent these issues, we investigate in this paper a multi-user multiple-input multiple-output (MIMO) system assisted by a wireless beacon (WB) enabled RIS over millimeter wave (mmWave) frequency. Specifically, WB is a functionally independent hardware that attaches parasitically to the RIS using only one radio frequency chain for broadcasting pilots, allowing for the accurate acquisition of partial cascaded channel information, e.g., some of angles of arrival/departure (AoA/AoDs) and complex gains. This efficiently eliminates the angular distortion effect and thus facilitates high precision recovery of sparse cascaded mmWave channels. Then, a hybrid structured sparsity (HSS) model is presented to capture hybrid sparse priors and approximate exact posterior distributions associated with RIS-aided twin-hop channels. We conceive of channel estimation as a compressive sensing problem in contrast to its conventional copy due to the presence of an unknown sensing matrix. To tackle this problem, an expectation-maximization (EM)-based algorithm, i.e., HSS-EM, is developed in order to fully employ the sparse priors encapsulated by the proposed HSS model for a robust and accurate recovery of the cascaded channels. Numerical results validate our analytical claims and demonstrate the significant improvement in terms of the normalized mean square error (NMSE) performance over conventional designs.

Joint Beamforming and Power Optimization for Cooperative Downlink MIMO Systems

Herein, we propose a joint optimization algorithm to minimize the overall transmit power in multicell multiuser multiple-input multiple-output (MIMO) systems for B5G. The proposed algorithm is an iterative optimization algorithm that jointly computes the transmit/receive beamforming vectors and the transmit power. The superiority of the proposed method is demonstrated through simulations.