Downlink Of Multiuser Systems Research Articles

Low complexity hybrid beamforming for downlink multi-user MIMO OFDM systems

Hybrid beamforming is critical for massive multiple input multiple output (MIMO) to achieve high spectral efficiency at reduced hardware cost. Existing hybrid beamforming schemes either design the analog beamforming and digital beamforming separately, or jointly optimize them through alternating optimization. Such strategies incur non-negligible performance degradation or huge computational complexity in optimization, which is especially evident with the partially connected architecture. In this work, we consider a general downlink multi-user MIMO orthogonal frequency division multiplexing (OFDM) system with the hybrid beamforming architecture, where finite-resolution phase shifters are adopted. We propose an efficient beamforming strategy on the sum rate maximization criterion. Rather than eliminating the interference entirely by digital beamforming, we optimize the analog beamforming by considering the interference at the same time. We consider a fixed digital beamforming structure to reduce the overall complexity. Given the fact of finite-resolution phase shifters, the analog beamforming is optimized over discrete phase values. Compared to existing hybrid beamforming designs, the proposed algorithm achieves much improved performance. By applying the average channel covariance to the analog beamforming design, the computational complexity of the proposed algorithm is further reduced. Meanwhile, the proposed algorithm is applicable to both fully and partially connected architectures.

Performance analyses of intelligent reflecting surface aided downlink multi-user rate-splitting multiple access system for 6G applications

The future wireless networks will handle extremely high data rates and fundamentally new applications, demanding a paradigm leap in wireless radio technology. The combination of rate-splitting multiple access (RSMA) and an intelligent reflecting surface (IRS) is an efficient way for improving the spectral/energy efficiency of next-generation wireless networks, such as 5G and beyond and 6G. In this paper, we propose an IRS-assisted downlink multi-user RSMA system towards 6G applications. Near users (NU) and cell edge users (CEU) are the two types of users considered in the proposed system, with NUs directly connected to the BS and CEUs connected via IRS. During transmission, the IRS acts as a relay and we investigate the performance of an IRS-aided RSMA system in terms of sum rate and outage probability under various NU and CEU scenarios. The novelty of the work is to derive the sum rate of the system model over Rayleigh and Rician fading channel we also derive the outage probability expression of the system for direct and indirect transmission to the CEUs and direct transmission to the NUs. Here we plot and analyzed the sum rate and outage probability results by varying the user power allocation, transmit power, transmit signal to noise ratio (SNR) ρB and other parameters. The results show that at 7dBm of transmit power, the NU achieves an outage probability greater than 10−1 and the CEU achieves an outage probability less than 10−2.

Multiple Access for Downlink Multi-User VLC System: NOMA or OMA User Pairing?

Multiple Access for Downlink Multi-User VLC System: NOMA or OMA User Pairing?

Resource Allocation in MU-MISO Rate-Splitting Multiple Access With SIC Errors for URLLC Services

Rate-splitting multiple access (RSMA) is promising to be developed as a key enabling technology for 6G. This paper investigates the resource allocation problem in a downlink multi-user and multiple-input single-output (MU-MISO) RSMA system with successive interference cancellation (SIC) errors for ultra-reliable and low-latency communications (URLLC). The single-carrier RSMA (SC-RSMA) resource allocation scheme with URLLC is first given, where the beamforming vector and the transmission rate are optimized to maximize the effective throughput (ET). To solve the non-convex optimization problem formulated in the SC-RSMA scheme, an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) is proposed to alternately optimize the beamforming factor and the transmission rate. Furthermore, the multicarrier RSMA (MC-RSMA) scheme is developed for the URLLC access of large-scale users. Based on the results of the SC-RSMA scheme, a low-complexity three-step optimization algorithm is proposed to solve the resource allocation problem formulated in the MC-RSMA scheme. Finally, the simulation results show that the RSMA scheme and MC-RSMA scheme can respectively achieve higher ET than the non-orthogonal multiple access (NOMA) scheme and multi-carrier NOMA (MC-NOMA) scheme in the URLLC scenario, and verify that RSMA can reduce the latency and improve the reliability of the system.

Deep Reinforcement Learning Based End-to-End Multiuser Channel Prediction and Beamforming

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the previous methods which either require perfect channel state information (CSI), or estimate outdated CSI and set constraints on pilot sequences, the proposed algorithms have no such premised assumptions or constraints. Firstly, RL is considered in channel prediction and the actor-critic aided CP algorithm is proposed at the base station (BS). With the received pilot signals and partial feedback information, the actor network at BS directly outputs the predicted downlink CSI without channel reciprocity. After obtaining the CSI, BS generates the beamforming matrix using zero-forcing (ZF). Secondly, we further develop a deep RL based two-layer architecture for joint CP and BF design. The first layer predicts the downlink CSI with the similar actor network as in the CP algorithm. Then, by importing the outputs of the first layer as inputs, the second layer is the actor-critic based beamforming layer, which can autonomously learn the beamforming policy with the objective of maximizing the transmission sum rate. Since the learning state and action spaces in the considered CP and BF problems are continuous, we employ the actor-critic method to deal with the continuous outputs. Empirical numerical simulations and the complexity analysis verify that the proposed end-to-end algorithms could always converge to stable states under different channel statistics and scenarios, and can beat the existing traditional and learning based benchmarks, in terms of transmission sum rate.

Non-Orthogonal Multiple Access Technique Performance Characterization in Downlink Network

In this paper, a Non Orthogonal Multiple Access (NOMA) downlink multiuser communication system is, composed of a single base station (BS) over a wireless channel to M users equipped with single antenna is considered. The BS is located at the center of a disc denoted by D with radius R. A Gaussian-Chebyshev quadrature approximation of the cumulative density function, of unordered channel gain is applied in the case of two users independent and identically distributed, located in a disk of radius R, under two techniques OMA and NOMA, in a downlink network, to evaluate ergodic capacities of NOMA and OMA users at the bounds of the fairness region of NOMA are calculated, and from a comparison between NOMA and OMA ergodic capacities as functions of SNR ratio, one can observe that NOMA always outperforms OMA technique. In this system model, NOMA users are sorted according to their locations from the BS, so that an mth user is able to detect its own signal, after performing Successive Interference Cancellation (SIC) algorithm based on an iterative cancellation of the ith user information. In the case of the failure of mth user to detect the ith user signal, mth user meets outage probability. This outage probability under an increasing SNR ratio of two NOMA users is evaluated for different values of N parameter, disk radius R, path loss exponent and power allocation coefficients. One can note that, NOMA users achieve more outage probability with increasing values of disk radius R, and the path loss exponent tends to increase, as there are more obstructions, and NOMA users tend to achieve more outage probability.

Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications

Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.

Joint Placement and Beamforming Design for IRS-Enhanced Multiuser MISO Systems

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

Multiobjective Optimization Designs in Downlink NOMA Transmission Systems

To further balance the high-capacity transmission requirement and the scarce spectrum resources status quo, we investigate the joint optimization of energy efficiency (EE) and spectral efficiency (SE) in a downlink multiuser NOMA system. While ensuring the user quality of service (QoS), a power allocation algorithm based on two-layer optimization is proposed by transforming the original multiobjective optimization problem into a univariate problem through the linear weighted sum method. Based on the optimal user power allocation coefficient solution obtained from the inner layer, the outer layer applies the bisection principle to search for the optimal system transmit power according to the uniqueness of the maximum value of pseudo-concave functions. Simulation results show that the proposed algorithm always converges within fewer iterations and can achieve the flexible tradeoff between EE and SE. This is of great significance for improving the energy utilization of actual scenarios with time-varying communication demands.

Beamforming Design for Reconfigurable Intelligent Surface via Bayesian Optimization

This letter investigates the transmission design in the multi-user downlink system using the reconfigurable intelligent surface (RIS). The conventional transmission scheme requires the channel estimation by transmitting pilots and feeds back the channel state information (CSI) to the base station, which causes lots of overhead and substantial radio-frequency chains at the RIS. In this letter, we propose a cost-efficient downlink transmission scheme via Bayesian optimization that dispenses with CSI. The beamformer at the BS and the phase rotation at the RIS are jointly designed by minimizing the sum mean square error (MSE). Since the proposed scheme dispenses with CSI, the channel estimation and the CSI feedback are not required, which saves the number of training slots and dispenses with RF chains at the RIS. Moreover, the proposed scheme can be extended to the minimax MSE problem and the energy harvesting problem. Simulation results show that the proposed transmission scheme outperforms the particle swarm optimization scheme.

Rate-Splitting Multiple Access for Downlink Multiuser MIMO: Precoder Optimization and PHY-Layer Design

Rate-Splitting Multiple Access (RSMA) has recently appeared as a powerful and robust multiple access and interference management strategy for downlink Multi-user (MU) multi-antenna communications. In this work, we study the precoder design problem for RSMA scheme in downlink MU systems with both perfect and imperfect Channel State Information at the Transmitter (CSIT) and assess the role and benefits of transmitting multiple common streams. Unlike existing works which have considered single-antenna receivers (Multiple-Input Single-Output–MISO), we propose and extend the RSMA framework for multi-antenna receivers (Multiple-Input Multiple-Output–MIMO) and formulate the precoder optimization problem with the aim of maximizing the Weighted Ergodic Sum-Rate (WESR). Precoder optimization is solved using Sample Average Approximation (SAA) together with the proposed vectorization and Weighted Minimum Mean Square Error (WMMSE) based approach. Achievable sum-Degree of Freedom (DoF) of RSMA is derived for the proposed framework as an increasing function of the number of transmitted common and private streams, which is further validated by the Ergodic Sum Rate (ESR) performance using Monte Carlo simulations. Conventional MU–MIMO based on linear precoders and Non-Orthogonal Multiple Access (NOMA) schemes are considered as baselines. Numerical results show that with imperfect CSIT, the sum-DoF and ESR performance of RSMA is superior to those of the two baselines, and is increasing with the number of transmitted common streams. Moreover, by better managing the interference, RSMA not only has significant ESR gains over baseline schemes but is more robust to CSIT inaccuracies, network loads and user deployments.

Approximation of Capacity for Downlink Multi-User System with Combination of Precoding and NOMA Methods

Enhancing performance of downlink MU systems is an attractive and important research for future wireless systems. The non-orthogonal multiple access (NOMA) method was proposed to improve the performance of MU systems. In order to further improve the outage probability (OP) and ergodic capacity (EC) of downlink NOMA MU systems, we propose the combination of precoding and NOMA methods, and then the OP and EC of MU systems with our novel method are derived in scenarios of perfect and imperfect successive interference cancellation (SIC) scheme. Moreover, the closed-form expression of OP and EC for both scenarios is theoretically derived and compared with Monte Carlo simulations. The results show that, the analysis method is accurate, and the proposed combining precoding and NOMA can further enhance the performance of MU systems in comparing with the original orthogonal multiple access method.

Machine Learning-Inspired Algorithmic Framework for Intelligent Reflecting Surface-Assisted Wireless Systems

This study considers the simultaneous optimization problem of the transmit beamforming at the access point and the reflecting beamforming at the intelligent reflecting surface (IRS) in an IRS-assisted multiuser downlink system. This joint optimization problem is nonconvex and challenging due to the intricate coupling of transmit and reflecting beamforming variables and the highly nonconvex constant modulus constraints. All the known solutions apply the alternating optimization framework to decouple the joint optimization problem into two problems. Then, these problems are optimized individually with different optimization methods in an alternating manner. However, such an alternative procedure may cause performance degradation. On the basis of the cross-entropy (CE) framework, which was developed initially for machine learning applications, we propose a machine learning-inspired algorithmic framework for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> optimizing transmit and reflecting beamforming in an IRS-assisted wireless system. Extensive simulation results reveal that the proposed CE-based algorithms significantly perform better than the state-of-the-art test algorithms for various system configurations.

Joint Precoding and Power Allocation for Multiuser MIMO System With Nonlinear Power Amplifiers

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

Learning Robust Beamforming for MISO Downlink Systems

This letter investigates a learning solution for robust beamforming optimization in downlink multi-user systems. A base station (BS) identifies efficient multi-antenna transmission strategies only with imperfect channel state information (CSI) and its stochastic features. To this end, we propose a robust training algorithm where a deep neural network (DNN), which only accepts estimates and statistical knowledge of the perfect CSI, is optimized to fit to real-world propagation environment. Consequently, the trained DNN can provide efficient robust beamforming solutions based only on imperfect observations of the actual CSI. Numerical results validate the advantages of the proposed learning approach compared to conventional schemes.

Probabilistic Constructive Interference Precoding for Imperfect CSIT

This paper proposes a stochastic-robust constructive interference (CI) precoding scheme for downlink multi-user MISO systems, assuming that channel state information (CSI) at the transmitter side (CSIT) is contaminated by Gaussian-distributed uncertainties. Our objective is to minimize the total transmit power under users’ quality-of-service constraints: formulating CI at each user with high probabilities for a given target signal-to-noise ratio (SINR). We first analyze the probability of CI under imperfect CSIT. A series of approximations are then developed, transforming the intractable stochastic CI constraints into determined convex constraints. The non-convex stochastic-robust CI power minimization (CIPM) problem is then converted into second-order cone programming. We show that we could create tightened or relaxed approximations by changing the parameters, enabling us to find upper-bounds and lower-bounds for the original stochastic CIPM problem. The best parameter values corresponding to the tightest upper and lower bounds are also discussed and obtained. Simulation results show that the proposed methods reasonably approximate the stochastic CIPM problem. Using the given parameter values, it can guarantee the required probability of CI for each user under acceptable channel uncertainties and outperform the existing robust CI precoding in terms of both transmit power and feasibility rate. The small gap between the upper and lower bounds also shows that the proposed method does not cause too much performance loss.

Joint Secure Design of Downlink and D2D Cooperation Strategies for Multi-User Systems

This work studies the role of inter-user device-to-device (D2D) cooperation for improving physical-layer secret communication in multi-user downlink systems. It is assumed that there are out-of-band D2D channels, on each of which a selected legitimate user transmits an amplified version of the received downlink signal to other legitimate users. A key technical challenge for designing such systems is that eavesdroppers can overhear downlink as well as D2D cooperation signals. We tackle the problem of jointly optimizing the downlink precoding, artificial noise covariance, and amplification coefficients that maximize the minimum rate. An iterative alternating optimization algorithm is proposed based on the matrix fractional programming. Numerical results confirm the performance gains of the proposed D2D cooperation scheme compared to benchmark secret communication schemes.

Deep Learning Methods for Universal MISO Beamforming

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.

Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention from the research community recently. RISs create favorable propagation conditions by controlling the phase shifts of reflected waves at the surface, thereby enhancing wireless transmissions. In this paper, we study a downlink multi-user system where the transmission from a multi-antenna base station (BS) to various users is achieved by an RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only a limited number of discrete phase shifts can be realized by a finite-sized RIS. A hybrid beamforming scheme is proposed and the sum-rate maximization problem is formulated. Specifically, continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively, and an iterative algorithm is designed to solve this problem. Both theoretical analysis and numerical validations show that the RIS-based system can achieve good sum-rate performance by setting a reasonable size of the RIS and a small number of discrete phase shifts.

Energy-Efficient Power Allocation with Hybrid Beamforming for Millimetre-Wave 5G Massive MIMO System

Massive multiple-input and multiple-output systems combined with hybrid beamforming technique is a key approach to achieve high data rate and extended cell coverage in the fifth generation (5G) cellular networks . Specifically, the precoding for analog/digital hybrid beamforming technique is absolutely essential for millimeter-wave (mm-wave) frequency based transmission for 5G, simply because the technique reduces hardware complexity and energy consumption for high-frequency design. In order to prove the aforementioned advantages, the present paper will investigate the energy efficiency (EE) and optimization of low complexity hybrid precoding (EELCHP) algorithm to enable the reduction of radio frequency (RF) chains within the base station (BS). The proposed solution will also incorporate high-resolution phase shifters (HRPS) specifically for downlink multi-user mm-wave system. The intelligence within the EELCHP algorithm for hybrid beamforming is based on the fully complex zero-forcing (ZF) precoding mechanism that allowed the maximization of energy efficiency by reducing the RF chains, without affecting the potentially increasing number of antennas. In addition, maximum EE is possible by eliminating the nonlinear concave fractional issue based on the Lagrange duality method. During the simulation process of the proposed method, we further realized that hardware complexity and power consumption can be reduced by allowing the RF chains to select only one beam per switch within the beamforming solution. Furthermore, with the implementation of EELCHP algorithm, more energy is saved due to low complexity system by reducing the required RF chain.