Multiuser Multiple-input Single-output Research Articles

Robust Beamforming Design for IRS-Aided URLLC in D2D Networks

Intelligent reflecting surface (IRS) and device-to-device (D2D) communication are two promising technologies for improving transmission reliability between transceivers in communication systems. In this paper, we consider the design of reliable communication between the access point (AP) and actuators for a downlink multiuser multiple-input single-output (MISO) system in the industrial IoT (IIoT) scenario. We propose a two-stage protocol combining IRS with D2D communication so that all actuators can successfully receive the message from AP within a given delay. The superiority of the protocol is that the communication reliability between AP and actuators is doubly augmented by the IRS-aided first-stage transmission and the second-stage D2D transmission. A joint optimization problem of active and passive beamforming is formulated, which aims to maximize the number of actuators with successful decoding. We study the joint beamforming problem for cases where the channel state information (CSI) is perfect and imperfect. For each case, we develop efficient algorithms that include convergence and complexity analysis. Simulation results demonstrate the necessity and role of IRS with a well-optimized reflection matrix, and the D2D network in promoting reliable communication. Moreover, the proposed protocol can enable reliable communication even in the presence of stringent latency requirements and CSI estimation errors.

Intelligent Reflecting Surface Aided Secure Transmission With Colluding Eavesdroppers

This paper studies a secure multiuser multiple-input single-output (MISO) communication system aided by an intelligent reflecting surface (IRS), where multiple colluding eavesdroppers (EVEs) coexist. We aim to maximize the sum secrecy rate (SSR) via jointly optimizing the beamforming vectors, the artificial noise (AN) and the phase shifts at the IRS subject to the maximum transmit power constraint and unit modulus constraints. To address the non-convex optimization problem, we first propose an alternating optimization (AO) algorithm based on semidefinite relaxation (SDR) and obtain a high-quality sub-optimal solution. In order to reduce the high computational complexity, a low-complexity alternating optimization (LC-AO) algorithm is developed, in which the beamforming vectors, AN and the IRS phase shifts are optimized alternately by the generalized power iteration (GPI) and the Riemannian manifold conjugate gradient (RMCG) algorithm, respectively. Simulation results show the advantages of deploying the IRS in improving the system secrecy performance.

RF Precoding for Cognitive Radio Systems

In this letter, a precoding framework in the radio-frequency (RF) domain is developed for the downlink of a multiuser multiple-input single-output underlay cognitive radio (CR) system. Firstly, a low hardware complexity analog multiantenna architecture is proposed for the system’s transmitter. The proposed analog architecture is based on a phase-shifting network driven by an variable gain amplifier and has low power consumption since it avoids the use of energy hungry components such as digital-to-analog converters (DACs). Secondly, a novel RF precoder for the design of the transmit signals is developed such that the performance of the CR system is optimized and the constraints related to both the system’s architecture and the employed underlay CR paradigm are satisfied. The corresponding nonconvex and difficult optimization problem is formulated and solved via a novel algorithmic solution based on the saddle point method. The convergence of the proposed algorithmic solution is theoretically studied. The proposed approach is shown to be much more energy efficient than existing approaches based on fully digital transceivers.

Precoding Design for Multi-User MISO Systems With Delay-Constrained and -Tolerant Users

In both academia and industry, multi-user multiple-input single-output (MU-MISO) techniques have shown enormous gains in spectral efficiency by exploiting spatial degrees of freedom. So far, an underlying assumption in most of the existing MU-MISO design has been that all the users use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering delay-constrained users whose blocklength tends to be finite. In this paper, we consider a heterogeneous setting in MU-MISO systems where delay-constrained users and delay-tolerant users coexist, called a DCTU-MISO network. To maximize the sum spectral efficiency in this system, we present the spectral efficiency for delay-tolerant users and provide a lower bound of the spectral efficiency for delay-constrained users. We consider an optimization problem that maximizes the sum spectral efficiency of delay-tolerant users while satisfying the latency constraint of delay-constrained users, and propose a generalized power iteration (GPI) precoding algorithm that finds a principal precoding vector. Furthermore, we extend a DCTU-MISO network to the multiple time slots scenario and propose a recursive GPI precoding algorithm. In simulation results, we validate proposed methods outperform baseline schemes and present the effect of network parameters on the average sum spectral efficiency.

Robust Symbol-Level Precoding and Passive Beamforming for IRS-Aided Communications

This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consideration of bounded channel uncertainty. The joint beamforming design is formulated into a nonconvex worst-case robust programming to minimize the transmit power subject to single-to-noise ratio (SNR) requirements. To address the challenges due to the constant modulus and the coupling of the beamformers, we first study the single-user case. Specifically, we propose and compare two algorithms based on the semidefinite relaxation (SDR) and alternating optimization (AO) methods, respectively. It turns out that the AO-based algorithm has much lower computational complexity but with almost the same power to the SDR-based algorithm. Then, we apply the AO technique to the multiuser case and thereby develop an algorithm based on the proximal gradient descent (PGD) method. The algorithm can be generalized to the case of finite-resolution IRS and the scenario with direct links from the transmitter to the users. Numerical results show that the SLP can significantly improve the system performance. Meanwhile, 3-bit phase shifters can achieve near-optimal power performance.

Proximal Policy Optimization-Based Transmit Beamforming and Phase-Shift Design in an IRS-Aided ISAC System for the THz Band

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design.

Weighted Sum-Rate of Intelligent Reflecting Surface Aided Multiuser Downlink Transmission With Statistical CSI

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

Impact of Phase-Noise and Spatial Correlation on Double-RIS-Assisted Multiuser MISO Networks

We study the performance of a phase-noise impaired double reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input single-output (MISO) system under spatial correlation at both RISs and base-station (BS). The downlink achievable rate is derived in closed-form under maximum ratio transmission (MRT) precoding. In addition, we obtain the optimal phase-shift design at both RISs in closed-form for the considered channel and phase-noise models. Numerical results validate the analytical expressions, and highlight the effects of different system parameters on the achievable rate. Our analysis shows that phase-noise can severely degrade the performance when users do not have direct links to both RISs, and can only be served via the double-reflection link. Also, we show that high spatial correlation at RISs is essential for high achievable rates.

Dynamic-Controlled RIS Assisted Multi-User MISO Downlink System: Joint Beamforming Design

This paper investigates a multi-user multiple-input single-output (MISO) downlink communication system, which is assisted by a dynamic-controlled Reconfigurable Intelligent Surface (RIS). Firstly, we formulate a problem to optimize the active -and passive beamforming matrices with the goal of maximizing the sum rate (SR) of all users under the constraints of Quality of Service (QoS) requirements. A state selection matrix is creatively introduced to control the number of RIS elements used for reflection. And quantized phases are considered in order to be more practical, which is often neglected in existing works. Then, an algorithm based on block coordinate descent (BCD) is proposed to obtain the stationary solution. The algorithm can guarantee the optimal SR regardless of any proportion of closed RIS elements. Finally, we perform simulation verification and analysis the results of the proposed scheme and six baseline schemes. Simulation results show that the proposed scheme is much better than the existing schemes in terms of SR, and realizes good energy efficiency (EE) performance simultaneously. Moreover, the proposed scheme can achieve more significant EE gain by sacrificing less SR.

Energy‐efficient alternating current computing‐enabled receiver design for K‐user interference channel simultaneous wireless information and power transfer networks

SummaryEnergy efficiency is one of the key challenges in fifth generation (5G) and beyond wireless communication systems. Recently, simultaneous wireless information and power transfer (SWIPT) systems are usually adopted to enhance energy efficiency. The harvested energy through power transfer is first rectified to generate the direct current (DC) voltage that is used to power the communication devices. This rectification reduces the energy efficiency due to losses in conversion to DC voltages. In this work, we consider integrated information decoding and energy harvesting receiver architecture through power splitting SWIPT network that enables alternating current (AC) computational logic. The AC computing method is used to supply the computing block of receivers to further enhance the energy efficiency. Multiuser multiple input single output (MISO) interference channel (IC) is considered, where receivers are required to compromise between quality of service (QoS) and energy consumption. The system sum rate is maximized under sum harvested energy and total transmit power constraints through joint beamforming and power splitting factors design. For the problem tractability in the proposed AC computing‐enabled receiver, standard beamforming schemes are deployed, namely, zero forcing (ZF), regularized zero forcing (RZF), maximum ratio transmission (MRT), and a hybrid scheme between MRT and ZF beamforming (MRT‐ZF). Moreover, the power splitting ratios design problem is transformed into a one dimension search problem. Simulation results show that different beamforming designs exhibit a great trade‐off between the achieved rate and the harvested energy.

Joint waveform and precoding design for coexistence of MIMO radar and MU‐MISO communication

The joint design problem for the coexistence of multiple-input multiple-output (MIMO) radar and multi-user multiple-input-single-output (MU-MISO) communication is investigated. Different from the conventional design schemes, which require defining the primary function, we consider designing the transmit waveform, precoding matrix and receive filter to maximize the radar SINR and the minimal SINR of communication users, simultaneously. By doing so, the promising overall performance for both sensing and communication is achieved without requiring parameter tuning for the threshold of communication or radar. However, the resulting optimization problem which contains the maximin objective function and the unit sphere constraint, is highly nonconvex and hence difficult to attain the optimal solution directly. Towards this end, the epigraph-form reformulation is first adopted, and then an alternating maximisation (AM) method is devised, in which the Dinkelbach’s algorithm is used to tackle the nonconvex fractional-programing subproblem. Simulation results indicate that the proposed method can achieve improved performance compared with the benchmarks.

Joint Coordinated Beamforming and Power Splitting Ratio Optimization in MU-MISO SWIPT-Enabled HetNets: A Multi-Agent DDQN-Based Approach

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically.

Average Rate Approximation and Maximization for RIS-Assisted Multi-User MISO System

In this letter, we investigate a reconfigurable intelligent surface (RIS) assisted downlink multi-user multiple-input single-output (MISO) system. We derive the approximations of average rate of user equipments (UEs) under proper assumptions, and reveal the relations between average rates and system parameters. Furthermore, a RIS configuration algorithm is proposed to improve the average sum rate with low complexity by exploiting the approximation of average rate and statistical channel state information (CSI). The rationality of the assumptions, the tightness of the approximations and the effectiveness of the proposed algorithm are validated by numerical results.

Sum-Rate Analysis of a Multi-Cell Multi-User MISO System Under Double Scattering Channels

This paper aims to derive expressions of the downlink ergodic user rates in a multi-cell large-scale multi-user multiple-input single-output (MISO) system under the assumption that each base station (BS) employs maximum ratio transmission (MRT) precoding and that single-antenna users in each cell are divided into groups, where channels of users in the same group share common covariance matrices and follow the double scattering channel model. Moreover, both channel estimation errors and pilot contamination effects caused by re-use of pilot sequences in neighboring cells are taken into consideration in this work. The analysis is carried out using statistical tools under the exact and asymptotic regimes in which the number of antennas at BS N, the number of users in each cell K, and the number of scatterers S grow large at the same pace. Furthermore, the obtained exact expressions and deterministic approximations of the ergodic rates are expressed in simplified closed-forms under the special case of multi-keyhole channels to yield useful insights. They reveal that signal-to-noise plus interference ratio (SINR) without user grouping in a multi-keyhole channel is similar to that under standard Rayleigh channel in the asymptotic regime. However, under user grouping, we show that the massive multiple-input multiple-output (MIMO) gains promised by deploying large-scale antenna arrays in multi-cell settings are limited by the number of scatterers even if the number of antennas grows large. Simulation results illustrate the close match provided by the asymptotic analysis for moderate system dimensions and confirm the insights drawn from the theoretical findings.

Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Channel estimation in the RIS-aided massive multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive feature of RIS and the large number of reflecting elements that incur high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave massive MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users may not change in several or even tens of consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much less pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the partial CSI of the common base station (BS)-RIS channel obtained in Phase I. The total theoretical minimum pilot overhead in the first coherence block is $8J-2+(K-1)\left\lceil (8J-2)/L\right\rceil $, where $K$, $L$ and $J$ denote the numbers of users, paths in the BS-RIS channel and paths in the RIS-user channel, respectively. In each of the remaining coherence blocks, the minimum pilot overhead is $JK$. Moreover, the training phase shift matrices at the RIS are optimized to improve the estimation performance.

Cooperative Rate-Splitting for Downlink Multiuser MISO Systems With Partial CSIT

Multiple antenna technology and cooperative relaying are two efficient methods to improve the capacity and reliability of wireless networks. By marrying cooperative relaying with a Rate-Splitting (RS) approach, Cooperative Rate-Splitting (CRS) shows certain benefits compared to several non-cooperative and cooperative schemes in multi-antenna Broadcast Channel (BC). In this paper, we further investigate the CRS with partial Channel State Information at the Transmitter (CSIT), as partial CSIT is an inevitable issue in practical downlink Multi-user Multiple Input Single Output (MU-MISO) systems. The proposed CRS structure includes multiple users, and one user could be selected as a relay to help the other users. With the flexible design, CRS leverages the benefits of user cooperation and relay (or user) selection for cooperative network, and also retains the superiority of RS for multi-antenna BC. Considering the Weighted Average Sum Rate (WASR) performance, we aim to solve the stochastic optimization that jointly involves the precoder design, the time resource allocation, and the relay selection issue. To address the above non-convex problem, a dedicated algorithm for CRS that exploits the Weighted Minimum Mean Square Error (WMMSE) approach and the Convex-Concave Procedure (CCP) is proposed, such that the WASR is maximized by jointly updating the precoding matrix and time allocation factor according to the incoming user channel state estimate. The proposed algorithm then enables us to solve the relay selection through an exhaustive search. Since the optimal relay selection algorithm has a high computational complexity when the number of users is large, we additionally provide a suboptimal heuristic algorithm to strike a balance between complexity and performance. Numerical results demonstrate that the proposed CRS scheme is capable of achieving a larger WASR than its non-cooperative counterpart and several existing beamforming schemes in a wide range of propagation conditions, and the heuristic algorithm achieves a performance close to the optimal one but with lower complexity.

On the Performance of Fully-Connected and Sub-Connected Hybrid Beamforming System

When the wireless communication system evolves to massive multiple-input multiple-output (MIMO) system, the system becomes more and more complex. Hybrid beamforming architecture provides excellent trade-off between the system performance and the complexity for the massive MIMO system. Hybrid beamforming architecture can be classified as fully-connected (FC) and sub-connected (SC) architecture. Existing studies on the system performance comparison of the two architectures are limited. In this paper, we investigate the performance of the FC and SC architecture in the multi-user multiple-input single-output (MU-MISO) system. We derive the closed-form sum rate of two architectures under the same total transmission power constraint. From the results, we conclude that the system performance of FC architecture is always better than that of SC architecture. When the number of users is small or the signal noise ratio (SNR) is low, the performance difference between the FC and SC architecture is small. Our analysis can serve as a guideline of choosing hybrid beamforming architectures.

Robust and Secure Sum-Rate Maximization for Multiuser MISO Downlink Systems With Self-Sustainable IRS

This paper investigates robust and secure multiuser multiple-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can simultaneously reflect and harvest energy from the received signals. We study the joint design of beamformers at an access point (AP) and the phase shifts as well as the energy harvesting schedule at the IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the wireless energy harvesting capability of IRS elements, secure communications, and the robustness against the impact of channel state information (CSI) imperfection. Subsequently, we propose a computationally-efficient iterative algorithm to obtain a suboptimal solution to the design problem. In each iteration, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {S}$ </tex-math></inline-formula> -procedure and the successive convex approximation are adopted to handle the intermediate optimization problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and the self-sustainability of the IRS; 2) the performance gain achieved by the proposed scheme is saturated with a large number of energy harvesting IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sum-rate of the ideal case with continuous phase shifts.

Construction of 1-Bit Transmit Signal Vectors for Downlink MU-MISO Systems: QAM Constellations

In this paper, we investigate the construction of a transmit signal for downlink multiuser multiple-input single-output (MU-MISO) systems under the cost-effective 1-bit digital-to-analog converters (DACs). Due to the coarse nonliner property, conventional precoding methods, developed under infinite-resolution DACs, could not yield an attractive performance having a severe error-floor. Finding an optimal transmit signal, moreover, is computationally implausible due to its combinatorial nature. This challenging problem has been partially solved in our earlier work focusing on phase-shift-keying constellations. Our major contribution is to extend it into more practical quadrature-amplitude-modulation constellations in a non-trivial way. We first derive the so-called feasibility condition to guarantee that every user's noiseless received signal belongs to a desired decision region, and then transform it as linear constraints. Taking the robustness to an additive noise into account, the proposed construction method is formulated as a mixed integer linear programming problem. We construct an efficient algorithm to solve it (equivalently, to generate an 1-bit transmit signal). Via simulation results, we demonstrate the superiority of the proposed method with respect to a computational complexity and detection performance.

RIS-Assisted Multi-User MISO Communications Exploiting Statistical CSI

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users.