Multiuser Multi-input Single-output Research Articles

Sum‐rate maximization for downlink multiuser MISO URLLC system aided by IRS with discrete phase shifters

AbstractIntelligent reflecting surface (IRS) has recently been considered as a potential technology for realizing ultra‐reliable and low‐latency (URLLC) in wireless networks. This paper proposes a resource optimization scheme to maximize the sum‐rate for an IRS‐assisted downlink multiuser multi‐input single‐output (MISO) URLLC system. For the perfect CSI scenario, we jointly optimize each user's block‐length and packet‐error probability, the precoding vectors at the base station (BS), and the passive beamforming with discrete phase shifts at the IRS. Given the problem's complexity, we design a computationally efficient iterative algorithm using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to obtain a locally optimal solution. Specifically, for the imperfect CSI scenario, we construct a robust resource optimization problem model and incorporate the S‐procedure to address the impact of channel uncertainty, proposing an iterative algorithm based on the alternating optimization (AO) method to achieve a locally optimal solution. Simulation results demonstrate that: 1) An IRS equipped with a 2‐bit quantized resolution phase shifter is sufficient to achieve a system sum‐rate comparable to that of an ideal phase shifter; 2) Compared to other Baseline schemes, Algorithm 2 exhibits better robustness and superior performance gains under imperfect CSI.

Joint Secure Transmit Beamforming Designs for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC), which allows individual radar and communication systems to share the same spectrum bands, is an emerging and promising technique for alleviating spectrum congestion problems. In this paper, we investigate how to exploit the inherent interference from strong radar signals to ensure the physical layer security (PLS) for the considered multi-user multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence system. In particular, with known eavesdroppers' channel state information (CSI), we propose to jointly design the transmit beamformers of communication and radar systems to minimize the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while guaranteeing the communication quality-of-service (QoS) of legitimate transmissions, the requirement of radar detection performance, and the transmit power constraints of both radar and communication systems. When eavesdroppers' CSI is unavailable, we develop a joint artificial noise (AN)-aided transmit beamforming design scheme, which utilizes residual available power to generate AN for disrupting malicious receptions as well as satisfying the requirements of both legitimate transmissions and radar target detection. Extensive simulations verify the advantages of the proposed joint beamforming designs for ISAC systems on secure transmissions and the effectiveness of the developed algorithms.

Joint Beamforming Design for Intelligent Omni Surface Assisted Wireless Communication Systems

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.

Joint Transmit Waveform and Passive Beamforming Design for RIS-Aided DFRC Systems

Reconfigurable intelligent surface (RIS) is a promising technology for 6 G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) systems for improving both radar sensing and communication functionalities. In particular, we consider a RIS-assisted DFRC system in which the multi-antenna base station (BS) simultaneously performs both multi-input multi-output (MIMO) radar sensing and multi-user multi-input single-output (MU-MISO) communications using the same hardware platform. We aim to jointly design the dual-functional transmit waveform and the passive beamforming of RIS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) achieved by space-time adaptive processing (STAP), while satisfying the communication quality-of-service (QoS) requirement under one of three metrics, the constant-modulus constraint on the transmit waveform, and the unit-modulus constraint of RIS reflecting coefficients. An efficient algorithm framework based on the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods is developed to solve the complicated non-convex optimization problem. Simulation results verify the advancement of the proposed RIS-assisted DRFC scheme and the effectiveness of the developed ADMM-MM-based joint transmit waveform and passive beamforming design algorithm.

Rank-Two Multicast Beamforming for Multiuser MISO Downlink With Intelligent Reflecting Surface

Recently, intelligent reflecting surface (IRS) has received considerable attention and is seen as a promising technology for 6 G communications. This correspondence studies the IRS-aided multiuser multi-input single-output (MISO) downlink multicast beamforming. Depart from the conventional (rank-one) beamforming, the Alamouti-aided <i>rank-two</i> beamforming is employed at the base station (BS), and a joint optimization of the rank-two beamforming and the IRS phase shift is formulated to minimize the transmit power with guaranteed signal-to-noise ratio at the users. This power minimization problem is nonconvex. To tackle it, we custom-derive a proximal distance algorithm (PDA) with generalized power method (GPM) to iteratively optimize the beamformer and the phase shift. Both PDA and GPM can be performed very efficiently than the semidefinite relaxation method. Simulation results demonstrate that the rank-two beamforming attains much lower porwer than the rank-one beamforming with reduced computation complexity.

Deep Learning Based Efficient Symbol-Level Precoding Design for MU-MISO Systems

The recently emerged symbol-level precoding (SLP) technique has been regarded as a promising solution in multi-user wireless communication systems, since it can convert harmful multi-user interference (MUI) into beneficial signals for enhancing system performance. However, the tremendous computational complexity of conventional symbol-level precoding designs severely hinders the practical implementations. In order to tackle this difficulty, we propose a novel deep learning (DL) based approach to efficiently design the symbol-level precoders. Particularly, in this correspondence, we consider a multi-user multi-input single-output (MU-MISO) downlink system. An efficient precoding neural network (EPNN) is introduced to optimize the symbol-level precoders for maximizing the minimum quality-of-service (QoS) of all users under the power constraint. Simulation results demonstrate that the proposed EPNN based SLP design can dramatically reduce the computing time at the price of slight performance loss compared with the conventional convex optimization based SLP design.

Space-Time Constellation for MU-MISO Dimmable Visible Light Communications

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

Intelligent Reflecting Surface Enhanced Wideband MIMO-OFDM Communications: From Practical Model to Reflection Optimization

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.

Constant Envelope Precoding With Extended Degrees of Freedom Through Per-User Symbol Scaling

In this letter, we propose a novel constant envelope precoding (CEP) design for multi-user multi-input single-output (MU-MISO) downlink, where we exploit the extra degrees of freedom (DoFs) by allowing individual rotation and scaling for each constellation point. By jointly optimizing the transmitting signal matrix and the complex scaling factors, the signal-to-interference-and-noise-ratio (SINR) at the receiver is maximized. The formulated optimization problem is divided into two sub-problems and solved by the alternating optimization approach. We further propose a simplified gradient descent algorithm and extend our algorithm to frame-level precoding design. Simulation results show that the proposed algorithms outperform the existing CEP algorithms in terms of both symbol error rate (SER) and computational complexity.

Reconfigurable Intelligent Surface Enhanced Multi-User MISO Symbiotic Radio System

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.

Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

Interference Exploitation Precoding for Intelligent Reflecting Surface Aided Communication System

In this letter, we propose a novel precoding design for a multi-user multi-input-single-output (MU-MISO) communication system with the assistance of an intelligent reflecting surface (IRS). By jointly designing the precoder at the base station (BS) and the phase shift matrix at the IRS, we exploit the constructive effect of the multi-user interference (MUI) to improve the performance of the communication system. While the objective function and constraints of the problem are both non-convex, an alternating optimization (AO) based algorithm with two different solvers is proposed. This algorithm is guaranteed to converge to at least locally optimal solution. Simulations show that by applying the proposed symbol level precoding technique, the communication performance can be significantly enhanced compared with the existing linear precoding method, at the price of slightly increased computational overhead.

Channel estimation for intelligent reflecting surface aided multi-user MISO terahertz system

Intelligent reflecting surface (IRS) is considered as a promising application in terahertz (THz) communications since it is able to enhance the THz communication with no additional power consumptions. In this letter, we consider the channel estimation problem for an IRS-aided THz multi-user multi-input single-output (MISO) system with lens antenna array. The main challenge of the problem is that we need to estimate multiple channels and some of the channels are cascaded. To deal with the problem, we propose a two-stage channel estimation scheme, where we set different IRS modes to estimate different channels for each stage. In stage 1, we set the IRS to an absorbing mode and estimate the channel without IRS. Removing the influence of the prior estimated channel, in stage 2, we estimate the channel with IRS by setting the IRS to a perfect reflecting mode. And we decompose the total channel estimation problem into a series of independent problems, where we estimate each independent channel component with a least square method.

Secure Symbol-Level Precoding in MU-MISO Wiretap Systems

Multi-user interference (MUI) is usually considered to be a harmful component in wireless communications. However, recently emerged symbol-level precoding techniques can constructively exploit the MUI by transforming it into useful signal at the receiver and contribute to symbol detection. This paper adopts this concept and aims to investigate the exploitation of the MUI to enhance both physical layer security against eavesdropping and the quality of legitimate transmissions. Particularly, we consider the problem of secure symbol-level precoding in multi-user multi-input single-output (MU-MISO) wiretap systems. Our goal is to design the symbol-level precoder to guarantee the quality of service (QoS) of all legitimate transmissions as well as ensure security against eavesdropping. The symbol-level precoder algorithms for physical layer security are developed under different assumptions about the availability of channel state information (CSI) of the legitimate and eavesdropping channels. Extensive simulation results validate the exploitation of MUI for security and illustrate the effectiveness of our proposed secure symbol-level precoding algorithms.

One-Bit Symbol-Level Precoding for MU-MISO Downlink With Intelligent Reflecting Surface

This paper considers symbol-level precoding (SLP) for multiuser multi-input single-output (MISO) downlink transmission with the aid of intelligent reflecting surface (IRS). Specifically, by assuming one-bit transmitted signals at the base station (BS), which arises from the use of low-resolution DACs in the regime of massive transmit antennas, a joint design of one-bit SLP at the BS and the phase shifts at the IRS is proposed with a goal of minimizing the worst-case symbol error probability (SEP) of the users under the PSK modulation. This joint design problem is essentially a mixed integer nonlinear program (MINLP). To tackle it, we alternately optimize the one-bit signal and the phase shifts. For the former, a dual of the relaxed one-bit SLP problem is solved by the mirror descent (MD) method with the maximum block improvement (MBI) heuristics. For the latter, the accelerated projected gradient (APG) method is employed to optimize the phases. Numerical results demonstrate that the proposed joint design can attain better SEP performance than the conventional linear precoding and one-bit SLP.

Symbol-Level Hybrid Precoding in mmWave Multiuser MISO Systems

Recently, symbol-level precoding has emerged as a novel efficient solution in multi-user multi-antenna downlink systems, since it can exploit multi-user interference (MUI) and transform it into useful signal. In this correspondence, we attempt to apply symbol-level precoding into the well-known hybrid precoding architecture in multi-user multi-input single-output (MU-MISO) millimeter wave (mmWave) communication systems. Particularly, we aim to design the symbol-varying digital precoder and the analog precoder with low-resolution phase shifters (PSs) to optimize symbol detection performance under the transmit power constraint and the discrete phase constraint. To handle this difficult joint optimization problem, we propose to minimize the Euclidean distance between the optimal fully-digital precoder and the hybrid precoder. An efficient iterative algorithm is developed to solve this problem. Simulation results demonstrate the advantages of symbol-level precoding and validate the effectiveness of our proposed symbol-level hybrid precoding algorithm.

Joint power allocation and power splitting for MISO SWIPT RSMA systems with energy-constrained users

Simultaneous wireless information and power transfer (SWIPT) has been widely used in multi-input single-output (MISO) systems to transmit information and energy simultaneously from the base station (BS) towards the users. The traditional approach used in the literature is based on space-division multiple access (SDMA) method by using the multi-user linear precoding technique, where the information decoding is done by considering the multi-user interference as noise. Recently, the novel rate-splitting multiple access (RSMA) method, based on partially decode the multi-user interference and partially treat that interference as noise, has been shown to outperform the SDMA method in multi-user MISO systems. Motivated by the superior performance of RSMA, we consider a multi-user MISO SWIPT system applying the RSMA method, where multiple energy-constrained users are equipped with a power-splitting structure to harvest energy and decode information simultaneously. In particular, we investigate the optimal precoders and power-splitting ratios design to minimize the total transmit power at the BS, subject to constraints of the minimum data rate for users, minimum energy harvesting by users, and maximum power at the BS. The proposed solution for the formulated non-convex problem is based on two phases. First, we convert the non-convex problem into bilevel programming where the upper optimization problem is solved by using particle swarm optimization. Second, we propose two algorithms to solve the inner optimization problem based on a semidefinite relaxation method or a successive interference cancellation method. Numerical results show that RSMA achieves significant improvement over SDMA in reducing the total transmit power.

On the duality between array beam-formers and MU-MISO precoders

Multi-user transmission, whereby users share time and frequency resources has been an active research area in the past decade in which the uniform linear array (ULA) was proposed and implemented for its model simplicity and easy implementation. This paper studies the performance of different methods used in ULA environment and their implication to multiuser multi-input single-output (MU-MISO) scenario where the channel is considered AWGN and Rayleigh fading (slow flat fading channel). The noise power level and the effect of multipath are investigated based on the assumption that the paths have the same loss exponent and the same length (i.e. the power of the received signal are the same from different paths). The results show that the best method dependent on numerous variables, the most important being the signal to noise ratio (SNR) and the number of multipath received from each source. The implementation of these methods in MU-MISO environment reveals that they can be successfully adopted for MU-MISO. It is observed that any method applicable to ULA scenario, is also applicable to MU-MISO scenario.

Constellation-Optimal Beamformers for Multiuser MISO Broadcast Visible Light Communications

In this paper, we design energy-efficient space constellations for multiuser multi-input single-output visible light communication broadcast systems with channel state information (CSI) at the receiver in the three scenarios: 1) with CSI at the transmitter (CSIT); 2) with statistical CSIT; and 3) without CSIT. By utilizing the cooperation of multiuser interference, optimal multidimensional additively uniquely decomposable constellation groups are designed to optimize the received worst case performance metrics of all users subject to two commonly used power constraints, i.e., average and peak optical power constraints. The resulting optimal designs are proved to be pulse amplitude modulated with an optimal beamformer, which can be efficiently attained by numerical search for Scenarios 1 and 2 and be given in a closed form for Scenario 3. One of the common significant advantages of these designs is the fast demodulation of the sum signal from a noisy received signal, since these optimal designs result in an equivalent ideal scalar AWGN channel. Comprehensive computer simulations demonstrate that our designs significantly outperform traditional zero-forcing, minimum mean square error, and time-division methods.

Downlink Beamforming for SWIPT Multi-User MISO Underlay Cognitive Radio Networks

This letter investigates downlink beamforming for simultaneous wireless information and power transfer in multi-user multi-input single-output underlay cognitive radio networks. Under the assumption of the integrated power splitting (PS) receiver architecture at the secondary receivers, we aim to minimize the total transmission power from the secondary base station by jointly optimizing the beamforming vectors and the PS ratios. We first exploit the structure of the relaxed problem to provide a closed form expression for the optimal solution. Since the underlying optimization problem is non-convex, we resort to semidefinite programming relaxation and we analytically prove that the relaxation is in fact tight and yields optimal beamforming solution.