Joint Beamforming Design Research Articles

Hybrid Relay and Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems

Reconfigurable intelligent surfaces (RISs) are viewed as key enablers for next-generation wireless communications. This paper investigates a multiuser downlink multiple-input single-output (MISO) system in which a multiantenna base station (BS) transmits information to multiple single-antenna users with the aid of both a half-duplex decode-and-forward (DF) relay and a full-duplex RIS. Active beamforming at the BS and the DF relay, as well as passive beamforming at the RIS, are jointly designed for system sum-rate maximization. The design problem is challenging to solve due to coupled beamforming variables. An alternating optimization (AO) based algorithm is proposed to tackle this complex co-design problem. Numerical results demonstrate the superior performance of the proposed hybrid relay–RIS system with a judicious joint beamforming design. Convergence and complexity analysis shows that the convergence rate of the proposed algorithm is dominated by the numbers of users and RIS elements, and the proposed scheme can converge in a few iterations even in the configuration of large numbers of users and RIS elements. Interesting tradeoffs posed in the joint design are discussed. An extension of the proposed design method to a related energy efficiency (EE) optimization problem is also outlined and implemented.

Research on Joint Beamforming of High-Speed Railway Millimeter-wave MIMO Communication with Reconfigurable Intelligent Surface

The advantage of a reconfigurable intelligent surface (RIS) is that it optimizes the wireless signal transmission environment. It can ameliorate the high loss of high-speed railway (HSR) millimeter-wave (mmWave) communications and increase signal strength by coherently superimposing signals at the receiving end through an appropriate beamforming design. In this paper, an mmWave multiple-input multiple-output communication system for HSR is discussed. The channel modeling takes into account the Doppler effect and the properties of mmWave channels. Aiming at the problem of high coupling of optimization variables, the singular value decomposition method is utilized to decouple them and to achieve the optimal active beamforming design. The original optimization problem is then rewritten into a more manageable form through a sequence of transformations. In order to overcome the restrictions imposed by non-convex constraints, the optimal RIS phase shift is directly calculated using the Riemann gradient descent (RGD) method. In this paper, spectral efficiency (SE) is taken as the optimization objective. Considering the fast time-varying environment of HSR, a step size selection scheme with easier convergence is adopted in the RGD method to complete the joint beamforming design. The numerical simulations demonstrate that the proposed solution outperforms the semi-definite relaxation scheme and the RIS-free auxiliary scheme in terms of SE performance and computational complexity.

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning

In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods’ superiority.

Simultaneously Transmitting and Reflecting (STAR) RIS Assisted Over-the-Air Computation Systems

The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs). However, the conventional RISs require that the WDs have to be located in the half-plane of the reflection space, which restricts their potential benefits. To address this issue, the novel family of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is considered in AirComp systems to improve the computation accuracy across a wide coverage area. To minimize the computation mean-squared-error (MSE) in STAR-RIS assisted AirComp systems, we propose a joint beamforming design for optimizing both the transmit power at the WDs, as well as the passive reflect and transmit beamforming matrices at the STAR-RIS, and the receive beamforming vector at the fusion center (FC). Specifically, in the updates of the passive reflect and transmit beamforming matrices, closed-form solutions are derived by introducing an auxiliary variable and exploiting the coupled binary phase-shift conditions. Moreover, by assuming that the number of antennas at the FC and that of elements at the STAR-RIS/RIS are sufficiently high, we theoretically prove that the STAR-RIS assisted AirComp systems provide higher computation accuracy than the conventional RIS assisted systems. Our numerical results show that the proposed beamforming design outperforms the benchmark schemes relying on random phase-shift constraints and the deployment of conventional RIS. Moreover, its performance is close to the lower bound achieved by the beamforming design based on the STAR-RIS dispensing with coupled phase-shift constraints.

Energy-Efficient Beamforming Design for User-Centric Networks With Full-Duplex Wireless Fronthaul

In this paper, we investigate the joint beamforming design for access and fronthaul links in a user-centric network with full-duplex fronthaul. We formulate an optimization problem to maximize the network energy efficiency while guaranteeing fronthaul rate requirements. Particularly, the power consumed by self-interference cancellation at full-duplex access points (APs) is taken into consideration to comprehensively reflect the effect of full-duplex technology. Due to the non-convexity and the nonlinear fractional form of the formulated problem, we resort to the successive convex approximation method. The original problem is approximated as a convex second-order cone program. We then extend the study to the scenario of imperfect and partial channel state information (CSI). To avoid the intractable expression of the achievable rate in this case, we derive its lower bound for further beamforming design. Simulation results demonstrate the superiority of the proposed algorithm in terms of energy efficiency and power consumption under both CSI scenarios. For our considered full-duplex fronthaul networks, moderate maximum transmit power of macro base station (MBS) and APs is sufficient to obtain desired energy efficiency performance. Moreover, the lower bound is verified to be tight at the low-maximum power region.

Simultaneous Transmission and Reflection Beamforming Design for RIS-Aided MU-MISO

In this paper, we study the beamforming design for the simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted MU-MISO system, where the energy splitting (ES) mode is adopted for STAR-RIS. We aim to jointly design the beamforming strategy at the BS and the transmission-reflection coefficients (TRCs) at the STAR-RIS to minimize the total downlink transmit power of the base station (BS) subject to each user's SINR constraint. The formulated optimization problem is difficult to solve directly, and therefore we employ the iterative alternating optimization (AO) framework to obtain suboptimal solutions with promising performance. Specifically, we propose an AO-based solution to optimize each amplitude and phase of the TRCs at the STAR-RIS separately in a sequential manner, where two distinct approaches are introduced for the design of the amplitudes of STAR-RIS elements. Simulation results show that the proposed joint beamforming design at BS and passive design at STAR-RIS achieves a promising performance, and requires fewer iterations compared with the state-of-the-art.

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.

Proportional fairness secrecy beamforming for massive MIMO-SWIPT systems with low-resolution ADCs

Simultaneous wireless information and power transfer (SWIPT) technology is one of the solutions to the energy shortage problem in Massive multiple-input and multiple-output (MIMO) systems, while the low-resolution analog-to-digital convertor (ADC) is a potential way to significantly reduce the power consumption of radio frequency circuits in Massive MIMO enabled SWIPT systems. In this paper, we focus on the secrecy transmission maximization problem for Massive MIMO-SWIPT systems over Nakagami-m fading channels, where a low-resolution ADC quantization model and a non-linear energy harvester are exploited. By considering proportional fairness, a joint beamforming design and power allocation problem are proposed to achieve the sum logarithmic secrecy rates. To tackle the non-convex maximization problem, the suboptimal solutions of the beamforming design and power allocation are respectively obtained based on a successive convex approximation and a semidefinite relaxation. Numerical results demonstrate the effectiveness of the proposed joint optimization scheme.

Information and sensing beamforming optimization for multi-user multi-target MIMO ISAC systems

Integrated sensing and communication (ISAC) has been envisioned as a key enabler in the next-generation wireless networks. In this paper, we consider the joint information and sensing beamforming design in a multi-user and multi-target multi-input multi-output ISAC system, where a transmit BS and a sensing BS collaborate to sense targets, and the transmit BS sends information streams to communication users at the same time. To optimize the sensing performance and guarantee the communication throughput, we formulate a joint beamforming design problem to minimize the trace of the weighted Cramer–Rao bound of target parameters subject to the sum-rate constraint. The problem is challenging to solve due to the intricate non-convex objective function and constraints. We firstly exploit the weighted mean square error minimization (WMMSE) and semidefinite relaxation (SDR) techniques to devise a WMMSE–SDR algorithm that can achieve a KKT point of the problem. The SDR can be shown to be tight for a subproblem in the WMMSE–SDR algorithm, which implies zero duality for the subproblem. Based on this property and fractional programming techniques, we further reformulate the beamforming problem as a min–max form with simple constraints which then can be efficiently solved by first-order min–max optimization algorithms. Finally, the proposed algorithms are evaluated extensively in simulations. Numerical results show that both proposed algorithms can achieve promising performance in sensing and communication, and the low-complexity algorithm has a significantly reduced computation time.

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is dedicatedly deployed to assist an access point (AP) to sense a target at its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. We consider two types of target models, namely the point and extended targets, for which the AP aims to estimate the target's direction-of-arrival (DoA) and the target response matrix with respect to the IRS, respectively, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) on the estimation error. Towards this end, we first obtain the CRB expressions for the two target models in closed form. It is shown that in the point target scenario, the CRB for estimating the DoA depends on both the transmit and reflective beamformers; while in the extended target scenario, the CRB for estimating the target response matrix only depends on the transmit beamformers. Next, for the point target scenario, we optimize the joint beamforming design to minimize the CRB, via alternating optimization, semi-definite relaxation, and successive convex approximation. For the extended target scenario, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form. Finally, numerical results show that for both cases, the proposed designs based on CRB minimization achieve improved sensing performance in terms of mean squared error, as compared to other traditional schemes.

Rate-Splitting Multiple Access for Satellite-Terrestrial Integrated Networks: Benefits of Coordination and Cooperation

This paper investigates the joint beamforming design problem to achieve max-min rate fairness in a satellite-terrestrial integrated network (STIN) where the satellite provides wide coverage to multibeam multicast satellite users (SUs), and the terrestrial base station (BS) serves multiple cellular users (CUs) in a densely populated area. Both the satellite and BS operate in the same frequency band. Since rate-splitting multiple access (RSMA) has recently emerged as a promising strategy for non-orthogonal transmission and robust interference management in multi-antenna wireless networks, we present two RSMA-based STIN schemes, namely the coordinated scheme relying on channel state information (CSI) sharing and the cooperative scheme relying on CSI and data sharing. Our objective is to maximize the minimum fairness rate amongst all SUs and CUs subject to transmit power constraints at the satellite and the BS. A joint beamforming algorithm is proposed to reformulate the original problem into an approximately equivalent convex one, which can be iteratively solved. Moreover, an expectation-based robust joint beamforming algorithm is proposed against the practical environment when the satellite channel phase uncertainties are considered. Simulation results demonstrate the effectiveness and robustness of our proposed RSMA schemes for STIN and exhibit significant performance gains compared with various baseline strategies.

Joint Beamforming Design for Integrated Passive Sensing and Communications in V2I Networks

Joint Beamforming Design for Integrated Passive Sensing and Communications in V2I Networks

Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) has been envisioned as a promising technology to tackle the spectrum congestion problem for future networks. In this correspondence, we investigate to deploy a reconfigurable intelligent surface (RIS) in an ISAC system for achieving better performance. In particular, a multi-antenna base station (BS) simultaneously serves multiple single-antenna users with the assistance of a RIS and detects potential targets. The active beamforming of the BS and the passive beamforming of the RIS are jointly optimized to maximize the achievable sum-rate of the communication users while satisfying the constraint of beampattern similarity for radar sensing, the restriction of the RIS, and the transmit power budget. An efficient alternating algorithm based on the fractional programming (FP), majorization-minimization (MM), and manifold optimization methods is developed to convert the resulting non-convex optimization problem into two solvable sub-problems and iteratively solve them. Simulation studies illustrate the advancement of deploying RIS in ISAC systems and the effectiveness of the proposed algorithm.

Joint beamforming design and resource allocation for double-IRS-assisted RSMA SWIPT systems

Being able to achieve high passive beamforming gains cost-effectively, intelligent reflecting surface (IRS) is envisioned as a promising technique in assisting simultaneous wireless information and power transfer (SWIPT). Different from existing works that consider deploying a single IRS or applying conventional multiple access schemes like space division multiple access (SDMA) and non-orthogonal multiple access (NOMA), we consider a double-IRS-assisted multi-user SWIPT system applying the rate-splitting multiple access (RSMA) scheme in this paper. To maximize the minimum achievable rate of all users and guarantee their minimum energy harvesting amount, we jointly optimize the transmit beamforming for both the common and private messages at the access point of the system, the reflecting beamforming of the two IRSs, as well as the power splitting coefficients and the individual common rates of all users. Although the considered optimization problem is difficult to solve due to its coupling variables and non-convex structure, we propose an efficient alternative optimization-based algorithm to obtain a high-quality solution to it. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superior performance of deploying two IRSs and applying RSMA, as compared to the baseline schemes that deploy a single IRS or use SDMA/NOMA.

Joint Beamforming Design for Multiuser MISO Downlink Aided by a Reconfigurable Intelligent Surface and a Relay

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention due to their ability to direct electromagnetic waves into desirable directions. Although RISs share some similarities with relays, the two have fundamental differences impacting their performance. To harness the benefits of both, we propose a downlink system wherein a relay and an RIS improve performance in terms of energy-efficiency. Using singular value decomposition (SVD), semidefinite programming (SDP), and function approximations, we propose different solutions for optimizing the beamforming matrices at the base-station (BS), the relay, and the phase shifts at the RIS to minimize the total power under quality-of-service (QoS) constraints. The problem is solved when the relay operates in half-duplex and full-duplex modes and when the reflecting elements have continuous and discrete phase shifts. Simulation results compare the performance of the system with and without the RIS or the relay, under different optimization solutions. The results show that the system with full-duplex relay and RIS outperforms the other scenarios, and the contribution of full-duplex relay is higher than that of the RIS. However, an RIS outperforms a half-duplex relay when the required QoS is high. The results also show that increasing the number of reflecting elements improves the performance better in the presence of a relay than in its absence.

Active and Passive Beamforming Design for Reconfigurable Intelligent Surface Assisted CR-NOMA Networks

In this letter, a underlay cognitive radio inspired non-orthogonal multiple access (CR-NOMA) network assisted by the reconfigurable intelligent surface (RIS) technique is conceived to maximize the energy efficiency (EE), while all the cognitive users (CUs) are located in the “dead zone”. In particular, the CUs could only receive the information from the cognitive base station (CBS) via RIS. The EE maximization optimization problem which is a non-convex problem has been constructed to realize joint beamforming design at both the CBS and RIS with the constraints of the primary user’s (PU) interference power restriction and the CUs’ rate fairness. Moreover, we propose the alternating pragmatic iterative algorithm (APIA) to optimize the non-convex optimization problem until the final value of EE converges. Based on the simulation results, our proposed algorithm attains the significant gain than the two benchmarks of the random phase scheme as well as the fixed phase scheme on the RIS.

Joint Beamforming Design for IRS Aided Multiuser MIMO With Imperfect CSI

This paper investigates the joint beamforming design for the intelligent reflecting surface (IRS) aided multi-antenna multiuser (MU) multiple-input multiple-output (MIMO) downlink transmissions under imperfect channel state information (CSI), where the IRS can only take discrete PSs (PSs) at each element and the weighted sum rate (WSR) is adopted as the performance metric. By deriving the covariance matrix of the interference and noise at the users under the statistical channel estimation errors (CEEs) model, the joint WSR maximization problem is reformulated as an equivalent weighted minimum mean square error minimization problem and decoupled into two subproblems which are solved alternatively. In particular, two algorithms based on the majorization-minimization and gradient descent methods are proposed to obtain the optimal beamforming matrix at the IRS. Simulation results validate the accuracy of the proposed robust design and reveal that in case of imperfect CSI, the IRS can still achieve a significant improvement in WSR even with 2-bits PSs at the IRS. Moreover, it is also found that increasing the number of reflecting elements may not always be beneficial and that the optimal location of the IRS may be closer to the base station in the presence of CEEs.

Integrating Sensing, Computing, and Communication in 6G Wireless Networks: Design and Optimization

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

Refracting RIS-Aided Hybrid Satellite-Terrestrial Relay Networks: Joint Beamforming Design and Optimization

Reconfigurable intelligent surface (RIS) has been viewed as a promising solution in constructing reconfigurable radio environment of the propagation channel and boosting the received signal power by smartly coordinating the passive elements’ phase shifts at the RIS. Inspired by this emerging technique, this article focuses on joint beamforming design and optimization for RIS-aided hybrid satellite-terrestrial relay networks, where the links from the satellite and base station (BS) to multiple users are blocked. Specifically, a refracting RIS cooperates with a BS, where the latter operates as a half-duplex decode-and-forward relay, in order to strengthen the desired satellite signals at the blocked users. Considering the limited onboard power resource, the design objective is to minimize the total transmit power of both the satellite and BS while guaranteeing the rate requirements of users. Since the optimized beamforming weight vectors at the satellite and BS, and phase shifters at the RIS are coupled, leading to a mathematically intractable optimization problem, we propose an alternating optimization scheme by utilizing singular value decomposition and uplink–downlink duality to optimize beamforming weight vectors, and using Taylor expansion and penalty function methods to optimize phase shifters iteratively. Finally, simulation results are provided to verify the superiority of the proposed scheme compared to the benchmark schemes.