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

Reconfigurable intelligent surface (RIS) is a revolutionary technology to achieve spectrum-, energy-, and cost-efficient wireless networks. This paper considers an RIS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the RIS. A combined-channel-strength based user-ordering scheme for NOMA decoding is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the non-convex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. For the single-antenna BS setup, the optimal power allocation at the BS and the asymptotically optimal phase shifts at the RIS are obtained in closed forms. For the multiple-antenna BS setup, it is shown that the rank of the SDR solution of the transmit beamforming design is upper bounded by two. Also, the proposed algorithms are analyzed in terms of convergence and complexity. Simulation results show that the RIS-assisted NOMA system can enhance the rate performance significantly, compared to traditional NOMA without RIS and traditional orthogonal multiple access with/without RIS.

Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper proposes a deep learning-based joint beamforming (DLBF) design, aiming to maximize the system data rate for multi-hop RIS-aided communication systems. The proposed DLBF design consists of the reflection matrices design of all RISs and the transmit beamforming design at the base station, which has a reduced computational complexity. Numerical results show that the proposed DLBF can achieve 1.8 bit/s/Hz sum rate gain compared to the conventional beamforming method for the two-user scenario, which can be enhanced by large-scale users. The sum rate performance can be improved by increasing the number of RISs due to the reflection gain, and corresponding results provide a guidance of the multi-hop number selection for further investigation.

Reconfigurable Intelligent Surface (RIS) is a new and promising technique to solve the energy-efficiency, spectral-efficiency and hardware-cost problem faced by beyond-5G wireless networks. In this paper, we consider a RIS-assisted two-way relay network in which two users exchange information via the base station (BS) with the help of a RIS. By jointly designing the beamforming matrix at the BS and the phase shifts introduced by the RIS, the minimum SNR of the two users is maximized, under the transmit power constraint at the BS. The formulated problem is non-convex and difficult to solve in general. To start with, we first study the single BS antenna case. The design problem is reduced to the problem of how to choose the phase shifts for the RIS, in addition to optimizing the BS power amplification parameter. A Channel-Gain-Maximization (CGM) algorithm is proposed to solve the problem. For the multiple BS antenna case, we decouple the phase shifts and the beamforming matrix by taking an upper bound of the SNR. The way to obtain the RIS phase shifts is similar to the single BS antenna case while the beamforming matrix is obtained by utilizing an existing solution. A Channel-Gain-Maximization Maximal-Ratio-Beamforming (CGM-MRB) algorithm is thus developed. Finally, numerical results are presented to show the effectiveness of the proposed algorithms.

With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

In this research study, a Reconfigurable Intelligent Surface (RIS)–assisted Multiple-Input Single-Output (MISO) downlink wireless communication system is implemented in three-dimensional (3D) space. Each entity—base station (BS), RIS, and user equipments (UEs)—has its own geographical coordinates (X, Y, Z). The distances between the fixed entities (BS and RIS) and the randomly located UEs are computed using the Euclidean norm. The BS–RIS and RIS–UEs links are modeled as rician fading channels, whereas the BS–UEs links are modeled as rayleigh fading channels. Joint beamforming at the BS and phase shift at the RIS is formulated to provide a better spectral efficiency to the UEs. To achieve this, the entire communication system is translated into a Reinforcement Learning (RL) framework with state, action, and reward. A Randomized Ensembled Double Q-Learning (REDQ) RL agent, which belongs to the model-free, off-policy RL family, is trained on this RL framework. REDQ has produced a better average spectral efficiency compared to other model-free, off-policy RL agents such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft-Actor Critic (SAC) in all experimental setups (signal power and RIS elements). This improvements is attributed to REDQ’s better update-to-data (UTD) ratio, ensemble of critics, and controlled Q-function variance with a common target value for all critics. These findings underscore the potential of REDQ in enhancing performance in RIS-assisted wireless communication systems.

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under signal-to-interference-plus-noise ratio (SINR) constraints. The problem is highly challenging due to the non-convex SINR constraints as well as the non-convex unit-modulus constraints for both the phase shifts at the RIS and the analog beamforming at the BS. A penalty-based algorithm in conjunction with the manifold optimization technique is proposed to handle the problem, followed by an individual optimization method with much lower complexity. Simulation results show that the proposed algorithm outperforms the state-of-art algorithm. Results also show that the joint optimization of RIS response matrix and BS hybrid beamforming is much superior to individual optimization.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication has been considered as a promising technology for enabling future sixth-generation (6G) wireless systems. Due to the exploitation of extremely large bandwidth and large scale of RIS, RIS-aided THz communications would suffer from the beam split effect, where the generated beams cannot be aligned with the target physical direction in the whole bandwidth, so a severe array gain loss will be introduced. In this paper, the beam split effect is first analyzed in the existence of RIS. Then, a novel sub-connected RIS architecture is proposed to mitigate the beam split effect. The crux is to introduce additional time-delay (TD) modules and phase shifters into RIS elements so as to convert the classical phase-only precoding to the joint phase and delay precoding. Accordingly, a wideband precoding design is proposed to compensate for the severe array gain loss, and the performance analysis on the array gain is also provided. After that, we extend our discussions to the emerging scenarios with massive antennas equipped at the base station (BS), where the effect of “double beam split”, i.e., the coupling of beam split at the BS and the RIS, occurs. We prove the decomposability of the array gain, based on which the double beam split effect can be addressed by separately optimizing the wideband precoding at the BS and the RIS. Simulation results demonstrate that our proposed sub-connected RIS significantly alleviates the beam split effect with a small number of TD modules, and it is capable of achieving sub-optimal achievable rate performance with acceptable hardware cost and power consumption.

This paper investigates a reconfigurable intelligent surface (RIS) assisted cooperative multicell integrated sensing and communication (ISAC) system with multiple users and targets. In particular, the RIS is leveraged to assist the joint transmission of the multiple base stations (BSs) to multiple users, while assisting cooperative sensing by multiple BSs to perform multiple targets sensing. We formulate a problem for the purpose of minimizing the transmit power via jointly designing the transmit beamforming of the BSs and phase shifts of the RIS, while guaranteeing the achievable communication rate requirements and the sensing mutual information requirements. To address this non-convex problem, a high-quality alternating optimization algorithm is developed to split the intractable problem into two sub-problems. Specifically, with the given phase shifts of the RIS, the transmit beamforming sub-problem is addressed by semidefinite relaxation-based algorithm. A successive convex approximation (SCA) method-based and penalty function-based convex-concave procedure algorithm is proposed to tackle the RIS phase-shift optimization sub-problem. To reduce the computational complexity, an efficient low-complexity alternating optimization algorithm is developed. For the transmit beamforming design, an SCA method-based second-order cone programming algorithm is proposed, while for the RIS phase-shift design, a circle manifold optimization-based algorithm is introduced by utilizing penalty function. Simulation results validate the advancement of deploying RIS in enhancing the performance of cooperative multicell ISAC systems in terms of transmit power. Furthermore, our results illustrate the significant superiority of the proposed algorithms over the benchmark schemes.

This paper investigates the problem of secure energy efficiency maximization for a reconfigurable intelligent surface (RIS) assisted uplink wireless communication system, where an unmanned aerial vehicle (UAV) equipped with an RIS works as a mobile relay between the base station (BS) and a group of users. We focus on maximizing the secure energy efficiency of the system via jointly optimizing the UAV’s trajectory, the RIS’s phase shift, user association and transmit power. To tackle this problem, we divide the original problem into three sub-problems, and propose an efficient iterative algorithm. In particular, the successive convex approximation (SCA) method is applied to solve the nonconvex UAV trajectory, the RIS’s phase shift, and transmit power optimization sub-problems. Simulation results demonstrate that the proposed algorithm converges fast, and the proposed design can enhance the secure energy efficiency by up to 38% gains, compared to the traditional schemes without any RIS.

When deployed as reflectors for existing wireless base stations (BSs), reconfigurable intelligent surfaces (RISs) can be a promising approach to achieve high spectrum and energy efficiency. However, due to the large number of RIS elements, the joint optimization of the BS and reflector RIS configuration is challenging. In essence, the BS transmit power and RIS's reflecting configuration must be optimized so as to improve users' data rates and reduce the BS power consumption. In this paper, the problem of energy efficiency optimization is studied in an RIS-assisted cellular network endowed with an RIS reflector powered via energy harvesting technologies. The goal of this proposed framework is to maximize the average energy efficiency by enabling a BS to determine the transmit power and RIS configuration, under uncertainty on the wireless channel and harvested energy of the RIS system. To solve this problem, a novel approach based on deep reinforcement learning is proposed, in which the BS receives the state information, consisting of the users' channel state information feedback and the available energy reported by the RIS. Then, the BS optimizes its action composed of the BS transmit power allocation and RIS phase shift configuration using a neural network. Due to the intractability of the formulated problem under uncertainty, a case study is conducted to analyze the performance of the studied RIS-assisted downlink system by asymptotically deriving the upper bound of the energy efficiency. Simulation results show that the proposed framework improves energy efficiency up to 77.3% when the number of RIS elements increases from 9 to 25.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted two-way relay network, in which two users exchange information through the base station (BS) with the help of an RIS. By jointly designing the phase shifts at the RIS and beamforming matrix at the BS, our objective is to maximize the minimum signal-to-noise ratio (SNR) of the two users, under the transmit power constraint at the BS. We first consider the single-antenna BS case, and propose two algorithms to design the RIS phase shifts and the BS power amplification parameter, namely the SNR-upper-bound-maximization (SUM) method, and genetic-SNR-maximization (GSM) method. When there are multiple antennas at the BS, the optimization problem can be approximately addressed by successively solving two decoupled subproblems, one to optimize the RIS phase shifts, the other to optimize the BS beamforming matrix. The first subproblem can be solved by using SUM or GSM method, while the second subproblem can be solved by using optimized beamforming or maximum-ratio-beamforming method. The proposed algorithms have been verified through numerical results with computational complexity analysis.

This paper studies reconfigurable intelligent surface (RIS) empowered symbiotic radio over broadcasting signals, i.e., a base station (BS) broadcasts to multiple primary receivers (PRs) under the assistance of a RIS, while the RIS also transmits information to an Internet-of-Things receiver (IR) by modulating the incident broadcasting signals. We formulate a problem to minimize the BS's transmit power by jointly optimizing the BS's active beamforming and the RIS's phase shifts (i.e., passive beamforming), under the signal-to-noise-ratio constraints of the primary and IoT transmission as well as the RIS's phase-shift constraints. However, the problem is challenging to be solved optimally, since the optimization variables are coupled and the constraints are non-convex. An efficient iterative algorithm based on the block coordinated descent and modified semidefinite relaxation techniques is proposed to solve this problem for both discrete and continuous phase shift scenarios. The convergency of the algorithm is proved and the complexity of the algorithm is analyzed. Numerical results validate that the proposed system outperforms the benchmark of traditional broadcasting system without RIS.

With the rapid growth of wireless communication and the increasing demand for high-speed data transmission, next-generation wireless technologies are required to address the limitations of existing infrastructure. Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution to enhance wireless communications by intelligently manipulating the propagation environment. RISs are passive surfaces embedded with a large number of sub-wavelength elements that can reflect and control the propagation of wireless signals. By adaptively adjusting the reflection properties of incident signals, RISs can mitigate the effects of blockages and optimize resource allocation. This dissertation explores the potential of RISs in next-generation wireless communication systems. RISs offer numerous benefits, including overcoming network blockage, improving signal directionality, enhancing system performance, and enabling resource allocation optimizations. The dissertation contributes to the field of wireless communications by providing novel solutions for blockage prediction, resource allocation in unmanned aerial vehicle (UAV)-assisted networks, energy harvesting in Internet of Things (IoT) systems, efficient control of RIS elements, adaptive signal strength maintenance, and resource allocation in small cell networks (ScNs). We investigate using RISs to counter the blockage effect in wireless communication caused by network blockage conditions. A novel RIS-aided wireless communication problem is formulated, considering signal propagation through multiple paths and the Doppler spread for mobile user equipment (UE). Combining RGB camera sensing at the base station (BS) and RIS-assisted gain, a deep neural learning model is employed to maximize the probability of UE communication blockage detection. Simulation results demonstrate the effectiveness of the proposed RIS-assisted model in improving blockage prediction accuracy. We focus on resource allocation challenges in UAV-assisted wireless networks. The concept of actively simultaneously transmitting and reflecting (ASTAR)-RISs is introduced to amplify incident signals, enhancing signal-to-noise ratio (SNR) in remote areas. A path-planning problem is formulated, and iterative algorithms are used to find the optimal UAV trajectory. Simulation results highlight the potential of ASTAR-RISs in improving network performance and resource allocation in UAV-assisted wireless networks. We investigate integrating active and passive elements in RISs for batteryless IoT (b-IoT) systems. The trade-off between the number of RIS elements and system performance is analyzed. An optimization problem is formulated to minimize RIS energy consumption and maximize bits transmission, considering energy harvesting and device-to-device (D2D) communications. Iterative algorithms are employed to solve the problem, showcasing significant performance enhancements compared to benchmark models. The focus is on efficiently controlling the programmable passive and active elements of RISs. The proposed solution introduces the concept of "Module," where each module consists of an optimal number of active or passive elements controlled by a microcontroller. An optimization problem is formulated to minimize RIS energy consumption while satisfying energy harvesting and information causality constraints. Iterative algorithms solve the non-convex problem, leading to substantial performance improvements in IoT systems. We present a paradigm-shifting innovation called the RIS dynamic element, which transitions between active and passive modes to adapt to incoming signals' strength. The dynamic element is integrated into mobile edge computing IoT systems to maintain a desired SNR value. A non-convex maximization problem is formulated and solved using an iterative algorithm, demonstrating the advantage of RIS dynamic element-assisted systems in enhancing computational capacity. We propose a novel approach using ASTAR-RISs to address resource allocation limitations and limited reflection space in ScNs. The optimization problem is formulated to maximize SNR and minimize power consumption, considering the ON/OFF status, phase shift, and power budget of ASTAR-RISs. Simulation results reveal significant performance improvements compared to traditional RIS schemes. This future work entails the distributed RIS-assisted blockage prediction for mobile UEs and the security challenges of RISs. For blockage prediction, the aim is to develop a distributed framework utilizing machine learning algorithms and the distributed nature of RIS to dynamically adjust the reflection and transmission properties of RIS elements. Regarding security challenges, the focus lies on covert communications, such as eavesdropping and injection of false information, potential malicious exploitation, RIS malfunctioning, lack of standardized security protocols, physical attacks, absence of authentication mechanisms, and timely security updates. Addressing these challenges will bolster the overall security of RIS-enabled communication systems.

The papers in this special issue aim to report on the latest advances in signal processing for reconfigurable intelligent surface (RIS)-aided 6G networks with focus on theoretical development, algorithmic design, and future applications.RIS or intelligent reflecting surface (IRS) technology has been widely regarded as one of the most promising techniques to deal with the blockage issue in millimeter-wave (mmWave) communications. An RIS is a planar surface consisting of an array of nearlypassive reflecting elements, each of which can independently induce an controllable phase shift on the incident signal. By installing RIS on walls or ceilings, a virtual line-of-sight (LoS) link between a mmWave base station (BS) and the users can be established so as to bypass any blockage between them. In addition, RIS is a promising technology for applications in conventional sub-6 GHz communications. Specifically, by judiciously adjusting the phase shifts of the reflecting elements, the reflected signals can be constructively superimposed (even with the direct path if it is available) to enhance the desired signal power or can be cancelled out to mitigate the impact of co-channel interference or signal leakage to eavesdroppers. Since the elements of an RIS reflect the incoming signals without any signal processing operations that require radio-frequency (RF) chains, an RIS has a lower implementation cost than conventional active receivers and transmitters. An RIS can be fabricated with light weight and small-thickness layers, and thus can be readily integrated into the environment.

In this paper, we study the reconfigurable intelligent surface (RIS) based downlink multi-user system where a multi-antenna base station (BS) sends signals to various users assisted by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only the large-scale fading gain is required at the BS and a limited number of phase shifts can be realized by the finite-sized RIS. To maximize the sum rate, we propose a hybrid beamforming scheme where the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively. An iterative algorithm is designed for beamforming and theoretical analysis is provided to evaluate how the size of RIS influences the achievable rate. Simulation results show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts.