RIS-Empowered Satellite-Aerial-Terrestrial Networks With PD-NOMA

Physical layer security and covert communication in UAV-ISAC networks: A comprehensive survey

In this paper, we explore the communication performance of vehicle-to-vehicle (V2V) networks assisted by a reconfigurable intelligent surface (RIS) and a simultaneous transmitting and reflecting intelligent omni-surface (STAR-IOS) under non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. In particular, we consider that the RIS is close to the transmitter vehicle while the STAR-IOS is near the receiver vehicles. In addition, it is assumed that the STAR-IOS exploits the energy-splitting (ES) protocol for communication and the fading channels between the RIS and STAR-IOS follow composite Fisher-Snedecor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {F}$</tex-math></inline-formula> distribution. Under such assumptions, we first use the central limit theorem (CLT) to derive the PDF and the CDF of equivalent channels at receiver vehicles, and then, we derive the closed-form expression of outage probability (OP) under NOMA/OMA scenarios. Additionally, by exploiting Jensen's inequality, we propose an upper bound of the ergodic capacity (EC) and subsequently develop an analytical expression of the energy efficiency (EE) for both NOMA and OMA cases. Further, our analytical results, which are double-checked with the Monte-Carlo simulation, reveal that applying RIS/STAR-RIS in V2V communications significantly enhances the communication performance of intelligent transportation systems (ITS). Besides, the results indicate that considering the NOMA scheme provides better performance in terms of the OP, EC, and EE as compared with the OMA case for the considered V2V communication system.

Integrating visible light communication (VLC) with the reconfigurable intelligent surface (RIS) significantly enhances physical layer security by enabling precise directional signal control and dynamic adaptation to the communication environment. These capabilities strengthen the confidentiality and security of VLC systems. This paper presents a comprehensive study on the joint optimization of VLC access point (AP) power allocation, RIS association, and RIS elements orientation angles for secure VLC systems, while considering rate-splitting multiple access (RSMA) and power-domain non-orthogonal multiple access (NOMA) schemes. Specifically, two frameworks are proposed to maximize both the minimum secrecy rate (SR) and the minimum secrecy energy efficiency (SEE) by jointly optimizing power allocation, RIS association, and RIS elements orientation angles for both power-domain NOMA and RSMA-based VLC systems. The proposed frameworks consider random device orientation and guarantee the minimum user-rate requirement. The proposed optimization frameworks belong to the class of mixed integer nonlinear programming, which has no known feasible solution methodology to guarantee the optimal solution. Moreover, the increased degree of freedom and flexibility from the joint consideration of power control, RIS association and element orientation results in a large set of decision variables and constraints, which further complicates the optimization problem. To that end, we utilize a genetic algorithm-based solution method, which through its exploration and exploitation capabilities can obtain a good quality solution. Additionally, comprehensive simulations show that the RSMA scheme outperforms the power-domain NOMA scheme across both the SR and SEE metrics over various network parameters. Furthermore, useful insights on the impact of minimum user rate requirement, number of RIS elements, and maximum VLC AP transmit power on the minimum SR and SEE performances are provided.

Power-domain non-orthogonal multiple access (NOMA) has become a promising technology to exploit the new dimension of the power domain to enhance the spectral efficiency of wireless networks. However, most existing NOMA schemes rely on the strong assumption that users’ channel gains are quite different, which may be invalid in practice. To unleash the potential of power-domain NOMA, we propose a reconfigurable intelligent surface (RIS)-empowered NOMA scheme to introduce desirable channel gain differences among the users by adjusting the phase shifts at the RIS. Our goal is to minimize the total transmit power by jointly optimizing the beamforming vectors at the base station, the phase-shift matrix at the RIS, and user ordering. To address challenge due to the highly coupled optimization variables, we present an alternating optimization framework to decompose the non-convex bi-quadratically constrained quadratic problem under a specific user ordering into two rank-one constrained matrices optimization problems via matrix lifting. To accurately detect the feasibility of the non-convex rank-one constraints and improve performance by avoiding early stopping in the alternating optimization procedure, we equivalently represent the rank-one constraint as the difference between nuclear norm and spectral norm. A difference-of-convex (DC) algorithm is further developed to solve the resulting DC programs via successive convex relaxation, followed by establishing the convergence of the proposed DC-based alternating optimization method. We further propose an efficient user ordering scheme with closed-form expressions, considering both the channel conditions and users’ target data rates. Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.

On performance of multi-RIS assisted multi-user nonorthogonal multiple access system over Nakagami-[formula omitted] fading channels

In a device-to-device communication scenario, the number of far users (FUs) is higher than the near users (NUs) in each cluster. In such a scenario, the paired NUs and FUs communicate through non-orthogonal multiple access (NOMA), and the remaining FUs communicate through orthogonal multiple accesses (OMA) schemes. This system is called as hybrid-NOMA (HNOMA). This letter proposes a reconfigurable intelligent surface (RIS)-assisted HNOMA system to enhance the energy and spectral efficiencies of the conventional HNOMA. In this proposed RIS-assisted HNOMA (PR-HNOMA) system, the NU is paired with multiple FUs with allocations of different bandwidths. The PR-HNOMA system’s sum rate is maximized by considering joint beamforming at BS and RIS via semidefinite relaxation (SDR) and alternatively optimization (AO) techniques. Further, the sum rate of the PR-HNOMA under imperfect successive interference cancellation (ISIC) is analyzed and compared with different NOMA and OMA schemes. Simulation results show that the PR-HNOMA achieves better sum-rate performance than the conventional RIS assisted HNOMA (R-HNOMA), and RIS assisted NOMA (R-CNOMA), and OMA (R-OMA).

Active reconfigurable intelligent surface (RIS) is deemed a prospective candidate to compensate the double fading attenuation caused by the passive RIS, where each element can reflect and amplify the received signals through its low power integrating amplifiers. This paper investigates the physical layer security (PLS) for a non-orthogonal multiple access (NOMA) system through a deployed active RIS to defend multiple eavesdroppers (Eves), where the practical discrete RIS phase shift designs are taken into account. To characterize the secrecy performance, analytical expressions of secrecy outage probability (SOP) and effective secrecy throughput (EST) for an active RIS assisted NOMA (RIS-NOMA) system are derived based on heuristic approximations, which is in consideration of two scenarios with and without direct links. Furthermore, secrecy diversity orders are attained based on theoretical results, of which the convergence rates and gaps between the discrete and continuous phase shifting are further evaluated. The results reveal that a 4-bit quantization can achieve the secrecy diversity order of continuous phase shifts. Numerical results are furnished to corroborate the analyses, and illustrate that the security performance for active RIS-NOMA outperforms the passive RIS-NOMA and conventional cooperation communications under the same total power consumption. The impacts of phase quantization bit, amplification factor and the number of Eves as well as reflective elements on security performance are also substantiated by simulations.

Reconfigurable Intelligent Surfaces (RIS) are considered a technology in future wireless communication networks due to their unique material properties. RIS can be combined with non-orthogonal multiple access (NOMA) to improve physical layer security (PLS). In this paper, we propose a double RIS-assisted NOMA physical layer security communication scenario that constructs an optimization problem in the presence of internal eavesdropping to maximize security rate for legitimate users. An iterative algorithm based on alternating optimization is proposed to solve the nonconvex and high coupling of this optimization problem. Simulation results show that the safety transmission of the double RIS-assisted NOMA physical layer is much better than the traditional single RIS-assisted scheme.

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology. The problem of joint deployment, phase shift design, as well as power allocation in the multiple-input-single-output (MISO) NOMA network is formulated for maximizing the energy efficiency with considering users particular data requirements. To tackle this pertinent problem, machine learning approaches are adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. Secondly, a decaying double deep Q-network (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS. In the proposed algorithm, the base station, which controls the RIS by a controller, acts as an agent. The agent periodically observes the state of the RIS-enhanced system for attaining the optimal deployment and design policies of the RIS by learning from its mistakes and the feedback of users. Additionally, it is proved that the proposed D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> QN based deployment and design algorithm is capable of converging within mild conditions. Simulation results are provided for illustrating that the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between the prediction accuracy and computational complexity. Finally, it is demonstrated that the proposed D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> QN based algorithm outperforms the benchmarks, while the NOMA-enhanced RIS system is capable of achieving higher energy efficiency than orthogonal multiple access (OMA) enabled RIS system.

Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are considered key enabling technologies for sixth-generation (6G) wireless networks due to their ability to improve spectral efficiency, coverage, and energy efficiency. However, the joint optimization of user clustering and power allocation in RIS-assisted NOMA systems leads to highly complex and non-convex optimization problems, especially in dynamic and large-scale network environments. Recently, Deep Reinforcement Learning (DRL) has emerged as a powerful tool for solving such complex resource allocation problems without relying on accurate mathematical models. In this paper, an in-depth review of existing research on RIS, NOMA, and DRL-based optimization techniques is presented. The literature is analyzed and classified based on system models, optimization objectives, and learning methods. To illustrate the benefits and limitations of current methods, a comparative analysis is given. Finally, open challenges and future research directions are identified, with a focus on the cooperative optimization of power distribution and user clustering for 6G networks.

Reconfigurable intelligent surface (RIS) has recently been envisioned to provide an adaptable channel for futuristic wireless communication networks. This work characterized the impact of hardware impairments in the downlink RIS-aided non-orthogonal multiple access (NOMA) wireless networks. We considered the hardware impairments in the transceivers of the base station (BS) and users, phase error at the RIS and the imperfect successive interference cancellation (SIC) for the NOMA users. Specifically, the performance is evaluated by deriving the closed-form expressions for the outage probability (OP), spectral efficiency (SE) and energy efficiency (EE). Additionally, the performance of the orthogonal multiple access (OMA) system is also evaluated and compared with its NOMA counterpart. The RIS-aided NOMA outperforms OMA for the ideal hardware scenario. However, the hardware impairments significantly deteriorate RIS-aided NOMA's performance compared to RIS-aided OMA. Further, we have also compared the proposed RIS-aided NOMA with the baseline full-duplex relaying (FDR) based NOMA. The results show that the gains are more significant for a RIS-aided system than FDR.

An optimization framework for RIS-based energy-efficient multi-cell NOMA systems

In the upcoming beyond fifth-generation (B5G), the surging demand on high network capacity and numerous access devices can hardly be satisfied by the scarce spectrum resources. Capable of sharing the same spectrum resource among multiple users, non-orthogonal multiple access (NOMA) provides an efficient method to enhance the spectrum efficiency, serving as a strong candidate for next generation multiple access (NGMA). To achieve a better performance gain of NOMA, reconfigurable intelligent surface (RIS) provides an effective leverage by reconstructing the channel conditions. Since the increasingly computational complexity and elusive communication environment in RIS-aided NOMA networks, we employ machine learning (ML) to improve network performance. In this article, we first present an overview of the NOMA, RIS, and ML. Based on the good compatibility between NOMA and RIS, we then provide a series of emerging application scenarios and associated challenges of RIS-aided NOMA. After that, we discuss the potential of ML approaches to cope with the challenges in RIS-aided NOMA. Finally, evaluation results are provided to illustrate the superiority of the RIS-aided NOMA network based on ML.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

In this article, a cooperative non-orthogonal multiple access (NOMA) mobile edge computing (MEC) network aided by reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) is considered, where the transmitters offload parts of their computing tasks to the MEC server via a relay and an RIS for enhancing the transmission environment. The goal is to minimize the task delay and energy consumption for the considered networks by jointly designing the RIS's reflection beamforming vector, the power splitting (PS) ratio, the power allocation factors of the relay, as well as the offloading transmit power and the offloading task ratio. To solve the proposed problems, an iterative optimization algorithm based on the successive convex approximation (SCA) method and the penalty method is proposed to decompose the formulated problems into two sub-problems. In particular, we jointly optimize the PS ratio, the power allocation coefficients and the edge computing resource based on the SCA technology, and then the penalty method is used to optimize the RIS's reflecting beamforming vector. Simulation results illustrate that the proposed NOMA-MEC networks aided by RIS and SWIPT are superior to the conventional orthogonal multiple access MEC networks with SWIPT aided by RIS in terms of task delay and energy consumption.