Performance Analysis of RIS Assisted NOMA Networks over Rician Fading Channels

In this paper,we consider a downlink non-orthogonal multiple access(NOMA)network assisted by two reconfigurable intelligent surfaces(RISs)over Rician fading channels,in which each user communicates with the base station by the virtue of a RIS to enhance the reliability of the received signal.To evaluate the system performance of our proposed RIS-NOMA network,we first derive the exact and asymptotic expressions for the outage probability and ergodic rate of two users.Then,we derive the exact and asymptotic upper bound expressions for the ergodic rate of the nearby user.Based on asymptotic analytical results,the diversity orders for the outage probability and the high signal-to-noise ratio(SNR)slopes for the ergodic rate of the two users are obtained in the high SNR regime.Moreover,we derive the system throughputs of the proposed RIS-NOMA network in delay-limited and delay-tolerant transmission modes.Numerical results confirm our analysis and demonstrate that:1)The outage probability and ergodic rate of RIS-NOMA networks are superior to that of RIS-assisted orthogonalmultiple access(OMA)networks;2)The RIS-NOMA networks have ability to achieve a larger system throughput compared to RIS-OMA networks;and 3)The system performance of RIS-NOMA networks can be significantly improved as the number of reflecting elements and Rician factor increases.

Wireless communications empowered by reconfigurable intelligent surfaces: Model-based vs model-free channel estimation

Reconfigurable intelligent surfaces(RISs)are lately being attractive for their great potential in future sixth generation wireless communications(6G),which is attributed to their affordable energy consumption and easy integration.However,the large numbers of low-cost reflecting elements comprising RISs impose challenges for channel acquisition in various RIS-based wireless applications,such as RIS-enhanced orthogonal frequency-division multiplexing and multi-user multipleinput multiple-output systems.In this article,we first overview the state-of-the-art RIS hardware architectures designed to assist channel estimation for RIS-empowered wireless communication systems.We also overview existing channel estimation approaches,which are categorized into model-based and model-free techniques,and discuss their advantages and limitations depending on the RIS deployment.Design challenges with RIS-empowered systems in terms of hardware and other parameter limitations are presented,together with future research directions for channel estimation in RIS-based wireless systems,such as RISs with extremely large numbers of elements,multi-hop communications with RISs,and frequency division duplexing for high mobility systems.

Deep Learning for Secure Transmission in Reconfigurable Intelligent Surface-Assisted Communications

This paper investigates the secure transmission for reconfigurable intelligent surface(RIS)-assisted wireless communication systems.In the studied model,one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point.Therefore,it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem.This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined as the ratio of the secure rate to the total energy consumption of the system via jointly optimizing the RIS reflection reflector as well as the number of RIS elements,which results in a non-convex optimization problem that is intractable to solve by traditional methods.To tackle this issue,a new algorithm by leveraging the advance of the established deep learning(DL)technique is proposed so as to find the optimal RIS reflection vector and determine the optimal number of RIS reflection elements.Simulation results show that the proposed method reaches 96%of the optimal secure energy efficiency of the genie-aided algorithm.

Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing paradigms.Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities.Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation.We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks.Finally,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.