Joint Beamforming Design for RIS-Empowered NOMA-ISAC Systems

2025-08-19

AI Summary

This paper investigates the joint beamforming optimization problem for communication and sensing in RIS-aided NOMA-ISAC systems, aiming to maximize the sum rate of users subject to coupled constraints—including radar SNR thresholds, user SINR requirements, RIS phase continuity, total transmit power limits, and successive interference cancellation (SIC) decoding order. To tackle this highly non-convex problem, we propose an alternating optimization-based joint design framework that jointly optimizes the base station’s active beamformers, the RIS’s passive reflection coefficients, and the radar’s receive filter. The proposed method effectively decouples the intertwined optimization variables while preserving sensing performance and significantly enhancing communication efficiency. Simulation results demonstrate that the proposed algorithm substantially outperforms existing baseline schemes in both user sum rate and radar SNR, validating the effectiveness and superiority of integrated sensing-and-communication design empowered by RIS-enhanced NOMA.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Security and Privacy: Large-scale security measurements

Abstract

This paper investigates a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system and proposes a joint communication and sensing beamforming design based on non-orthogonal multiple access (NOMA) technology. The system employs a dual-functional base station (DFBS) to simultaneously serve multiple users and sense multiple targets with the aid of RIS. To maximize the sum-rate of users, we jointly optimize the DFBS's active beamforming, the RIS's reflection coefficients, and the radar receive filters. The optimization is performed under constraints including the radar signal-to-noise ratio thresholds, the user signal-to-interference-plus-noise ratio requirements, the phase shifts of the RIS, the total transmit power, the receive filters, and the successive interference cancellation decoding order. To tackle the complex interdependencies and non-convex nature of the optimization problem, we introduce an effective iterative algorithm based on the alternating optimization framework. Simulation results demonstrate that the proposed algorithm outperforms baseline algorithms, highlighting its distinct advantages in the considered RIS-empowered NOMA-ISAC systems.

Problem

• Optimizing beamforming for RIS-assisted NOMA-ISAC systems
• Maximizing user sum-rate under multiple system constraints
• Solving non-convex joint communication-sensing optimization problem

Innovation

• Joint beamforming with RIS and NOMA
• Alternating optimization for non-convex problem
• Dual-functional base station for sensing and communication