Joint Beamforming Design for RIS-Empowered NOMA-ISAC Systems

TL;DR

This paper presents a joint beamforming design for RIS-assisted NOMA-ISAC systems, optimizing communication and sensing performance through an iterative algorithm that enhances sum-rate and target detection capabilities.

Contribution

It introduces a novel joint optimization framework for active beamforming, RIS reflection, and radar filtering in RIS-empowered NOMA-ISAC systems, addressing complex non-convex challenges.

Findings

Proposed algorithm outperforms baseline methods in simulations.

Enhanced sum-rate and sensing accuracy achieved.

Effective handling of non-convex optimization problems.

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.