ARIS-RSMA-Enhanced ISAC System: Joint Rate Splitting and Beamforming Design

2026-02-06

IEEE Wireless Communications Letters

AI Summary

This work addresses the fairness bottleneck in multi-target sensing under obstructed line-of-sight conditions by proposing an active reconfigurable intelligent surface (ARIS)-assisted rate-splitting multiple access (RSMA) integrated sensing and communication (ISAC) system. The design jointly optimizes transmit/receive beamforming, ARIS phase shifts, and RSMA power allocation to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the multi-target echo signals. To the best of our knowledge, this is the first integration of ARIS with RSMA for ISAC, significantly enhancing sensing fairness and overall performance in obstructed scenarios. The resulting non-convex problem is efficiently solved via a majorization-minimization (MM) approach combined with sequential rank-one constraint relaxation (SROCR). Simulations demonstrate that the proposed scheme outperforms NOMA-, SDMA-, and passive RIS-based baselines, closely approaching the performance upper bound of pure sensing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

This letter proposes an active reconfigurable intelligent surface (ARIS) assisted rate-splitting multiple access (RSMA) integrated sensing and communication (ISAC) system to overcome the fairness bottleneck in multi-target sensing under obstructed line-of-sight environments. Beamforming at the transceiver and ARIS, along with rate splitting, are optimized to maximize the minimum multi-target echo signal-to-interference-plus-noise ratio under multi-user rate and power constraints. The intricate non-convex problem is decoupled into three subproblems and solved iteratively by majorization-minimization (MM) and sequential rank-one constraint relaxation (SROCR) algorithms. Simulations show our scheme outperforms non-orthogonal multiple access, space-division multiple access, and passive RIS baselines, approaching sensing-only upper bounds.

Problem

• fairness bottleneck
• multi-target sensing
• obstructed line-of-sight
• integrated sensing and communication
• rate-splitting multiple access

Innovation

• Active Reconfigurable Intelligent Surface (ARIS)
• Rate-Splitting Multiple Access (RSMA)
• Integrated Sensing and Communication (ISAC)
• Joint Beamforming and Rate Splitting
• Fairness-aware Multi-target Sensing