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Multi-Agent PPO-Based Resource Optimization for Full-Duplex RIS-Aided NOMA-ISAC Systems
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DOI：10.1109/OJCOMS.2025.3635274.png)
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Abstract
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This paper proposes a multi-agent deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO) for joint resource optimization in full-duplex (FD) reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA) integrated sensing and communication (ISAC) systems. The goal is to maximize the minimum beampattern gain under quality-of-service (QoS) constraints for both uplink (UL) and downlink (DL) users. The optimization jointly controls transmit beamforming, RIS phase shift, DL power allocation, and UL transmit power. A centralized training with decentralized execution approach is adopted, where two agents are defined: a DL agent responsible for DL beamforming, RIS configuration, and power allocation, and a UL agent responsible for uplink power control. Each agent interacts with the shared environment, which comprises the base station (BS), RIS, and users, and learns its optimal policy under time-varying channels and mutual interference. Simulation results demonstrate that the proposed multi-agent PPO (MA-PPO) significantly outperforms baseline methods, including single-agent PPO and heuristic schemes, in terms of convergence speed, sum-rate, and beampattern gain. Moreover, the MA-PPO method exhibits superior scalability and performance in FD mode over half-duplex (HD) counterparts under various user densities and RIS configurations, showcasing its effectiveness for real-time joint communication and sensing in next-generation wireless networks.
Keywords:
Full-duplex
integrated sensing and communication
deep reinforcement learning
reconfigurable intelligent surfaces
non-orthogonal multiple access
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