DRL-driven Online Optimization for Joint Traffic Reshaping and Channel Reconfiguration in RIS-assisted Semantic NOMA Communications

TL;DR

This paper introduces a DRL-based online optimization framework for RIS-assisted semantic NOMA networks, jointly optimizing traffic reshaping, channel reconfiguration, and decoding order to enhance energy efficiency and reduce computational complexity.

Contribution

It proposes a novel DRL-driven online optimization method for joint resource allocation in RIS-assisted semantic NOMA systems, addressing computational challenges in dynamic environments.

Findings

Deferrable semantic extraction improves long-term energy efficiency.

DRL framework reduces optimization runtime while maintaining high performance.

Numerical results validate the effectiveness of the proposed methods.

Abstract

This paper explores a reconfigurable intelligent surface (RIS)-assisted and semantic-aware wireless network, where multiple semantic users (SUs) transmit semantic information to an access point (AP) using the non-orthogonal multiple access (NOMA) method. The RIS reconfigures channel conditions, while semantic extraction reshapes traffic demands, providing enhanced control flexibility for NOMA transmissions. To enable efficient long-term resource allocation, we propose a deferrable semantic extraction scheme that can distribute the semantic extraction tasks across multiple time slots. We formulate a long-term energy efficiency maximization problem by jointly optimizing the RIS's passive beamforming, the SUs' semantic extraction, and the NOMA decoding order. Note that this problem involves multiple and coupled control variables, which can incur significant computational overhead in time-varying network environments. To support low-complexity online optimization, a deep reinforcement learning (DRL)-driven online optimization framework is developed. Specifically, the DRL module facilitates the adaptive selection and optimization of the most suitable option from traffic reshaping, channel reconfiguration, or NOMA decoding order assignment based on the dynamic network status. Numerical results demonstrate that the deferrable semantic extraction scheme significantly improves the long-term energy efficiency. Meanwhile, the DRL-driven online optimization framework effectively reduces the running time while maintaining superior learning performance compared to state-of-the-art methods.