Joint Traffic Reshaping and Channel Reconfiguration in RIS-assisted Semantic NOMA Communications

TL;DR

This paper proposes a joint traffic reshaping and channel reconfiguration scheme in RIS-assisted semantic NOMA networks, significantly improving energy efficiency by optimizing semantic extraction and passive beamforming.

Contribution

It introduces a novel joint optimization framework for semantic control and RIS beamforming in RIS-assisted NOMA, enhancing energy efficiency and traffic management.

Findings

Significant energy savings compared to benchmark methods.

Effective joint optimization of semantic extraction and RIS beamforming.

Improved system performance in RIS-assisted semantic NOMA networks.

Abstract

In this paper, we consider a semantic-aware reconfigurable intelligent surface (RIS)-assisted wireless network, where multiple semantic users (SUs) simultaneously transmit semantic information to an access point (AP) by using the non-orthogonal multiple access (NOMA) method. The SUs can reshape their traffic demands by modifying the semantic extraction factor, while the RIS can reconfigure the channel conditions via the passive beamforming. This provides the AP with greater flexibility to decode the superimposed signals from the SUs. We aim to minimize the system's overall energy consumption, while ensuring that each SU's traffic demand is satisfied. Hence, we formulate a joint optimization problem of the SUs' decoding order and semantic control, as well as the RIS's passive beamforming strategy. This problem is intractable due to the complicated coupling in constraints. To solve this, we decompose the original problem into two subproblems and solve them by using a series of approximate methods. Numerical results show that the joint traffic reshaping and channel reconfiguration scheme significantly improves the energy saving performance of the NOMA transmissions compared to the benchmark methods.