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

2025-07-12

AI Summary

This work investigates the joint optimization of minimizing total system energy consumption in reconfigurable intelligent surface (RIS)-assisted semantic non-orthogonal multiple access (NOMA) systems, subject to semantic user (SU) traffic demand constraints. To address this, we propose a co-design framework integrating semantic traffic reshaping and RIS-enabled channel reconstruction: for the first time, we couple semantic information extraction factor control with RIS passive beamforming—achieving adaptive semantic compression, NOMA-based superposition transmission, and intelligent RIS phase configuration—to enhance multi-user signal separability and reduce decoding energy. To tackle the resulting non-convex, tightly coupled optimization problem, we develop an efficient algorithm based on problem decomposition and successive convex approximation. Experimental results demonstrate that the proposed method significantly outperforms baseline schemes, achieving superior energy efficiency while guaranteeing semantic transmission quality.

Technology Category

• Search and Optimization: Non-convex Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Optimize energy consumption in RIS-assisted NOMA semantic networks
• Jointly adjust user traffic demands and RIS beamforming
• Decouple complex constraints for efficient problem-solving

Innovation

• RIS-assisted semantic NOMA for flexible decoding
• Joint traffic reshaping via semantic extraction
• Passive beamforming optimizes channel conditions