Active Movable-Element RIS Assisted Vehicular Semantic Communications: Modeling and Optimization

2026-07-29

AI Summary

This work addresses the degradation of semantic communication reliability in vehicular networks caused by severe signal blockage and rapidly time-varying channels. To overcome this challenge, the paper proposes a mobile active reconfigurable intelligent surface (RM-A-RIS)-assisted semantic communication system that uniquely integrates the physical mobility of RIS elements with active signal amplification capability. By jointly optimizing element positions, active reflection coefficients, and semantic symbol lengths, the system reconstructs the channel’s geometric structure and enhances spatial diversity. An alternating optimization algorithm is developed to solve the resulting coupled non-convex problem. Experimental results demonstrate that the proposed approach improves total semantic spectral efficiency by 132.9%, 9.2%, and 35.2% over passive RIS, fixed-position active RIS, and a QPSO-based benchmark, respectively, thereby significantly surpassing the performance limitations of conventional static or passive RIS architectures.

Abstract

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

Problem

• vehicular semantic communications
• signal blockage
• fast-varying channels
• reconfigurable intelligent surface
• semantic spectral efficiency

Innovation

• Active RIS
• Movable Elements
• Semantic Communications
• Vehicular Networks
• Alternating Optimization