AI 中文总结
针对车载环境信号阻塞与快时变信道挑战，提出RM-A-RIS辅助的车载语义通信系统，采用AO算法优化，在Sum-SSE上较三类基准分别实现132.9%、9.2%、35.2%提升。
AI 中文摘要
车载环境中严重的信号阻塞和快时变信道对可靠的语义通信构成了关键挑战。为解决这些问题，本文提出一种新型的行可移动主动可重构智能表面（RM-A-RIS）辅助的车载语义通信系统。该架构将主动信号放大与单元移动性相结合，以补偿乘性衰落并重构信道几何结构，从而增强空间分集。我们构建了一个联合优化问题，通过协调RIS单元位置、主动反射系数和语义符号长度，最大化语义频谱效率（SSE）。为解决耦合非凸性，开发了一种高效的交替优化（AO）算法。仿真结果表明，所提方案显著优于现有基准，与被动RIS、固定位置主动RIS和QPSO基准相比，在总语义频谱效率（Sum-SSE）上分别实现了高达132.9%、9.2%和35.2%的提升。
英文摘要
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.
CommentsThis paper has been accepted by IEEE TWC
