用于分布式集成感知与通信（ISAC）支持的车辆协调的目标导向语义通信
Goal-Oriented Semantic Communication for Distributed ISAC-Enabled Vehicle Coordination
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中文总结 AI 辅助
研究分布式ISAC支持的车辆在交叉口的协调，提出目标导向语义通信（GSC）框架，采用扩展卡尔曼滤波器、掩码混合近端策略优化及不确定性感知传输设计，相比基线实现100%无碰撞车辆协调且降低信令开销。
中文摘要 AI 辅助
无信号交叉口的车辆协调依赖于准确实时的车辆状态获取和可靠的指挥与控制（C&C）信号传递。现有研究通常将传感、通信和控制分开处理，可能导致冗余传输、过时状态信息和不可靠的车辆协调。本文研究了一种新场景，即多个路边单元（RSU）在中央基站（BS）管理下协同传输用于车辆状态获取的传感信号和用于车辆运动控制的C&C信号。为提高信令效率，提出统一的目标导向语义通信（GSC）框架，仅在对提高交叉口交通吞吐量语义重要时传输信号。采用扩展卡尔曼滤波器（EKF）预测车辆状态并融合分布式传感测量。开发了掩码混合近端策略优化（MHPPO）框架基于信息价值（VoI）奖励联合确定传感传输决策、C&C传输决策和C&C信号内容。还提出不确定性感知传输设计（UTD）提高可靠性。仿真结果表明，与相关研究的预测ISAC基线和几个消融基线相比，所提框架实现了100%无碰撞车辆协调且显著降低信令开销。
英文摘要
Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery. However, existing studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, outdated state information, and unreliable vehicle coordination. In this paper, we investigate a new scenario of distributed integrated sensing and communication (ISAC)-enabled vehicle coordination at intersections, where multiple roadside units (RSUs) collaboratively transmit sensing signals for vehicle state acquisition and C&C signals for vehicle movement control under the management of a central base station (BS). To improve signaling efficiency, we propose a unified goal-oriented semantic communication (GSC) framework, which transmits sensing and C&C signals only when they are semantically important for improving intersection traffic throughput. Specifically, an extended Kalman filter (EKF) is adopted to predict vehicle states and fuse distributed sensing measurements. A masked hybrid proximal policy optimization (MHPPO) framework is then developed to jointly determine sensing transmission decisions, C&C transmission decisions, and C&C signal contents based on a value-of-information (VoI) reward. Furthermore, we propose an uncertainty-aware transmission design (UTD), including robust beamforming and VoI-based time-division power allocation, to improve sensing and communication reliability under vehicle state uncertainty and inter-RSU interference. Simulation results show that our proposed framework achieves 100% collision-free vehicle coordination with significantly reduced signaling overhead compared with predictive ISAC baselines adapted from state-of-the-art related studies and several ablation baselines.
发表机构
- King’s College London（伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。
