针对工业系统通信计算控制延迟问题，提出任务导向协同设计框架。
Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems
- 用信息瓶颈优化编码，只传任务关键数据提升效率。
- 预测端到端延迟并规划轨迹，驾驶得分比BPG高31.59点。
- 适合工业自动驾驶等低延迟高可靠场景使用。
本文提出一种任务导向的通信、计算与控制协同设计框架，解决关键工业网络物理系统中的带宽限制、噪声干扰和延迟问题。为提升通信效率与鲁棒性，设计基于信息瓶颈（IB）的任务导向联合源信道编码（JSCC），优先传输任务相关数据。为缓解端到端（E2E）延迟影响，提出延迟感知轨迹引导控制预测（DTCP）策略，将轨迹规划与控制预测结合，基于E2E延迟预判指令。该策略与任务导向JSCC协同设计，聚焦传输任务特定信息，实现及时可靠的自主驾驶。在CARLA仿真中，当E2E延迟为1秒（20个时隙）时，本框架驾驶得分为48.12，较使用更好便携图形（BPG）提升31.59分，同时带宽消耗降低99.19%。
原文摘要 · Abstract (English)
This paper proposes a task-oriented co-design framework that integrates communication, computing, and control to address the key challenges of bandwidth limitations, noise interference, and latency in mission-critical industrial Cyber-Physical Systems (CPS). To improve communication efficiency and robustness, we design a task-oriented Joint Source-Channel Coding (JSCC) using Information Bottleneck (IB) to enhance data transmission efficiency by prioritizing task-specific information. To mitigate the perceived End-to-End (E2E) delays, we develop a Delay-Aware Trajectory-Guided Control Prediction (DTCP) strategy that integrates trajectory planning with control prediction, predicting commands based on E2E delay. Moreover, the DTCP is co-designed with task-oriented JSCC, focusing on transmitting task-specific information for timely and reliable autonomous driving. Experimental results in the CARLA simulator demonstrate that, under an E2E delay of 1 second (20 time slots), the proposed framework achieves a driving score of 48.12, which is 31.59 points higher than using Better Portable Graphics (BPG) while reducing bandwidth usage by 99.19%.
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