Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems

2025-03-11

AI Summary

To address the low communication-computation-control co-design efficiency in industrial cyber-physical systems—caused by bandwidth constraints, channel noise, and stringent end-to-end latency requirements—this paper proposes a task-oriented joint source-channel coding (JSCC) framework integrated with delay-aware trajectory-guided control prediction (DTCP). Leveraging the information bottleneck principle, the method enables semantic-level prioritization of critical information and anticipatory control decision-making. Evaluated on the CARLA simulation platform under a 1-second end-to-end latency constraint, the approach achieves a driving score of 48.12—31.59 points higher than the baseline BPG—while reducing bandwidth consumption by 99.19%. This work is the first to deeply integrate task-semantic-driven JSCC with dynamically trajectory-guided predictive control, significantly enhancing real-time performance, robustness, and spectral efficiency of closed-loop control systems under resource constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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%.

Problem

• Address bandwidth limitations, noise, and latency in industrial CPS.
• Enhance data transmission efficiency with task-oriented JSCC using IB.
• Mitigate E2E delays with DTCP strategy for timely autonomous driving.

Innovation

• Task-oriented Joint Source-Channel Coding (JSCC)
• Delay-Aware Trajectory-Guided Control Prediction (DTCP)
• Integration of communication, computing, and control