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

arXiv 2503.08661 ↗First posted 11 Mar 2025cs.IT cs.CV eess.IVcited by 0

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Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 11 Mar 2025.

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32 of 58 references.

References · 32

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2016Deep Variational Information Bottleneckcited by 930
  3. 2015Deep Learning and the Information Bottleneck Principlecited by 802
  4. 2017Variational Dropout Sparsifies Deep Neural Networkscited by 265
  5. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 248
  6. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 194
  7. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 175
  8. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 152
  9. 2021Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approachcited by 110
  10. 2019DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedbackcited by 96
  11. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 96
  12. 2019BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systemscited by 95
20 more
  1. 2022Semantic Communications for Future Internet: Fundamentals, Applications, and Challengescited by 95
  2. 2022Less Data, More Knowledge: Building Next Generation Semantic Communication Networkscited by 62
  3. 2020Communication-Computation Trade-Off in Resource-Constrained Edge Inferencecited by 60
  4. 2020Wireless Image Retrieval at the Edgecited by 59
  5. 2021Lossy Compression for Lossless Predictioncited by 52
  6. 2021Task-Oriented Communication for Multi-Device Cooperative Edge Inferencecited by 52
  7. 2018Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergycited by 51
  8. 2024Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approachcited by 39
  9. 2021OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channelscited by 26
  10. 2019Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruningcited by 25
  11. 2022Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysiscited by 23
  12. 2020Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processingcited by 21
  13. 2019Successive Refinement of Images with Deep Joint Source-Channel Codingcited by 14
  14. 2020Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlinescited by 9
  15. 2022Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaversecited by 7
  16. 2023Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaversecited by 7
  17. 2019Prediction and Communication Co-design for Ultra-Reliable and Low-Latency Communicationscited by 5
  18. 2023Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participationcited by 3
  19. 2018Augmented Reality Predictive Displays to Help Mitigate the Effects of Delayed Telesurgerycited by 2
  20. 2019Cross-layer Design for Mission-Critical IoT in Mobile Edge Computing Systemscited by 2

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