mlsanityData to 7 Oct 2026

Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

arXiv 2407.06227 ↗First posted 6 Jul 2024eess.SY cs.AI cs.SYcited by 6

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Abstract (arXiv)

This article investigates a control system within the context of six-generation wireless networks. The control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Accounting for the system dynamics, we formulate the sequential co-design decision-makings of communication and control over the discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potentials from our proposed learning framework. Furthermore, we discuss the open issues remaining to make our proposed offline learning framework feasible for real-world implementations, and highlight the research directions for future explorations.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 6 Jul 2024.

Builds on

16 of 29 references.

References · 16

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2023A Survey of Large Language Modelscited by 2,537
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  5. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  6. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  7. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 542
  8. 2023Reward Design with Language Modelscited by 257
  9. 2023Guiding Pretraining in Reinforcement Learning with Large Language Modelscited by 185
  10. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  11. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  12. 2020Age of Information: An Introduction and Surveycited by 64
4 more
  1. 2024Learning with Language-Guided State Abstractionscited by 11
  2. 2018Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networkscited by 9
  3. 2022A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systemscited by 2
  4. 2023Goal-Oriented Communication, Estimation, and Control over Bidirectional Wireless Linkscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicingcited by 3
  2. 2025Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicingcited by 2
  3. 2025Verifying Memoryless Sequential Decision-making of Large Language Modelscited by 1
  4. 2026Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimizationcited by 1
  5. 2024See Where You Read with Eye Gaze Tracking and Large Language Modelcited by 0
  6. 2025Semantic-Driven AI Agent Communications: Challenges and Solutionscited by 0