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.
@article{arxiv.2407.06227,
title = {Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs},
author = {Xianfu Chen and Celimuge Wu and Yi Shen and Yusheng Ji and Tsutomu Yoshinaga and Qiang Ni and Charilaos C. Zarakovitis and Honggang Zhang},
journal= {arXiv preprint arXiv:2407.06227},
year = {2024}
}