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

TL;DR

This article investigates a control system within the context of sixth-generation wireless networks and formulate the sequential co-design decision-makings of communication and control over a discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed.

Abstract

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.

Figures (6)

• Figure 1: Diagram of a single-agent Markov decision process, during which an agent interacts with the surrounding environment over the discrete time horizon. Following a policy, the agent chooses an action based on the state observation at each current time point.
• Figure 2: Offline reinforcement learning framework with large language models being integrated as different roles of state descriptor (SD), action recommender (AR) and feedback designer (FD).
• Figure 3: Convergence performance of our proposed learning framework, where $\alpha = \beta = 0.5$ and the IRS is with $75$ reflecting elements. Each point on the curves of our proposed learning framework corresponds to an average value of $10^5$ reward realizations from testing the policy that is trained offline at each iteration.
• Figure 4: Average AoS performance versus accurate inference probability, where $\alpha = 0.3$ and the IRS is with $25$ reflecting elements.
• Figure 5: Average energy consumption versus accurate inference probability, where $\alpha = 0.3$ and the IRS is with $25$ reflecting elements.