  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.
