  The remote wireless control of industrial systems is one of the major use
cases for 5G and beyond systems: in these cases, the massive amounts of sensory
information that need to be shared over the wireless medium may overload even
high-capacity connections. Consequently, solving the effective communication
problem by optimizing the transmission strategy to discard irrelevant
information can provide a significant advantage, but is often a very complex
task. In this work, we consider a prototypal system in which an observer must
communicate its sensory data to a robot controlling a task (e.g., a mobile
robot in a factory). We then model it as a remote Partially Observable Markov
Decision Process (POMDP), considering the effect of adopting semantic and
effective communication-oriented solutions on the overall system performance.
We split the communication problem by considering an ensemble Vector Quantized
Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement
Learning (DRL) agent to dynamically adapt the quantization level, considering
both the current state of the environment and the memory of past messages. We
tested the proposed approach on the well-known CartPole reference control
problem, obtaining a significant performance increase over traditional
approaches.
