Pragmatic Communication for Remote Control of Finite-State Markov Processes

TL;DR

This paper develops a theoretical framework for goal-oriented communication in remote control of finite-state Markov processes, optimizing control performance and communication costs in cyber-physical systems.

Contribution

It introduces a pragmatic communication model for remote control, analyzing two decision architectures and proposing algorithms with optimality guarantees.

Findings

Proposed algorithms effectively balance control performance and communication costs.

Analyzed the computational complexity and fundamental limits of the algorithms.

Compared pull-based and push-based control architectures in a unified framework.

Abstract

Pragmatic or goal-oriented communication can optimize communication decisions beyond the reliable transmission of data, instead aiming at directly affecting application performance with the minimum channel utilization. In this paper, we develop a general theoretical framework for the remote control of finite-state Markov processes, using pragmatic communication over a costly zero-delay communication channel. To that end, we model a cyber-physical system composed of an encoder, which observes and transmits the states of a process in real-time, and a decoder, which receives that information and controls the behavior of the process. The encoder and the decoder should cooperatively optimize the trade-off between the control performance (i.e., reward) and the communication cost (i.e., channel use). This scenario underscores a pragmatic (i.e., goal-oriented) communication problem, where the purpose is to convey only the data that is most valuable for the underlying task, taking into account the state of the decoder (hence, the pragmatic aspect). We investigate two different decision-making architectures: in pull-based remote control, the decoder is the only decision-maker, while in push-based remote control, the encoder and the decoder constitute two independent decision-makers, leading to a multi-agent scenario. We propose three algorithms to optimize our system (i.e., design the encoder and the decoder policies), discuss the optimality guarantees ofs the algorithms, and shed light on their computational complexity and fundamental limits.