Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics

TL;DR

This paper presents a machine-learning framework combining Koopman operator theory and autoencoders to efficiently model, communicate, and control correlated systems with different dynamics, significantly reducing communication costs and enhancing control accuracy.

Contribution

It introduces a novel logical Koopman autoencoder framework that integrates semantic dynamics and control rules for multi-objective systems, improving efficiency and performance.

Findings

91.65% reduction in communication samples

Enhanced state prediction accuracy

Improved control performance in simulations

Abstract

This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules. These models form the proposed logical Koopman AE framework that reduces communication costs while improving state prediction accuracy and control performance, showing a 91.65% reduction in communication samples and significant performance gains in simulation.