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

2024-10-03

arXiv.org

AI Summary

Addressing the challenge of simultaneously achieving semantic dynamical modeling, low communication overhead, and high-accuracy state prediction/control in multi-objective coupled dynamical systems, this paper proposes a joint semantic communication and control design framework. We innovatively introduce a dual-model coordination mechanism comprising Dynamic Semantic Koopman (DSK) and Logical Semantic Koopman (LSK) models, and—uniquely—embed Signal Temporal Logic (STL) into a Koopman autoencoder to enable semantic-level encoding of control rules and interpretable dynamical learning. Leveraging Koopman operator theory and semantic dynamical modeling, our approach reduces communication sampling by 91.65% in simulations while significantly improving state prediction accuracy and closed-loop control performance. This work establishes a novel paradigm for semantics-driven intelligent control.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Develop a semantic communication and control co-design framework
• Linearize state evolution using dynamic semantic Koopman models
• Reduce communication costs while improving prediction and control performance

Innovation

• Uses Koopman operator in autoencoder for linear latent dynamics
• Incorporates signal temporal logic to encode control rules
• Reduces communication costs while improving prediction and control