Semantic Joint Source Channel Coding for Distributed Subsurface Imaging in Multi-Agent Systems

TL;DR

This paper introduces a semantic joint source-channel coding framework integrated with multi-agent exploration for subsurface imaging, significantly improving communication efficiency and imaging accuracy in distributed autonomous systems.

Contribution

It presents a novel semantic JSCC approach with AirComp for MAS, enhancing cooperative subsurface imaging and demonstrating benefits of side information in exploration tasks.

Findings

Semantic JSCC outperforms classical methods in high-connectivity networks.

Incorporating side information improves communication efficiency.

Approach achieves measurable improvements in imaging performance.

Abstract

Multi-agent systems (MAS) are a promising solution for autonomous exploration tasks in hazardous or remote environments, such as planetary surveys. In such settings, communication among agents is essential to ensure collaborative task execution, yet conventional approaches treat exploration and communication as decoupled subsystems. This work presents a novel framework that tightly integrates semantic communication into the MAS exploration process, adapting communication strategies to the exploration methodology to improve overall task performance. Specifically, we investigate the application of semantic joint source-channel coding (JSCC) with over-the-air computation (AirComp) for distributed function computation for the application of cooperative subsurface imaging using the adapt-then-combine full waveform inversion (ATC-FWI) algorithm. Our results demonstrate that semantic JSCC significantly outperforms classical point-to-point and standard JSCC methods, especially in high-connectivity networks. Furthermore, incorporating side information at the receiving agent enhances communication efficiency and imaging accuracy, a feature previously unexplored in MAS-based exploration. We validate our approach through a use case inspired by subsurface anomaly detection, showing measurable improvements in imaging performance per agent. This work underscores the potential of semantic communication in distributed multi-agent exploration, offering a communication-aware exploration paradigm that achieves task-relevant performance gains.