Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources

TL;DR

This paper introduces a cooperative and collaborative multi-task semantic communication system for distributed sources, improving task accuracy in realistic partial observation scenarios through an end-to-end data-driven approach.

Contribution

It proposes a novel CCMT SemCom system that enables multi-task processing with cooperation and collaboration for distributed sources, extending prior work to more practical settings.

Findings

Significant improvement in task execution accuracy with the proposed system.

Effective handling of distributed partial observations in multi-task SemCom.

Robust performance in complex datasets when channel noise is manageable.

Abstract

In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs). While earlier studies in multi-task SemCom focused on full observation settings, our research explores a more realistic case where only distributed partial observations are available, such as in a production line monitored by multiple sensing nodes. To address this, we propose an SemCom system that supports multi-task processing through cooperation on the transmitter side via split structure and collaboration on the receiver side. We have used an information-theoretic perspective with variational approximations for our end-to-end data-driven approach. Simulation results demonstrate that the proposed cooperative and collaborative multi-task (CCMT) SemCom system significantly improves task execution accuracy, particularly in complex datasets, if the noise introduced from the communication channel is not limiting the task performance too much. Our findings contribute to a more general SemCom framework capable of handling distributed sources and multiple tasks simultaneously, advancing the applicability of SemCom systems in real-world scenarios.