Semantic Communication for Task Execution and Data Reconstruction in Multi-User Scenarios

TL;DR

This paper introduces a multi-user semantic communication system that jointly optimizes task execution and data reconstruction by balancing mutual information, demonstrating that data quality can be improved without sacrificing task performance.

Contribution

It formulates a joint optimization framework for concurrent task execution and data reconstruction, linking mutual information maximization to human perceptual metrics like SSIM.

Findings

Increasing data reconstruction weight improves data quality significantly.

Task performance remains stable despite increased focus on data reconstruction.

The proposed method effectively balances dual objectives in multi-user scenarios.

Abstract

Semantic communication has gained significant attention with the advances in machine learning. Most semantic communication works focus on either task execution or data reconstruction, with some recent works combining the two. In this work, we propose a semantic communication system for concurrent task execution and data reconstruction for a multi-user scenario, which we formulate as the maximization of mutual information. To investigate the trade-off between the two objectives, we formulate a joint objective as a convex combination of task execution and data reconstruction. We show that under specific assumptions, the \ac{SSIM} loss can be obtained from the mutual information maximization objective for data reconstruction, which takes human visual perception into account. Furthermore, for constant resource use, we show that by increasing the weight of the reconstruction objective up to a certain point, the task execution performance can be kept nearly constant, while the data reconstruction can be significantly improved.