Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior

TL;DR

This paper introduces a practical multi-task semantic communication framework for wireless channels that effectively manages rate constraints using an implicit optimal prior and hybrid learning, enhancing robustness and applicability.

Contribution

It extends the CMT-SemCom framework to real-world rate-limited wireless channels by integrating the implicit optimal prior and hybrid neural-machine learning methods.

Findings

Demonstrates effectiveness of the proposed system in rate-constrained scenarios

Shows robustness of the hybrid learning approach

Validates the framework through simulation results

Abstract

In this work, we expand the cooperative multi-task semantic communication framework (CMT-SemCom) introduced in [1], which divides the semantic encoder on the transmitter side into a common unit (CU) and multiple specific units (SUs), to a more applicable design. Our proposed system model addresses real-world constraints by introducing a general design that operates over rate-limited wireless channels. Further, we aim to tackle the rate-limit constraint, represented through the Kullback-Leibler (KL) divergence, by employing the density ratio trick alongside the implicit optimal prior method (IoPm). By applying the IoPm to our multi-task processing framework, we propose a hybrid learning approach that combines deep neural networks with kernelized-parametric machine learning methods, enabling a robust solution for the CMT-SemCom. Our framework is grounded in information-theoretic principles and employs variational approximations to bridge theoretical foundations with practical implementations. Simulation results demonstrate the proposed system's effectiveness in rate-constrained multi-task SemCom scenarios, highlighting its potential for enabling intelligence in next-generation wireless networks.