Learning-based Power Control for Secure Covert Semantic Communication

TL;DR

This paper introduces a learning-based power control framework for secure semantic communication in wireless networks, enhancing covert transmission and reducing eavesdropping risk through a novel optimization approach.

Contribution

It presents a general covert SemCom framework and a soft actor-critic based power control method to improve security and performance in wireless semantic communication.

Findings

Effective enhancement of covert SemCom performance

Successful reduction of eavesdropping risk

Demonstrated superiority over existing methods

Abstract

Despite progress in semantic communication (SemCom), research on SemCom security is still in its infancy. To bridge this gap, we propose a general covert SemCom framework for wireless networks, reducing eavesdropping risk. Our approach transmits semantic information covertly, making it difficult for wardens to detect. Given the aim of maximizing covert SemCom performance, we formulate a power control problem in covert SemCom under energy constraints. Furthermore, we propose a learning-based approach based on the soft actor-critic algorithm, optimizing the power of the transmitter and friendly jammer. Numerical results demonstrate that our approach effectively enhances the performance of covert SemCom.