Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

TL;DR

This paper explores how Mixture of Experts can enhance generative AI models to improve physical layer communication security, addressing limitations like high complexity and limited adaptability.

Contribution

It introduces an MoE-enabled GAI framework for network optimization in communication security and demonstrates its effectiveness through a cooperative jamming case study.

Findings

MoE helps GAI overcome computational limitations

The framework improves security in cooperative jamming scenarios

Experimental results confirm enhanced communication security

Abstract

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model's applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework's effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security.