  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.
