Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts (2405.04198v1)
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.
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- Changyuan Zhao (17 papers)
- Hongyang Du (154 papers)
- Dusit Niyato (671 papers)
- Jiawen Kang (204 papers)
- Zehui Xiong (177 papers)
- Dong In Kim (168 papers)
- Xuemin (104 papers)
- Shen (108 papers)
- Khaled B. Letaief (209 papers)