Mixture of experts model improves wireless security across scenarios
Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models
Information TheoryMachine Learning
Summary
Securing wireless networks is hard because different places and setups need different ways to protect information. The authors created a smart system that learns from a few example scenarios to handle many kinds of wireless environments. Their system uses multiple specialized models that each focus on one scenario, then combines their advice to protect messages even in places it hasn’t seen before. This approach keeps wireless communication safe almost as well as the best dedicated method for each scenario.
What this means in practice
- •For wireless network engineers: Design secure transmission methods that adapt to diverse and changing wireless settings to maintain privacy without retraining for each environment.
- •For mobile network operators: Implement security frameworks that automatically recognize network conditions and select optimal protection strategies to improve service security coverage.
Authors
Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du, Li Sun, Zhu Han
Abstract
The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which are thus incapable for the security provisioning with wide scenario coverage. In this paper, we propose an adaptive and robust learning framework that leverages a mixture-of-experts (MoE) architecture to achieve cross-scenario physical layer security guarantee. Specifically, we first select a few representative scenarios and establish the scenario-specific generative diffusion model (GDM)-based experts for secure transmission beamforming with artificial noise. The diffusion nature of experts learns the overall probability distribution of security strategy solution landscape and the Transformer-based denoising process enhances the ability to generalize across varying network configurations. Then, a lightweight gating network is constructed to identify the scenarios by engineering the channel features and select the most relevant experts. Finally, an attention-based combiner is introduced to synthesize the security proposals from the top-rated experts to produce a high-fidelity security strategy to cover the unseen scenarios. Simulation results demonstrate that the proposed GDM-based MoE framework can accurately recognize the scenarios and properly select the experts, maintaining near-optimal secrecy rates across a continuum of wireless scenarios and outperforming traditional single-model paradigms.