# Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models

arXiv: https://arxiv.org/abs/2609.26598 · HTML: https://mlsanity.com/paper/2609.26598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Placed in

- Mixture-of-experts

## Builds on (most cited first, 20 of 20)

- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Generative AI based Secure Wireless Sensing for ISAC Networks](https://mlsanity.com/paper/2408.11398/) (2024)
- [Generative AI for Secure Physical Layer Communications: A Survey](https://mlsanity.com/paper/2402.13553/) (2024)
- [Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08914/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Generative AI for Advanced UAV Networking](https://mlsanity.com/paper/2404.10556/) (2024)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Context-Aware Security for 6G Wireless The Role of Physical Layer Security](https://mlsanity.com/paper/2101.01536/) (2021)
- [Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts](https://mlsanity.com/paper/2405.04198/) (2024)
- [Safeguarding Next Generation Multiple Access Using Physical Layer Security Techniques: A Tutorial](https://mlsanity.com/paper/2403.16477/) (2024)
- [PAST-AI: Physical-layer Authentication of Satellite Transmitters via Deep Learning](https://mlsanity.com/paper/2010.05470/) (2020)
- [Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing](https://mlsanity.com/paper/2405.10521/) (2024)
- [Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications](https://mlsanity.com/paper/2504.01446/) (2025)
- [Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice](https://mlsanity.com/paper/2506.09807/) (2025)
- [Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments](https://mlsanity.com/paper/2211.03065/) (2022)
- [Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks](https://mlsanity.com/paper/2409.15695/) (2024)
- [Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation](https://mlsanity.com/paper/2411.04423/) (2024)
- [Enhanced Physical Layer Security for Full-duplex Symbiotic Radio with AN Generation and Forward Noise Suppression](https://mlsanity.com/paper/2402.12747/) (2024)
- [Movable Frequency Diverse Array for Wireless Communication Security](https://mlsanity.com/paper/2407.21157/) (2024)
- [Active RIS-Aided Anti-Jamming Wireless Communications: A Stackelberg Game Perspective](https://mlsanity.com/paper/2512.17335/) (2025)

## Abstract (arXiv)

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.