解锁跨场景物理层安全：基于生成扩散模型的专家混合框架
Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models
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中文总结 AI 辅助
针对6G多场景物理层安全挑战，提出基于生成扩散模型的专家混合框架，通过场景专家、门控网络和注意力组合器实现跨场景自适应安全传输，仿真验证其保密速率接近最优并优于单模型。
中文摘要 AI 辅助
未来的6G网络预计将在多样化的环境中集成大量的无线服务，这对信息安全构成了重大挑战。传统上，优化总是需要重新计算，而学习策略往往泛化能力差，因此无法在广泛场景覆盖下提供安全保障。在本文中，我们提出了一种自适应且鲁棒的学习框架，利用专家混合（MoE）架构来实现跨场景的物理层安全保证。具体来说，我们首先选择几个代表性场景，并建立基于场景的生成扩散模型（GDM）专家，用于带人工噪声的安全传输波束成形。专家的扩散特性学习了安全策略解空间的整体概率分布，而基于Transformer的去噪过程增强了在不同网络配置下的泛化能力。然后，构建了一个轻量级门控网络，通过工程化信道特征来识别场景，并选择最相关的专家。最后，引入了一个基于注意力的组合器，综合来自顶级专家的安全提案，以生成高保真度的安全策略，覆盖未见过的场景。仿真结果表明，所提出的基于GDM的MoE框架能够准确识别场景并正确选择专家，在连续的无线场景中保持接近最优的保密速率，并优于传统的单模型范式。
英文摘要
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.
发表机构
- Shenzhen Research Institute of Northwestern Polytechnical University（西北工业大学深圳研究院）
- Northwestern Polytechnical University（西北工业大学）
- Pengcheng Laboratory（鹏城实验室）
- University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
