Channel-Aware Conditional Diffusion Model for Secure MU-MISO Communications

TL;DR

This paper introduces a novel channel-aware conditional diffusion model for secure multi-user MISO wireless communications, enabling real-time, adaptive, and superior physical layer security strategies.

Contribution

It reformulates security optimization as a conditional generative process using diffusion models, integrating channel information for improved security performance.

Findings

Achieves superior secrecy performance compared to baselines.

Converges reliably in simulation tests.

Enhances security by fine-tuning with secrecy rate objectives.

Abstract

While information securityis a fundamental requirement for wireless communications, conventional optimization based approaches often struggle with real-time implementation, and deep models, typically discriminative in nature, may lack the ability to cope with unforeseen scenarios. To address this challenge, this paper investigates the design of legitimate beamforming and artificial noise (AN) to achieve physical layer security by exploiting the conditional diffusion model. Specifically, we reformulate the security optimization as a conditional generative process, using a diffusion model to learn the inherent distribution of near-optimal joint beamforming and AN strategies. We employ a U-Net architecture with cross-attention to integrate channel state information, as the basis for the generative process. Moreover, we fine-tune the trained model using an objective incorporating the sum secrecy rate such that the security performance is further enhanced. Finally, simulation results validate the learning process convergence and demonstrate that the proposed generative method achieves superior secrecy performance across various scenarios as compared with the baselines.