Deep Complex Network Architecture for Multi-User Physical Layer Authentication in Wireless Communication

doi:10.1109/LCOMM.2025.3645190

Deep Complex Network Architecture for Multi-User Physical Layer Authentication in Wireless Communication

2026-01-01

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DOI：10.1109/LCOMM.2025.3645190

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Physical Layer Authentication (PLA) is a promising strategy for wireless security. Most existing PLA schemes have relied on real-valued neural networks, where complex-valued channel impulse response (CIR) is processed by separating the real and imaginary components into dual-channel inputs. This conversion disrupts the inherent coupling between magnitude and phase, thereby constraining authentication accuracy. Importantly, the spatial position of each user inherently serves as a reliable identity fingerprint. In this letter, a complex-valued network-based multi-task learning (CVN-MTL) model is proposed for multi-user authentication. By leveraging the spatiotemporal characteristics of both CIR and position, the CVN-MTL model simultaneously performs user authentication and fine-grained localization. Experiment results show that the CVN-MTL model performs superiority on authentication performance and is robust to different communication scenarios.

Keywords:

Multi-user physical layer authentication (PLA)

complex-valued network (CVN)

channel impulse response (CIR)

location

Deep Complex Network Architecture for Multi-User Physical Layer Authentication in Wireless Communication

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