Distributed authentication boosts secure satellite network transmission
Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning
Artificial Intelligence
Summary
Non-terrestrial networks like satellites often struggle to verify users securely and efficiently while keeping data private from eavesdroppers. This paper presents a new method that combines user authentication with data transmission, using group tags and noise to protect against spying. The system also groups users based on how important their information is and optimizes signal power and network setup to improve security and efficiency. The authors tested their approach against others and found it significantly increases secure data rates.
What this means in practice
- •For satellite network operators: Enhance secure user verification and data transmission efficiency by integrating collaborative authentication with rate-splitting methods under practical eavesdropping threats.
- •For wireless communication infrastructure teams: Deploy adaptive multi-antenna techniques and AI-driven optimization for secure, privacy-aware wireless links in dynamic aerial or space-based networks.
Authors
Parsa Rajabi, Mohammad Mirzaee, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi
Abstract
Existing physical-layer authentication (PLA) schemes for non-terrestrial networks (NTNs) often rely on single-anchor verification, lack joint authentication-transmission design, and ignore tag privacy leakage under eavesdropping. In this paper, we consider passive, location-aware, static eavesdroppers without access to legitimate channel state information (CSI). Under this threat model, we propose secure adaptive federated authentication for multi-zone NTN systems (SAFA-MZ) that maximizes secrecy spectral efficiency (SSE) while ensuring authentication reliability, power limits, and coverage constraints. The main idea is to embed group-level authentication tags into a collaborative multi-layer rate-splitting multiple access (RSMA) transmission structure. Private and common signals are jointly beamformed, artificial noise (AN) is used to reduce information leakage, and group differential privacy (GDP) protects tag information against inference attacks. In addition, users are grouped by semantic priority to allocate SSE based on information importance. We formulate a joint SSE maximization problem under authentication reliability and probabilistic secrecy constraints, optimizing high-altitude platform station (HAPS) placement, user association, and RSMA power allocation. The resulting problem is solved using a repair-based cross-entropy method (RCEM) and a graph-aware advantage actor-critic algorithm (GA2C). RCEM scales quadratically with the number of users, while GA2C scales linearly and achieves scalable, low-latency inference. Simulation results under both colluding and non-colluding eavesdroppers show that the proposed method improves average SSE by up to 135% over single-connect transmission and 21% over the scheme without AN. These results confirm SAFA-MZ offers a scalable and secure solution for dynamic NTN environments.