# Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning

arXiv: https://arxiv.org/abs/2609.09475 · HTML: https://mlsanity.com/paper/2609.09475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Generative AI for Secure Physical Layer Communications: A Survey](https://mlsanity.com/paper/2402.13553/) (2024)
- [Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning](https://mlsanity.com/paper/2402.07818/) (2024)
- [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022)
- [Physical Layer Authentication and Security Design in the Machine Learning Era](https://mlsanity.com/paper/2305.09748/) (2023)
- [PAST-AI: Physical-layer Authentication of Satellite Transmitters via Deep Learning](https://mlsanity.com/paper/2010.05470/) (2020)
- [A Survey of Machine Learning-based Physical-Layer Authentication in Wireless Communications](https://mlsanity.com/paper/2411.09906/) (2024)
- [Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice](https://mlsanity.com/paper/2506.09807/) (2025)
- [Graph Neural Network based Active and Passive Beamforming for Distributed STAR-RIS-Assisted Multi-User MISO Systems](https://mlsanity.com/paper/2405.01979/) (2024)
- [Generative AI for Physical-Layer Authentication](https://mlsanity.com/paper/2504.18175/) (2025)
- [Physical Layer Challenge-Response Authentication between Ambient Backscatter Devices](https://mlsanity.com/paper/2506.18767/) (2025)
- [Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices](https://mlsanity.com/paper/2603.19972/) (2026)
- [APEG: Adaptive Physical Layer Authentication with Channel Extrapolation and Generative AI](https://mlsanity.com/paper/2603.21923/) (2026)
- [Physical Layer Authentication for Non-Coherent Massive SIMO-Enabled Industrial IoT Communications](https://mlsanity.com/paper/2005.12686/) (2020)
- [Integrated Sensing, Communication, and Powering (ISCAP) for IoT: A Joint Beamforming Design](https://mlsanity.com/paper/2503.08557/) (2025)
- [CovertAuth: Joint Covert Communication and Authentication in MmWave Systems](https://mlsanity.com/paper/2507.08904/) (2025)

## Abstract (arXiv)

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.