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

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.

Authors 5

Parsa Rajabi, Mohammad Mirzaee, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi

Specification

Official page: arxiv.org/abs/2609.09475
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2609.09475
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: eess.SP, cs.AI, cs.SY, eess.SY
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2609.09475
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: eess.SP
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

2 h ago

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None

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Authors: Parsa Rajabi, Mohammad Mirzaee, Mohammad Reza Abedi

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Claim history for Primary category
Value	Valid from → to	Status	Source	Confidence	Extractor
eess.SP	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperDistributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning
New paper: Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	1

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