arXiv subjects

Parsa Rajabi

Publications and source records attributed to Parsa Rajabi.

3 recordsLinked to original sources

arXiv2026

Group Physical-Layer Authentication and Confidential Beamforming in Non-Terrestrial Networks

Coordinated high-altitude platform station (HAPS) networks need to authenticate group responses and protect private data under shared power constraints. This paper studies joint group physical-layer authentication (PLA) and confidential beamforming using a fresh secret tag superimposed on private data. For received attacks independent of the current tag, we derive exact finite-block expressions for false acceptance and legitimate rejection. These expressions translate authentication error targets into a required tag signal-to-interference-plus-noise ratio (SINR). We establish a nonzero asymptotic tag-energy floor and analyze residual frequency offset and peak-power restrictions. The legitimate receiver removes the tag by orthogonal projection, which discards one temporal dimension. For colluding eavesdroppers, we identify the spatial coverage condition under which artificial noise (AN) keeps their SINR bounded as data and AN powers increase together. We jointly optimize the private beams, tag beam, and AN covariance to minimize transmit power under authentication, secrecy, and per-HAPS power constraints. The method combines semidefinite relaxation (SDR), exact private-beam rank reduction, and feasible successive convex approximation. A corrected dual lower bound measures the remaining optimality gap. Numerical results show that increasing the tag length from 16 to 64 symbols reduces the required tag SINR by 7.16 dB. Joint beam design reduces mean transmit power by 27.4% relative to an optimized fixed-tag-direction baseline. The authentication guarantee requires fresh secret tags and excludes relays of the current response.

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arXiv2026

Joint Deployment and Resource Management for Group-Tag-Based Physical-Layer Authentication and Collaborative Communication in NTN

Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) has primarily been studied from a security perspective, while its system-level integration with communication remains less explored. We propose the secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a system-level formulation for the joint management of collaborative group-tag and private-data delivery in NTN, where high-altitude platform station (HAPS) placement, user association, and transmission powers are jointly selected under service and secrecy constraints. Group-level authentication tags and user data are collaboratively transmitted by multiple HAPSs, with artificial noise (AN) mitigating information leakage. We jointly optimize HAPS placement, user association, and power allocation to balance deployment regularity, tag distribution, and user-rate fairness under secrecy, quality-of-service (QoS), power, and coverage constraints. The problem is addressed using a repair-based cross-entropy method (RCEM) and a graph-aware advantage actor-critic (A2C) algorithm (GA2C). Under colluding and non-colluding eavesdropper models, GA2C improves average secrecy spectral efficiency (SSE) by 135--139% over its single-connect baseline and 21.1--23.2% over its baseline without AN. Across ten independent random seeds, GA2C achieves approximately 23.4% and 17.9% higher mean per-user SSE under non-colluding and colluding eavesdropping, respectively, and a 16.2\% higher mean private-rate Jain's index than RCEM. These results quantify the gains of multi-node connectivity and AN within the proposed framework under the evaluated assumptions.

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arXiv2026

Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning

Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade conventional learning methods. Existing PLA schemes often rely on single features or generalize poorly to unseen environments. This paper proposes a secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a causal meta-learning framework for distributed PLA (DPLA) in NTNs. First, we design a multi-feature fingerprint that combines spatial, angular, combiner, subspace, and Doppler-delay features. The fingerprint is adaptive and distributed, as it fuses heterogeneous physical-layer features and measurements from multiple aerial nodes. Second, we formulate a structural causal model (SCM) to capture the relations among design choices, environmental factors, extracted features, and authentication outcomes. Third, we develop a model-agnostic meta-learning (MAML) strategy with invariant risk minimization (IRM) and causal consistency regularization for fast adaptation to unseen NTN environments with few labeled samples. Fourth, we propose a two-stage authentication scheme that performs local recognition and activates time-difference-of-arrival (TDOA) localization with a graph attention (GAT) network only when needed, which reduces backhaul overhead. Simulations show that SAFA-MZ achieves 92% accuracy and 96% AUC, outperforming centralized deep learning and single-feature baselines across diverse environments.

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