arXiv subjects

Reza Ghasemi Alavicheh

Publications and source records attributed to Reza Ghasemi Alavicheh.

4 recordsLinked to original sources

arXiv2026

Secure Distortion-Aware GNN Precoding under Active Eavesdropping in Cell-Free Networks

This paper considers a cell-free (CF) network under active eavesdropping. We show that nonlinear power amplifier (PA) distortion, together with the per-access point (AP) large-scale-gain fingerprint of the attack, can be exploited across cooperating APs to enhance physical layer security (PLS). Classical zero-forcing (ZF)-artificial noise (AN) precoders rely on a linear transmitter and an unbiased estimate, and thus cannot exploit either. We propose a distortion aware graph neural network (GNN) precoder that designs the signal beams at cooperating APs and exploits both the PA distortion and the per-AP fingerprint to enhance the secrecy rate, combined with a null-space AN block to form the GNN-AN scheme. The GNN is trained on channel estimates biased by an uplink pilot-spoofing attack to maximise the average per-user secrecy rate. Simulations across different numbers of APs show uniformly higher outage secrecy rates than ZF-AN under attack, reaching gains of 15% at the 1% outage level and 8.9% at the 5% outage level at L = 32 APs, input back-off (IBO) = -1 dB, and attacker-to-targeted user pilot power ratio of 0.5, under the same signal/AN power split.

eess.SP↗

arXiv2026

When Distortion Helps: Secure GNN Precoding with Nonlinear Power Amplifiers

Physical layer security (PLS) provides information-theoretic protection against eavesdropping. While existing techniques assume ideal linear transmitters, power amplifiers (PAs) in practice introduce nonlinear distortion, typically considered detrimental to signal quality. This paper demonstrates that such distortion can instead be exploited as a security asset by redirecting it toward eavesdroppers, particularly in the power-efficient PA saturation regime. To this end, we propose a graph neural network (GNN)-based precoding framework for multi-user multiple-input single-output (MISO) wiretap channels that maximizes the sum secrecy rate by exploiting PA nonlinearity. Since the resulting optimization is highly non-convex, classical methods are intractable. The GNN instead learns precoding strategies directly from legitimate users' channel data, requiring neither eavesdropper channel state information (CSI) nor dedicated artificial noise (AN) power allocation. For this, the Bussgang decomposition and a high-order polynomial PA model provide an analytical secrecy rate as the training objective. At 22 dB signal-to-noise ratio (SNR) under severe PA saturation with input back-off (IBO) $= -1$ dB, the proposed GNN achieves 39.89% and 35.26% higher sum secrecy rate over maximum ratio transmission (MRT) and zero-forcing (ZF), respectively, 17.99% over AN-aided MRT and 8.67% over AN-aided ZF, with 58.13-75.31% lower standard deviation across all baselines.

eess.SP↗

arXiv2025

Leveraging Power Amplifier Distortion for Physical Layer Security

This paper introduces a new approach to physical layer security (PLS) by leveraging power amplifier (PA) nonlinear distortion through distortion-aware precoding. While some conventional PLS techniques inject artificial noise orthogonal to legitimate channels, we demonstrate that inherent PA nonlinearities typically considered undesirable can be exploited to enhance security. The zero 3rd order (Z3RO) precoder applies a negative polarity to several antennas to cancel the PA distortion at the user location, resulting in distortion being transmitted in non-user locations. Redirecting the distortion to non-user locations creates interference for potential eavesdroppers, lowering their signal-to-noise-and-distortion ratio (SNDR). Numerical simulations reveal that the Z3RO precoder achieves up to a $2.5\times$ improvement in secrecy rate compared to conventional maximum ratio transmission (MRT) precoding under a $10\%$ outage probability, SNR of $32$ dB and $-5$ dB input back-off (IBO) where the PAs enter the saturation regime.

eess.SP↗

arXiv2024

Integrated Access and Backhaul (IAB) in Low Altitude Platforms

In this paper, we explore the problem of utilizing Integrated Access and Backhaul (IAB) technology in Non-Terrestrial Networks (NTN), with a particular focus on aerial access networks. We consider an Uncrewed Aerial Vehicle (UAV)-based wireless network comprised of two layers of UAVs: (a) a lower layer consisting a number of flying users and a UAV Base Station (BS) that provides coverage for terrestrial users and, (b) an upper layer designated to provide both wireless access for flying users and backhaul connectivity for UAV BS. By adopting IAB technology, the backhaul and access links collaboratively share their resources, enabling aerial backhauling and the utilization of the same infrastructure and frequency resources for access links. A sum-rate maximization problem is formulated by considering aerial backhaul constraints to optimally allocate the frequency spectrum between aerial and terrestrial networks. We decompose the resulting non-convex optimization problem into two sub-problems of beamforming and spectrum allocation and then propose efficient solutions for each. Numerical results in different scenarios yield insightful findings about the effectiveness of using the IAB technique in aerial networks.

cs.IT↗