Year: 2026
Type: article
Abstract: 5G mmWave networks rely on directional beamforming to ensure high-bandwidth connectivity, but the initial beam alignment process is vulnerable to beam-stealing attacks. In this scenario, an adversary transmits forged synchronization signals to hijack the receiver's connection, potentially leading to denial of service. This paper analyzes these threats and proposes a physical-layer detection mechanism based on radio frequency fingerprinting. Using a 60 GHz laboratory test-bed, we emulate legitimate and malicious transmission scenarios to evaluate specific hardware impairments. We investigate two primary detection metrics: power amplifier nonlinearities, analyzed via their Amplitude Modulation to Amplitude Modulation (AM/AM) characteristics, and local oscillator stability, quantified by carrier frequency offset drift. Experimental results demonstrate that these metrics can successfully distinguish among different transmitting devices based on their saturation levels and frequency stability profiles. The study confirms that lightweight radio frequency (RF) fingerprinting is a viable solution for hardening 5G beam management against spoofing. Keywords: Beam-stealing, 5G, mmWave, RF fingerprinting, nonlinearities, CFO
Source: Radioengineering
Authors: M. Kousal, J. Vychodil, M. Ali, R. Marsalek
Institution: Brno University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $431
Awards: G2449101914, Robust 5G networks
OpenAlex ID: W7129061903
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