LinkPrint – Physical-Layer RF Fingerprinting Authentication for SatCom

Sat, 01 Feb 2025 00:00:00 +0000

European Space Agency

Role: Co-Lead (SnT – SIGCOM / 6GSPACE Lab)
Funding: European Space Agency (ESA ARTES Future Preparation – FP)
Official Page: uni.lu/snt-en/research-projects/linkprint/

Executive Summary

LinkPrint is a European Space Agency (ESA ARTES FP) research project focused on physical-layer authentication of satellite payloads and ground terminals using Radio Frequency Fingerprinting Identification (RFFI). By exploiting unique, immutable analog hardware impairments in transmitter front-ends (such as I/Q imbalance, phase noise, and power amplifier non-linearities), LinkPrint detects spoofing and unauthorized access before higher-layer cryptographic processing.

Key Objectives & Contributions

Deep-Learning & Self-Supervised RFFI: Multi-branch Convolutional Neural Networks (CNNs) and self-supervised representation learning over raw baseband I/Q satellite captures.
Channel-Robust Feature Extraction: Separating transmitter-specific hardware signatures from dynamic LEO/MEO/GEO Doppler shifts and atmospheric fading.
Over-the-Satellite Validation: Live experimental evaluation using the 6GSPACE Lab multi-band ground station and USRP SDR recording infrastructure.

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XTRUST-6G – AI-Driven Physical Layer Security & O-RAN Zero-Trust (Pilot 4)

Wed, 01 May 2024 00:00:00 +0000

Horizon Europe SNS JU

Role: Pilot 4 Lead (SnT – SIGCOM / 6GSPACE Lab)
Funding: European Commission (Horizon Europe Smart Networks and Services Joint Undertaking – SNS JU)
Official Page: uni.lu/snt-en/research-projects/xtrust-6g/

Executive Summary

Within the XTRUST-6G Horizon Europe SNS JU project, the 6GSPACE Lab at SnT leads Pilot 4 on AI-Driven Physical Layer Security and Zero-Trust O-RAN Resilience. The activity develops real-time AI/ML defenses at the radio physical layer and Open RAN Near-Real-Time RAN Intelligent Controller (Near-RT RIC) to protect 6G terrestrial, UAV, and non-terrestrial links against smart jamming and adversarial waveforms.

Key Objectives & Contributions

Adversarial-Resilient Neural Receivers: Deep-learning physical layer receivers hardened against protocol-aware and adversarial RF attacks.
Near-RT RIC Security xApps: Real-time interference classification, physical-layer key generation, and automated spectrum/beam mitigation via O-RAN E2 interfaces.

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Physical Layer Security | Dr. Jorge Querol

LinkPrint – Physical-Layer RF Fingerprinting Authentication for SatCom

Executive Summary

Key Objectives & Contributions

XTRUST-6G – AI-Driven Physical Layer Security & O-RAN Zero-Trust (Pilot 4)

Executive Summary

Key Objectives & Contributions