<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RF Fingerprinting | Dr. Jorge Querol</title><link>https://jorge.querol.org/tags/rf-fingerprinting/</link><atom:link href="https://jorge.querol.org/tags/rf-fingerprinting/index.xml" rel="self" type="application/rss+xml"/><description>RF Fingerprinting</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-US</language><lastBuildDate>Sat, 01 Feb 2025 00:00:00 +0000</lastBuildDate><image><url>https://jorge.querol.org/media/sharing.png</url><title>RF Fingerprinting</title><link>https://jorge.querol.org/tags/rf-fingerprinting/</link></image><item><title>LinkPrint – Physical-Layer RF Fingerprinting Authentication for SatCom</title><link>https://jorge.querol.org/project/linkprint/</link><pubDate>Sat, 01 Feb 2025 00:00:00 +0000</pubDate><guid>https://jorge.querol.org/project/linkprint/</guid><description>&lt;div class="mb-4"&gt;
&lt;span class="badge-agency badge-esa"&gt;European Space Agency&lt;/span&gt;
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&lt;p&gt;&lt;strong&gt;Role:&lt;/strong&gt; Co-Lead (SnT – SIGCOM / 6GSPACE Lab)&lt;br&gt;
&lt;strong&gt;Funding:&lt;/strong&gt; European Space Agency (&lt;strong&gt;ESA ARTES Future Preparation – FP&lt;/strong&gt;)&lt;br&gt;
&lt;strong&gt;Official Page:&lt;/strong&gt; &lt;a href="https://www.uni.lu/snt-en/research-projects/linkprint/" target="_blank" rel="noopener"&gt;uni.lu/snt-en/research-projects/linkprint/&lt;/a&gt;&lt;/p&gt;
&lt;h3 id="executive-summary"&gt;Executive Summary&lt;/h3&gt;
&lt;p&gt;&lt;strong&gt;LinkPrint&lt;/strong&gt; is a European Space Agency (&lt;strong&gt;ESA ARTES FP&lt;/strong&gt;) research project focused on physical-layer authentication of satellite payloads and ground terminals using &lt;strong&gt;Radio Frequency Fingerprinting Identification (RFFI)&lt;/strong&gt;. 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.&lt;/p&gt;
&lt;h3 id="key-objectives--contributions"&gt;Key Objectives &amp;amp; Contributions&lt;/h3&gt;
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&lt;li&gt;&lt;strong&gt;Deep-Learning &amp;amp; Self-Supervised RFFI&lt;/strong&gt;: Multi-branch Convolutional Neural Networks (CNNs) and self-supervised representation learning over raw baseband I/Q satellite captures.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Channel-Robust Feature Extraction&lt;/strong&gt;: Separating transmitter-specific hardware signatures from dynamic LEO/MEO/GEO Doppler shifts and atmospheric fading.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Over-the-Satellite Validation&lt;/strong&gt;: Live experimental evaluation using the &lt;a href="https://6gspacelab.uni.lu" target="_blank" rel="noopener"&gt;6GSPACE Lab&lt;/a&gt; multi-band ground station and USRP SDR recording infrastructure.&lt;/li&gt;
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