<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Signal Detection | Dr. Evangelos Vlachos</title><link>https://vlachos.science/tags/signal-detection/</link><atom:link href="https://vlachos.science/tags/signal-detection/index.xml" rel="self" type="application/rss+xml"/><description>Signal Detection</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en</language><lastBuildDate>Mon, 24 Aug 2026 00:00:00 +0000</lastBuildDate><image><url>https://vlachos.science/media/icon_hu_2beab1606e98fa9.png</url><title>Signal Detection</title><link>https://vlachos.science/tags/signal-detection/</link></image><item><title>Geometry-Aware Regularization for Deep-Unfolded MIMO-OTFS Detection</title><link>https://vlachos.science/publication/ntavanelos-geometry-2026/</link><pubDate>Mon, 24 Aug 2026 00:00:00 +0000</pubDate><guid>https://vlachos.science/publication/ntavanelos-geometry-2026/</guid><description>&lt;h2 id="key-contributions">Key Contributions&lt;/h2>
&lt;ul>
&lt;li>&lt;strong>Gram matrix analysis&lt;/strong>: Analytical characterization of the non-uniform diagonal structure of the MIMO-OTFS Gram matrix, proving that all diagonal variation arises exclusively from inter-path coupling.&lt;/li>
&lt;li>&lt;strong>Geometry-aware regularizer&lt;/strong>: A coordinate-wise regularizer embedded into a deep-unfolded Conjugate Gradient detector, with per-component weights learned in a data-driven manner.&lt;/li>
&lt;li>&lt;strong>Performance gains&lt;/strong>: The proposed detector outperforms low-complexity baselines and approaches heavier neural architectures at medium-to-high SNR, with improved robustness in high-mobility scenarios.&lt;/li>
&lt;/ul></description></item></channel></rss>