<?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 | Δρ. Ευάγγελος Βλάχος</title><link>https://evlachos.space/el/tags/signal-detection/</link><atom:link href="https://evlachos.space/el/tags/signal-detection/index.xml" rel="self" type="application/rss+xml"/><description>Signal Detection</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>el</language><lastBuildDate>Thu, 02 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://evlachos.space/media/icon_hu_2beab1606e98fa9.png</url><title>Signal Detection</title><link>https://evlachos.space/el/tags/signal-detection/</link></image><item><title>Paper Submitted to IEEE Wireless Communications Letters</title><link>https://evlachos.space/el/post/papers/paper-submitted-wcl26-geometry-otfs/</link><pubDate>Thu, 02 Apr 2026 00:00:00 +0000</pubDate><guid>https://evlachos.space/el/post/papers/paper-submitted-wcl26-geometry-otfs/</guid><description>&lt;p>We submitted a new paper to &lt;strong>IEEE Wireless Communications Letters&lt;/strong>:&lt;/p>
&lt;h3 id="geometry-aware-regularization-for-deep-unfolded-mimo-otfs-detection">Geometry-Aware Regularization for Deep-Unfolded MIMO-OTFS Detection&lt;/h3>
&lt;p>&lt;em>Nikolaos Ntavanelos and Evangelos Vlachos&lt;/em>&lt;/p>
&lt;p>MIMO-OTFS systems suffer from non-uniform scaling across symbol components due to spatial and fractional Doppler–Delay coupling. This work introduces a &lt;strong>geometry-aware coordinate-wise regularizer&lt;/strong> within a deep-unfolded Conjugate Gradient detector to exploit this structure.&lt;/p>
&lt;p>Key contributions:&lt;/p>
&lt;ul>
&lt;li>&lt;strong>Gram matrix analysis&lt;/strong>: We prove that all diagonal variation in the MIMO-OTFS Gram matrix arises exclusively from inter-path coupling, analytically characterizing its non-uniform structure.&lt;/li>
&lt;li>&lt;strong>Deep-unfolded CG detector&lt;/strong>: A geometry-aware regularizer is embedded directly into the CG linear system, with per-component weights learned in a data-driven manner.&lt;/li>
&lt;li>&lt;strong>Robust performance&lt;/strong>: The proposed detector outperforms low-complexity baselines and approaches heavier neural architectures at medium-to-high SNR under both low- and high-mobility scenarios.&lt;/li>
&lt;/ul></description></item></channel></rss>