<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Z. He | NIULAB</title><link>https://souig.github.io/authors/z.-he/</link><atom:link href="https://souig.github.io/authors/z.-he/index.xml" rel="self" type="application/rss+xml"/><description>Z. He</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en</language><lastBuildDate>Mon, 01 Jan 2024 00:00:00 +0000</lastBuildDate><image><url>https://souig.github.io/media/icon_hu_7d2a9e6dbe042ff1.png</url><title>Z. He</title><link>https://souig.github.io/authors/z.-he/</link></image><item><title>, Learning-Based Remote Channel Inference: Feasibility Analysisand Case Study</title><link>https://souig.github.io/publication/learning-based-068/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://souig.github.io/publication/learning-based-068/</guid><description/></item><item><title>Inferring Remote Channel State Infor- mation: Cramer-Rao Lower Boundand Deep Learning Implementation</title><link>https://souig.github.io/publication/inferring-remote-198/</link><pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate><guid>https://souig.github.io/publication/inferring-remote-198/</guid><description/></item></channel></rss>