Y
Yan Chen
Researcher at Northwestern University
Publications - 521
Citations - 24026
Yan Chen is an academic researcher from Northwestern University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 67, co-authored 415 publications receiving 21798 citations. Previous affiliations of Yan Chen include AT&T Labs & Huawei.
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Full-Duplex Cellular Networks: It Works!
TL;DR: This article will shed light on what scenarios FD communications should be applied in under the current technology maturity, how bad the performance will be if the authors do nothing to deal with the newly introduced interference, and how much improvement could be achieved after applying advanced solutions.
Posted Content
Protocol design and stability/delay analysis of half-duplex buffered cognitive relay systems
TL;DR: The buffer gain is shown analytically to improve the stability region and average end-to-end delay performance of the cognitive relay system and to propose buffered decode-and-forward (BDF) protocol.
Proceedings ArticleDOI
Advanced Grant-Free Transmission for Small Packets URLLC Services
TL;DR: It can be demonstrated from the evaluation results that the proposed grant-free transmission schemes are able to work together and accomplish the latency and the reliability requirements for the URLLC services, showing significant gains over the basic grant- free transmission design.
Patent
Method and Apparatus for Transmitting Indication Information
TL;DR: In this article, the authors present a method and an apparatus for transmitting codebook indication information in uplink uplink data streams, which includes determining, according to one or more codebooks, a first codebook to be used by a terminal device to send an uplink datacenter.
Proceedings ArticleDOI
RVAE-ABFA : Robust Anomaly Detection for HighDimensional Data Using Variational Autoencoder
TL;DR: Experimental results on several benchmark datasets show that the proposed method significantly outperforms state-of-the-art unsupervised anomaly detection methods and is more robust when training data is contaminated.