scispace - formally typeset
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.

Papers
More filters
Posted Content

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.