Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is e...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on wireless communications Vol. 20; no. 6; pp. 3710 - 3722
Main Authors:	Vu, Thang X., Chatzinotas, Symeon, Nguyen, Van-Dinh, Hoang, Dinh Thai, Nguyen, Diep N., Renzo, Marco Di, Ottersten, Bjorn
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms antenna selection Antennas Artificial neural networks Combinatorial analysis Complexity Convexity Electronics Engineering Sciences Iterative algorithms Iterative methods Machine learning Machine learning algorithms Multiuser neural networks Optimization Precoding Radio frequency Real time operation Receiving antennas Spatial diversity successive convex optimization System effectiveness Training Transmitting antennas
ISSN:	1536-1276, 1558-2248, 1558-2248
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!