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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on wireless communications Ročník 20; číslo 6; s. 3710 - 3722
Hlavní autoři:	Vu, Thang X., Chatzinotas, Symeon, Nguyen, Van-Dinh, Hoang, Dinh Thai, Nguyen, Diep N., Renzo, Marco Di, Ottersten, Bjorn
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!