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Scalable signal processing in cloud ...

Fan, Congmin.

Scalable signal processing in cloud radio access networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scalable signal processing in cloud radio access networksby Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan.
Author:	Zhang, Ying-Jun Angela.
other author:	Fan, Congmin.
Published:	Cham :Springer International Publishing :2019.
Description:	xi, 100 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Signal processing.
Online resource:	https://doi.org/10.1007/978-3-030-15884-2
ISBN:	9783030158842$q(electronic bk.)

Scalable signal processing in cloud radio access networks
Zhang, Ying-Jun Angela.

Scalable signal processing in cloud radio access networks[electronic resource] /by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan. - Cham :Springer International Publishing :2019. - xi, 100 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

This Springerbreif introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs. The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity. Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where 'scalable' means that the computational and implementation complexities do not grow rapidly with the network size. This Springerbrief will be target researchers and professionals working in the Cloud Radio Access Network (C-Ran) field, as well as advanced-level students studying electrical engineering.

ISBN: 9783030158842$q(electronic bk.)

Standard No.: 10.1007/978-3-030-15884-2doiSubjects--Topical Terms:

182382
Signal processing.

LC Class. No.: TK5102.9 / .Z436 2019

Dewey Class. No.: 621.3822

Scalable signal processing in cloud radio access networks
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490 1 $a SpringerBriefs in electrical and computer engineering, $x 2191-8112
520 $a This Springerbreif introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs. The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity. Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where 'scalable' means that the computational and implementation complexities do not grow rapidly with the network size. This Springerbrief will be target researchers and professionals working in the Cloud Radio Access Network (C-Ran) field, as well as advanced-level students studying electrical engineering.
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