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Scalable Signal Processing in Cloud Radio Access Networks: SpringerBriefs in Electrical and Computer Engineering

Autor Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan
en Limba Engleză Paperback – 27 apr 2019
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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Specificații

ISBN-13: 9783030158835
ISBN-10: 3030158837
Pagini: 90
Ilustrații: XI, 100 p. 30 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.17 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Electrical and Computer Engineering

Locul publicării:Cham, Switzerland