Scalable Signal Processing in Cloud Radio Access Networks [electronic resource] / by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan.
By: Zhang, Ying-Jun Angela [author.]
.
Contributor(s): Fan, Congmin [author.] | Yuan, Xiaojun [author.] | SpringerLink (Online service).
Material type: 
BookSeries: SpringerBriefs in Electrical and Computer Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XI, 100 p. 30 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030158842.Subject(s): Wireless communication systems | Mobile communication systems | Signal processing | Telecommunication | Wireless and Mobile Communication | Signal, Speech and Image Processing | Communications Engineering, NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 621.384 Online resources: Click here to access online
In:
Springer Nature eBookSummary: 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.
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.
There are no comments for this item.