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Scalable signal processing in cloud radio access networks

Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan (Springer Nature, 2019)

 Abstrak

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.

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 Metadata

Jenis Koleksi : eBooks
No. Panggil : e20509838
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Subjek :
Penerbitan : Switzerland: Springer Nature, 2019
Sumber Pengatalogan: LibUI eng rda
Tipe Konten: text
Tipe Media: computer
Tipe Pembawa: online resource
Deskripsi Fisik: xi, 100 pages : illustration
Tautan: https://doi.org/10.1007/978-3-030-15884-2
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No. Panggil No. Barkod Ketersediaan
e20509838 02-20-154400077 TERSEDIA
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