Scalable signal processing in cloud radio access networks
Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan
(Springer Nature, 2019)
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| 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. |
 Scalable Signal Processing in Cloud Radio Access Networks.pdf :: Unduh
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| No. Panggil : | e20509838 |
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| 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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| Tidak ada ulasan pada koleksi ini: 20509838 |
