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Scalable Signal Processing in Cloud Radio Access Networks
Gespeichert in:
| Personen und Körperschaften: | , , |
|---|---|
| Titel: | Scalable Signal Processing in Cloud Radio Access Networks/ by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan |
| Format: | E-Book |
| Sprache: | Englisch |
| veröffentlicht: |
Cham
Springer
2019
|
| Gesamtaufnahme: |
SpringerBriefs in Electrical and Computer Engineering Springer eBook Collection Springer eBooks |
| Schlagwörter: | |
| Quelle: | Verbunddaten SWB |
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| 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 | ||
| 650 | 0 | |a Telecommunication | |
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| author | Zhang, Ying-Jun Angela, Fan, Congmin, Yuan, Xiaojun |
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| contents | 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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| spelling | Zhang, Ying-Jun Angela VerfasserIn aut, Scalable Signal Processing in Cloud Radio Access Networks by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan, Cham Springer 2019, 1 Online-Ressource (XI, 100 p. 30 illus), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, SpringerBriefs in Electrical and Computer Engineering, Springer eBook Collection, Springer eBooks 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, Telecommunication, Wireless and Mobile Communication, Wireless communication systems, Mobile communication systems, Signal processing., Image processing., Speech processing systems., Electrical engineering., Fan, Congmin VerfasserIn aut, Yuan, Xiaojun VerfasserIn aut, 9783030158835, Erscheint auch als Druck-Ausgabe 978-3-030-15883-5, https://doi.org/10.1007/978-3-030-15884-2 X:SPRINGER Resolving-System lizenzpflichtig, https://zbmath.org/?q=an:1412.94001 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext, FID-MEDIEN-DE-15, https://doi.org/10.1007/978-3-030-15884-2 DE-14, DE-14 epn:3588577947 2020-02-06T11:12:03Z, https://doi.org/10.1007/978-3-030-15884-2 DE-Ch1, DE-Ch1 epn:3481151438 2019-06-04T17:17:54Z, DE-105 epn:3481515294 2019-06-05T11:23:12Z, https://doi.org/10.1007/978-3-030-15884-2 DE-Zwi2, DE-Zwi2 epn:3481151446 2019-06-04T17:17:54Z |
| spellingShingle | Zhang, Ying-Jun Angela, Fan, Congmin, Yuan, Xiaojun, Scalable Signal Processing in Cloud Radio Access Networks, 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, Telecommunication, Wireless and Mobile Communication, Wireless communication systems, Mobile communication systems, Signal processing., Image processing., Speech processing systems., Electrical engineering. |
| title | Scalable Signal Processing in Cloud Radio Access Networks |
| title_auth | Scalable Signal Processing in Cloud Radio Access Networks |
| title_full | Scalable Signal Processing in Cloud Radio Access Networks by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan |
| title_fullStr | Scalable Signal Processing in Cloud Radio Access Networks by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan |
| title_full_unstemmed | Scalable Signal Processing in Cloud Radio Access Networks by Ying-Jun Angela Zhang, Congmin Fan, Xiaojun Yuan |
| title_short | Scalable Signal Processing in Cloud Radio Access Networks |
| title_sort | scalable signal processing in cloud radio access networks |
| title_unstemmed | Scalable Signal Processing in Cloud Radio Access Networks |
| topic | Telecommunication, Wireless and Mobile Communication, Wireless communication systems, Mobile communication systems, Signal processing., Image processing., Speech processing systems., Electrical engineering. |
| topic_facet | Telecommunication, Wireless and Mobile Communication, Wireless communication systems, Mobile communication systems, Signal processing., Image processing., Speech processing systems., Electrical engineering. |
| url | https://doi.org/10.1007/978-3-030-15884-2, https://zbmath.org/?q=an:1412.94001 |
| work_keys_str_mv | AT zhangyingjunangela scalablesignalprocessingincloudradioaccessnetworks, AT fancongmin scalablesignalprocessingincloudradioaccessnetworks, AT yuanxiaojun scalablesignalprocessingincloudradioaccessnetworks |