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User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges and Methodologies

Pan, Cunhua, Elkashlan, Maged, Wang, Jiangzhou, Yuan, Jinhong, Hanzo, Lajos (2018) User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges and Methodologies. IEEE Communications Magazine, 56 (6). pp. 14-20. ISSN 0163-6804. (doi:10.1109/MCOM.2018.1700483)

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http://dx.doi.org/10.1109/MCOM.2018.1700483

Abstract

Ultra-dense networks (UDN) constitute one of the most promising techniques of supporting the fifth generation (5G) mobile system. By deploying more small cells in a fixed area, the average distance between users and access points can be significantly reduced, hence a dense spatial frequency reuse can be exploited. However, severe interference is the major obstacle in UDNs. Most of the contributions investigate the interference by designing distributed algorithms based on cooperative game theory. This paper advocates the application of dense user-centric cloud radio access network (CRAN) philosophy to UDNs, thanks to the recent development of cloud computing techniques. Under dense C-RAN architectures, centralized signal processing can be invoked for supporting Coordinated Multiple Points Transmission/Reception (CoMP). We summarize the main challenges in dense usercentric C-RANs. One of the most challenging issues is the requirement of the global CSI for the sake of cooperative transmission. We investigate this requirement by only relying on partial channel state information (CSI), namely, on inter-cluster large-scale CSI. Furthermore, the estimation of the intracluster CSI is considered, including the pilot allocation and robust transmission. Finally, we highlight several promising research directions to make the dense user-centric C-RAN become a reality, with special emphasis on the application of the ‘big data’ techniques.

Item Type: Article
DOI/Identification number: 10.1109/MCOM.2018.1700483
Uncontrolled keywords: Ultra-dense networks (UDN), user-centric C-RAN, virtual cells, DAS, imperfect CSI, pilot allocation.
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Jiangzhou Wang
Date Deposited: 04 Sep 2017 13:14 UTC
Last Modified: 12 Feb 2020 04:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63209 (The current URI for this page, for reference purposes)
Wang, Jiangzhou: https://orcid.org/0000-0003-0881-3594
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