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A new processing approach for reducing computational complexity in cloud-RAN mobile networks

Mahmood, A; Al-Yasiri, A; Alani, OYK

A new processing approach for reducing computational complexity in cloud-RAN mobile networks Thumbnail


Authors

A Mahmood

A Al-Yasiri



Abstract

Cloud computing is considered as one of the key drivers for the next generation of mobile
networks (e.g. 5G). This is combined with the dramatic expansion in mobile networks, involving millions
(or even billions) of subscribers with a greater number of current and future mobile applications
(e.g. IoT). Cloud Radio Access Network (C-RAN) architecture has been proposed as a novel concept to
gain the benefits of cloud computing as an efficient computing resource, to meet the requirements of future
cellular networks. However, the computational complexity of obtaining the channel state information in
the full-centralized C-RAN increases as the size of the network is scaled up, as a result of enlargement in
channel information matrices. To tackle this problem of complexity and latency, MapReduce framework
and fast matrix algorithms are proposed. This paper presents two levels of complexity reduction in the
process of estimating the channel information in cellular networks. The results illustrate that complexity
can be minimized from O(N3) to O((N/k)3), where N is the total number of RRHs and k is the number of
RRHs per group, by dividing the processing of RRHs into parallel groups and harnessing the MapReduce
parallel algorithm in order to process them. The second approach reduces the computation complexity
from O((N/k)3) to O((N/k)2:807) using the algorithms of fast matrix inversion. The reduction in complexity
and latency leads to a significant improvement in both the estimation time and in the scalability of
C-RAN networks.

Citation

Mahmood, A., Al-Yasiri, A., & Alani, O. (2018). A new processing approach for reducing computational complexity in cloud-RAN mobile networks. IEEE Access, 6, 6927-6946. https://doi.org/10.1109/ACCESS.2017.2782763

Journal Article Type Article
Publication Date Jan 1, 2018
Deposit Date May 1, 2018
Publicly Available Date May 1, 2018
Journal IEEE Access
Publisher Institute of Electrical and Electronics Engineers
Volume 6
Pages 6927-6946
DOI https://doi.org/10.1109/ACCESS.2017.2782763
Publisher URL http://dx.doi.org/10.1109/ACCESS.2017.2782763
Related Public URLs http://ieeeaccess.ieee.org/

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