Latency reduction by dynamic channel estimator selection in C-RAN networks using fuzzy logic

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

Abstract

Due to a dramatic increase in the number of
mobile users, operators are forced to expand their networks
accordingly. Cloud Radio Access Network (C-RAN) was
introduced to tackle the problems of the current generation of
mobile networks and to support future 5G networks. However,
many challenges have arisen through the centralised structure of
C-RAN. The accuracy of the channel state information
acquisition in the C-RAN for large numbers of remote radio
heads and user equipment is one of the main challenges in this
architecture. In order to minimize the time required to acquire
the channel information in C-RAN and to reduce the end-to-end
latency, in this paper a dynamic channel estimator selection
algorithm is proposed. The idea is to assign different channel
estimation algorithms to the users of mobile networks based on
their link status (particularly the SNR threshold). For the
purpose of automatic and adaptive selection to channel
estimators, a fuzzy logic algorithm is employed as a decision
maker to select the best SNR threshold by utilising the bit error
rate measurements. The results demonstrate a reduction in the
estimation time with low loss in data throughput. It is also
observed that the outcome of the proposed algorithm increases at
high SNR values.

Citation

Mahmood, A., Al-Yasiri, A., & Alani, O. (2018). Latency reduction by dynamic channel estimator selection in C-RAN networks using fuzzy logic. Computer Networks, 138, 44-56. https://doi.org/10.1016/j.comnet.2018.03.027

• Journal Article Type: Article
• Acceptance Date: Mar 23, 2018
• Online Publication Date: Mar 30, 2018
• Publication Date: Jun 19, 2018
• Deposit Date: May 15, 2018
• Publicly Available Date: Mar 30, 2019
• Journal: Computer Networks
• Print ISSN: 1389-1286
• Publisher: Elsevier
• Volume: 138
• Pages: 44-56
• DOI: https://doi.org/10.1016/j.comnet.2018.03.027
• Publisher URL: http://dx.doi.org/10.1016/j.comnet.2018.03.027
• Related Public URLs: https://www.journals.elsevier.com/computer-networks