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