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.