Scalable Processing in 5G Cloud-RAN Networks Using MapReduce Framework

Cloud Radio Access Network (C-RAN) was introduced as a revolutionary architecture to meet the exponential expansion in data traffic for the current and future mobile networks. However, the centralized configuration of C-RAN brings many challenges. One of the key questions to address is how the channel state information (CSI) is acquired accurately in C-RAN for numerous access points and users. For the purpose of reducing the time of acquiring CSI in C-RAN, the MapReduce framework is proposed in this paper as a novel approach to minimize the overhead of acquiring CSI in the full centralized C-RAN architecture. The main contribution of this paper is employing parallel processing to improve the scalability of mobile networks using C-RAN. The results, which have been obtained through simulation, illustrate that it can maintain the scalability as well as save a significant amount of acquisition time without affecting the performance of network.