Optimal C-RAN Clustering in 5G context based on Memetic Algorithm

To meet the 5G requirements in terms of high capacity and enhanced quality of service, the Cloud Radio Access Network (C-RAN) relies on the network densification and coordination. The main idea behind the C-RAN is to split the base station into two parts: the first component is the Remote Radio Head (RRH) which will be responsible for all Radio transmissions and the second one is the Base Band Unit (BBU) which will be responsible for the processing and control. Only the RRH will be densified to offer more capacity. The processing resources will be pooled and clustered so that many RRHs will be managed by one BBU pool which will reduce the related CAPEX and OPEX of the network's deployment, operation and maintenance. The main goal of this paper is to propose an optimized clustering algorithm able to improve interference management between BBU clusters and decrease the network mobility signaling cost. In this work, we will be interested by the implementation and optimization of local search algorithm (Tabu Search) and evolution algorithm (Genetic Algorithm) as well as the combination of both methods in order to enhance the convergent solution.