A Dynamic Clustering Algorithm Design for C-RAN Based on Multi-Objective Optimization Theory

Cloud radio access network (C-RAN) is a new concept of network architecture, which brings a technical revolution into the wireless communication market and leads to some kind of all new mode of the future wireless communications. In this paper the clustering algorithm based on multi-objective optimization is investigated. The proposed algorithm aims at maximizing the throughput contribution of the Remote RF Head (RRH) to the whole system and minimizing its total power consumption with guaranteed energy efficiency of RRH. Using the novel greedy dynamic clustering algorithm, the joint capacity of RRHs is improved. The throughput of each RRH is first given using the pricing mechanism and the Pascoletti and Serafini Scalarization method is then implemented to solve the multiobjective optimization problem. Finally, the performance of the algorithm is assessed by the simulation results. It is shown that the novel dynamic clustering algorithm based on multiobjective optimization in the C-RAN architecture outperforms the traditional greedy clustering approach.