RRH-Sector selection and load balancing based on MDP and dynamic RRH-Sector-BBU mapping in C-RAN

Abstract

Cloud radio access network (C-RAN) is a paradigmatic architecture that supports the tremendous increase in mobile network traffic. Despite the novel advancements that C-RAN offers, a time-varying traffic environment can cause load imbalances, resulting in inefficient resource utilization. Consequently, the network performance can degrade in terms of the blocked users, the number of unnecessary handovers, and the power consumption. This paper presents an RRH-Sector pair selection for new connections and network load-balancing framework that optimizes the network performance and operator reward without affecting the users’ Quality of Service (QoS) in C-RAN. Firstly, the proposed approach selects the best RRH-Sector pair for each new connection demand by considering the user and operator objectives jointly. This decision is based on an algorithm derived from the Markov decision process (MDP). Secondly, the load-balancing problem is addressed via optimization of the RRH-Sector-BBU dynamic mapping formulated as a linear integer-based constrained optimization problem. We compare solutions for this problem obtained by several evolutionary algorithms. Simulation results show that the proposed RRH-Sector selection scheme provides significant gains when compared with the traditional method based on the received signal strength (RSS). Furthermore, the RRH-Sector-BBU mappings obtained by the evolutionary algorithms are compared with the optimal exhaustive search method. The results show that in most cases the proposed models reach the optimum solution for the number of blocked users, the number of handovers, and the BBU power consumption.