Bandwidth and Energy-Aware Resource Allocation for Cloud Radio Access Networks

Abstract

Cloud radio access network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of cellular networks. In this paper, a novel resource allocation solution that optimizes the energy consumption of a C-RAN is proposed. First, an energy consumption model that characterizes the computation energy of the base band unit (BBU) pool is introduced based on the empirical results collected from a programmable C-RAN testbed. Then, the resource allocation problem is split into two subproblems-namely the bandwidth power allocation (BPA) and the BBU energy-aware resource allocation (EARA). The BPA, which is first cast via mixed-integer nonlinear programming and then reformulated as a convex problem, aims at assigning a feasible bandwidth and power to serve all users while meeting their quality of service (QoS) requirements. The second subproblem, i.e., the BBU EARA, is defined as a bin-packing problem that aims at minimizing the number of active virtual machines in the BBU pool to save energy. Simulation results coupled with the real-time experiments on a small-scale C-RAN testbed show that the proposed resource allocation solution optimizes the energy consumption of the network while meeting practical constraints and QoS requirements, and outperforms competing algorithms, such as best fit decreasing, RRH-clustering, and SINR-based.