Throughput-Aware RRHs Clustering in Cloud Radio Access Networks

Abstract

Cloud-Radio Access Network (C-RAN) is an attractive solution to Mobile Network Operators. Firstly, C-RAN leverages the effect of pooling multiple Baseband Units (BBUs) to offer centralized processing resources while hosting them on cloud. This results in multiple benefits ranging from statistical multiplexing gains, to energy efficiency. Secondly, C-RAN allows deploying Remote Radio Heads (RRHs) in proximity of end-users allowing exploiting Inter-Cell Interference Cancellation (ICIC) to maximize throughput by coordinating multiple RRHs. In this context, we propose, in this paper, a new throughput-aware RRHs clustering method for C-RAN that maximizes the throughput for end-users, while meeting multiple constrained resources on BBUs. Our approach consists of two stages: First, individual throughput value and requirements of each RRH are calculated taking into account the Signal-to-Interference-plus-Noise Ratio (SINR) values and the distance between RRHs and users. Then, they are included into a k-dimensional Multiple-Choice Knapsack Problem (k-MCKP) subject to several constraints in terms of required resources in order to form RRHs clusters that maximize the global throughput. Simulation results demonstrate the good performance of our proposal in terms of end-users throughput, spectral efficiency and execution time, when compared with the optimal solution and the basic strategy using no-clustering scenarios.