Remote Radio Head Clustering in 5G HetNets by Graph Partitioning.

Abstract

In 5G cellular systems, network densification is a key technique to cope with the strong increase of traffic volume in mobile communications. The deployment of indoor small cells offloads macrocells at the cost of increasing network complexity. In this work, a methodology for planning Centralized-Radio Access Networks (C-RANs) comprising macrocells and small cells is proposed. The aim is to group Radio Remote Heads (RRH) into Base Band Unit (BBU) pools and coordination sets (a.k.a. BBU planning) to maximize user throughput. To this end, the above assignment problem is formulated as a graph partitioning problem, which is solved by graph theory algorithms. Method assessment is carried out by using a radio planning tool that implements a novel analytical system model to check spectral efficiency and resource allocation. Different BBU planning strategies are first compared, and the impact of Inter-Cell Interference Coordination (ICIC), Coordinated Multi-Point Transmission/Reception (CoMP) and Multi-Connectivity (MC) on network performance with the best BBU plan is then assessed under different system loads and coordination constraints. Results show that the selection of a proper graph partitioning scheme for RRH clustering is key to ensure that the above schemes improve system capacity in heterogeneous environments.