Interference-aware clustering in cloud radio access networks

Abstract

Cloud Radio Access Network (C-RAN) is an evolution in base station architecture. The key concept is to break down the conventional base station into a Base Band Unit (BBU) and a Remote Radio Head (RRH). While BBUs are pooled in a cloud data center, RRHs are distributed across multiple sites. In this context, resource utilization can be enhanced through statistical multiplexing: many RRHs may be clustered and associated with a single BBU. In this paper, RRH clustering is formulated as a Set Partitioning Problem, considering inter-cluster interferences. Optimal and heuristic solutions are derived to reduce network power consumption with minimum throughput requirements. Simulation results show that our interference-aware solutions outperform the no-clustering method, where only one RRH is associated with each BBU, and the interference-unaware Bin Packing algorithm. Moreover, our heuristic achieves performance very close to the optimal solution.