Joint User Association and RRH Clustering in Cloud Radio Access Networks

Abstract

Cloud Radio Access Network (C-RAN) has emerged as a promising network architecture for 5G cellular networks. The conventional base station is broken down into a Remote Radio Head (RRH) and a Base Band Unit (BBU). The RRHs are geographically scattered across multiple sites, whereas the BBUs are sheltered in a data center. In this context, deciding to which RRH users connect is known as the user association problem. As a function of network load conditions, some RRHs may be turned off, reducing network power consumption. Furthermore, RRHs may be mapped to a single BBU, achieving statistical multiplexing gain. Deciding what RRHs are grouped together is known as the RRH clustering problem. Traditionally, user association and RRH clustering are independently addressed. As these two problems are mutually dependent, we provide in this paper a framework for the joint optimization of the user association and the RRH clustering. Our objective is to minimize both the network power consumption and the total transmission delay. As this problem is a mixed integer non-linear programming problem, it can be solved through exhaustive search. However, the computational complexity becomes intractable as the network size increases. Therefore, we decouple our joint problem into two sub-problems. These sub-problems are iteratively solved until convergence. We further evaluate the performance of our proposed solution. Simulation results show that our optimal solution for the RRH clustering sub-problem outperforms the no-clustering solution, where one BBU is exclusively dedicated to each RRH, and the grand coalition solution, where all RRHs are associated with a single BBU.