Adaptive remote radio head control for cloud radio access networks

Abstract

In this paper, we develop an adaptive remote radio head (RRH) control scheme to maximize the network capacity of frequency division duplexing (FDD)-based cloud radio access networks. We focus on a realistic performance metric that considers reference signal (RS) transmission overhead. Finding the optimal subset of RRHs that maximize the network capacity is formulated as an integer programming problem. We develop two efficient algorithms based on greedy search and linear programming relaxation combined with gradient ascent search, respectively. Our simulation results reveal that a larger number of antennas do not always guarantee capacity increase in real communication environments due to RS transmission overhead. The proposed scheme adaptively determines the subset of RRHs considering RS transmission overhead and provides significant capacity gain over previous approaches.