Joint remote radio head activation and beamforming for energy efficient C-RAN

Abstract

The cloud radio access network (C-RAN) has emerged as a promising architecture to provide extremely high throughput with fantastic energy efficiency (EE) performance. However, as all the RRHs need to be connected to the baseband unit pool (BBU) through transport links, the transmit power consumption becomes significant, which result in the demands of new researches in energy efficiency optimization. In this paper, we focus on the dynamic remote radio head (RRH) activation and network EE optimization in order to fully reap the benefits brought by Green C-RAN. First, an EE optimization problem that jointly considers RRH activation and group sparse beamforming is formulated, which is hard to solve due to its non-convexity nature. Thus, we utilize the weighted minimal mean square error (WMMSE) method to transfer the non-convex EE problem into a concave-convex fractional program problem. And the Lagrangian theory is exploited to assist the problem analysis and algorithm design. Specifically, the weighted group sparse beamforming algorithm is proposed. In this algorithm, we adopt the mixed l 1 =l p -norm to induce group sparsity in the beamformers, which corresponds to switching off RRHs. Simulation results will show that the proposed algorithm can significantly improve the EE for C-RAN.