Queue-Aware Joint Remote Radio Head Activation and Beamforming for Green Cloud Radio Access Networks

The cloud radio access network (C-RAN) is an emerging network architecture that holds the promise of coping with the explosive growth of mobile wireless data. In this paper, by considering the stochastic traffic arrivals and time-varying channel conditions, we address the stochastic optimization of joint remote radio head (RRH) activation and linear beam-forming to maintain delay performance and minimize average network power consumption in downlink slotted C-RAN. We first formulate the joint optimization as a group sparse beam-forming problem. Based on Lyapunov optimization technique, the stochastic optimization problem is then transformed into a queue-aware joint RRH activation and beamforming problem, which can be greedily solved at each slot. Finally, a low-complexity stationary algorithm with guaranteed convergence and closed-form expressions is proposed. The proposed algorithm can be implemented in a parallel manner, thus it is highly scalable to a large-sized C-RAN. Extensive numerical simulations validate the effectiveness of the proposed algorithm.