Energy efficiency for cloud-radio access networks with imperfect channel state information

The advent of smartphones and tablets over the past several years has resulted in a drastic increase of global carbon footprint, due to the explosive growth of data traffic. Improving energy efficiency (EE) becomes, therefore, a crucial design metric in next generation wireless systems (5G). Cloud radio access network (C-RAN), a promising 5G network architecture, provides an efficient framework for improving the EE performance, by means of coordinating the transmission across the network. This paper considers a C-RAN system formed by several clusters of remote radio heads (RRHs), each serving a predetermined set of mobile users (MUs), and assumes imperfect channel state information (CSI). The network performance becomes therefore a function of the intra-cluster and inter-cluster interference, as well as the channel estimation error. The paper optimizes the transmit power of each RRH in order to maximize the network global EE subject to MU service rate requirements and RRHs maximum power constraints. The paper proposes solving the optimization problem using a heuristic algorithm based on techniques from optimization theory via a two-stage iterative solution. Simulation results show that the proposed power allocation algorithm provides an appreciable performance improvement as compared to the conventional systems with maximum power transmission strategy. They further highlight the convergence of the proposed algorithm for different networks scenarios.

Citation

Al-Oquibi B, Amin O, Dahrouj H, Al-Naffouri TY, Alouini M-S (2016) Energy efficiency for cloud-radio access networks with imperfect channel state information. 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). Available: http://dx.doi.org/10.1109/PIMRC.2016.7794612.

Acknowledgements

Hayssam Dahrouj would like to thank Effat University in Jeddah, Saudi Arabia, for funding the research reported in this paper through the Research and Consultancy Institute.