User-centric Clustering and Beamforming for Energy Efficiency Optimization in Cloud-RAN

User-centric and energy efficient are becoming two foremost design principles in the cloud radio access networks (Cloud-RAN). In this paper, we thus consider the problem of how to assign each user to several preferred remote radio heads (RRHs) and design the corresponding beamforming coefficients in a user-centric and energy efficient manner. We formulate this problem as a joint clustering and beamforming optimization problem, with the objective to maximize the energy efficiency (EE) while satisfying the users' quality of service (QoS) requirement and respecting the RRHs' transmit power limits. We first transform it into an equivalent parametric subtractive problem using the approach in fractional programming, and then it is cast into a tractable convex optimization problem by introducing a lower bound of the objective function. Finally, the structure of the optimal solution is derived and a two-tier iterative scheme is developed to find the clustering pattern and beamforming coefficients that maximize EE. Specially, we derive a RRH-user association threshold, based on which the RRH clustering pattern and the corresponding beamforming coefficients can be simultaneously determined. Through simulations, we show the superior performance of the proposed user-centric clustering and beamforming scheme in Cloud-RAN.