A Novel Energy-Efficient Resource Allocation Approach in Limited Fronthaul Virtualized C-RANs

Abstract

We consider the downlink virtualized cloud-radio access networks (C-RANs) with limited capacity fronthaul. A novel virtual computing resource allocation (VCRA) which can dynamically split the users workload into smaller fragments to be served by virtual machines is presented. Under the proposed scheme, we aim at maximizing the network energy efficiency (EE) by a joint design of virtual computing resources, transmit beamforming, remote radio head (RRH) selection, and RRH-user association, considering rate dependent fronthaul power consumption model. The formulated problem is generally combinatorial and NP-hard. For an appealing solution approach, we resort to the difference of convex algorithm (DCA) to solve the continuous relaxed problem. In particular, Lipschitz continuity is derived for non-convex parts to arrive at a sequence of convex quadratic programs, which can be solved efficiently by modern convex solvers. Finally, a post-processing procedure is proposed to obtain a high-performance feasible solution from the continuous relaxation. Numerical results show that the proposed algorithms converge rapidly and the proposed scheme significantly improves the network EE compared to the existing schemes.