Joint Virtual Computing and Radio Resource Allocation in Limited Fronthaul Green C-RANs

We consider the virtualization technique in the downlink transmission of limited fronthaul capacity cloud-radio access networks. A novel virtual computing resource allocation (VCRA) method 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 by a joint design of virtual computing resources, transmit beamforming, remote radio head (RRH) selection, and RRH-user association. Moreover, we construct a more realistic fronthaul power consumption model, which is directly proportional to users’ rate transmitted by the corresponding RRHs. The formulated problem is combinatorial and difficult to solve in general. Our first contribution is to customize a branch-and-reduce-and-bound method to attain a globally optimal solution. To compute a high-quality approximate solution, a standard routine is used to deal with the continuous relaxation of the original problem. However, the proposed continuous relaxation is non-convex which implies another challenge. For a practically appealing solution approach, we resort to a local optimization method, namely the difference of convex algorithm. Our second contribution is on the use of Lipschitz continuity 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. Extensive numerical results demonstrate that the proposed algorithms converge rapidly and achieve near-optimal performance as well as outperform other known methods. Moreover, we numerically show that the VCRA scheme significantly improves the system energy efficiency compared to the existing schemes.