Joint Radio and Computation Resource Allocation in Cloud Radio Access Networks

In C-RAN architecture, all computational processing is performed in the central baseband unit (BBU) pool, while radio operations are carried out in the remote radio heads (RRHs). The central BBU pool is connected to the RRHs by fronthaul links. Therefore, by separating the processing unit and the radio units, the clustering structure of RRHs can be designed to adapt to network changes. This paper deals with the problem of radio and computation resource allocation to maximize weighted sum rate. To reduce complexity, we use user-centric clustering and appropriate training resources allocation. Additionally, to lessen channel training overhead, an incomplete model of channel state information is considered, in which only intra-cluster channel state information is estimated. By replacing a sticky lower bound of user data rate in the main problem, the beamforming vectors under the constraints of computational and radio resources are designed in three steps. In the first step, the weighted sum rate maximization problem is solved under maximum radio transmitted power constraints by weighted minimum mean square error method. Then, in the second step, using a proposed greedy algorithm to allocate computational resources to users. In the third step, fronthaul capacity constraints are applied by another greedy algorithm.