  In a cloud radio access network (C-RAN), distributed remote radio heads
(RRHs) are coordinated by baseband units (BBUs) in the cloud. The
centralization of signal processing provides flexibility for coordinated
multi-point transmission (CoMP) of RRHs to cooperatively serve user equipments
(UEs). We target enhancing UEs' capacity performance, by jointly optimizing the
selection of RRHs for serving UEs, i.e., resource allocation (and CoMP
selection). We analyze the computational complexity of the problem. Next, we
prove that under fixed CoMP selection, the optimal resource allocation amounts
to solving a so-called iterated function. Towards user-centric network
optimization, we propose an algorithm for the joint optimization problem,
aiming at maximumly scaling up the capacity for any target UE group of
interest. The proposed algorithm enables network-level performance evaluation
for quality of experience.
