  This paper provides a unified framework to deal with the challenges arising
in dense cloud radio access networks (C-RAN), which include huge power
consumption, limited fronthaul capacity, heavy computational complexity,
unavailability of full channel state information (CSI), etc. Specifically, we
aim to jointly optimize the remote radio head (RRH) selection, user equipment
(UE)-RRH associations and beam-vectors to minimize the total network power
consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI
subject to per-RRH power constraints, each UE's total rate requirement, and
fronthaul link capacity constraints. This optimization problem is NP-hard. In
addition, due to the incomplete CSI, the exact expression of UEs' rate
expression is intractable. We first conservatively replace UEs' rate expression
with its lower-bound. Then, based on the successive convex approximation (SCA)
technique and the relationship between the data rate and the mean square error
(MSE), we propose a single-layer iterative algorithm to solve the NPC
minimization problem with convergence guarantee. In each iteration of the
algorithm, the Lagrange dual decomposition method is used to derive the
structure of the optimal beam-vectors, which facilitates the parallel
computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE
selection algorithm is proposed to guarantee the feasibility of the problem.
Simulation results show the benefits of the proposed algorithms and the fact
that a limited amount of CSI is sufficient to achieve performance close to that
obtained when perfect CSI is possessed.
