  Cloud radio access networks (C-RAN) are a promising technology to enable the
ambitious vision of the fifth generation (5G) communication networks. In spite
of the potential benefits of C-RAN, the operational costs are still a
challenging issue, mainly due to the centralized processing scheme and the
large number of operating remote radio head (RRH) connecting to the cloud. In
this work we consider a setup in which a CRAN is powered partially with a set
of renewable energy sources (RESs), our aim is to minimize the
processing/backhauling costs at the cloud centre as well as the transmission
power at the RRHs, while satisfying some user quality of service (QoS). This
problem is first formulated as a mixed integer non linear program (MINLP) with
a large number of optimization variables. The underlying NLP is non-convex,
though we address this issue through reformulating the problem using the mean
squared error (MSE)-rate relation. To account to the large-scale of the
problem, we introduce slack variables to decompose the reformulated (MINLP) and
enable the application of a distributed optimization framework by using the
alternating direction method of multipliers (ADMM) algorithm.
