  This work presents a new network optimization framework for cellular networks
using neighborhood-based optimization. Under this optimization framework
resources are allocated within virtual cells encompassing several base-stations
and the users within their coverage areas. We form the virtual cells using
hierarchical clustering with a minimax linkage criterion given a particular
number of such cells. Once the virtual cells are formed, we consider an
interference coordination model in which base-stations in a virtual cell
jointly allocate the channels and power to users within the virtual cell. We
propose two new schemes for solving this mixed integer NP-hard resource
allocation problem. The first scheme transforms the problem into a continuous
variables problem; the second scheme proposes a new channel allocation method
and then alternately solves the channel allocation problem using this new
method, and the power allocation problem. We evaluate the average system sum
rate of these schemes for a variable number of virtual cells. These results
quantify the sum-rate along a continuum of fully-centralized versus
fully-distributed optimization for different clustering and resource allocation
strategies. These results indicate that the penalty of fully-distributed
optimization versus fully-centralized (cloud RAN) can be as high as 50%.
However, if designed properly, a few base stations within a virtual cell using
neighborhood-based optimization have almost the same performance as
fully-centralized optimization.
