In order to operate such small cell
network cost-effectively they need to be able to intelligently
optimize their configuration, which can be achieved by applying
machine learning techniques such as genetic programming. The
use of genetic programming has previously been used to derive
joint coverage algorithms for a group of enterprise femtocells.
However, the evolution of the algorithms was performed in an
offline manner, on a pre-defined simulation model of the
deployment scenario. In this paper, an approach to perform the
evolution in an online manner using an automated model
building process is presented. The model building process uses
network traces as inputs to create a hierarchical Markov model
that is shown to be able to capture the behavior of the femtocell
network well. It is shown that the resulting environment model
can effectively drive the on-line evolution of coverage
optimization algorithms.