Online evolution of femtocell coverage algorithms using genetic programming

The wide adoption of smartphones has resulted in an exponential increase in the demand for wireless data. To address this problem, operators have started deploying large numbers of small cells. 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.