Self-optimization of pilot power in enterprise femtocells using multi objective heuristic

Deployment of a large number of femtocells to jointly provide coverage in an enterprise environment raises critical challenges especially in future self-organizing networks which rely on plug-and-play techniques for configuration. This paper proposes a multi-objective heuristic based on a genetic algorithm for a centralized self-optimizing network containing a group of UMTS femtocells. In order to optimize the network coverage in terms of handled load, coverage gaps, and overlaps, the algorithm provides a dynamic update of the downlink pilot powers of the deployed femtocells. The results demonstrate that the algorithm can effectively optimize the coverage based on the current statistics of the global traffic distribution and the levels of interference between neighboring femtocells. The algorithm was also compared with the fixed pilot power scheme. The results show over fifty percent reduction in pilot power pollution and a significant enhancement in network performance. Finally, for a given traffic distribution, the solution quality and the efficiency of the described algorithm were evaluated by comparing the results generated by an exhaustive search with the same pilot power configuration.