Cellular Network Power Control Optimization Using Unsupervised Machine Learnings

Spectrum is the most scarce resources for any mobile operator, with the exponentially growing demand for data services that require higher speeds [1], operators have to utilize the available spectrum in the most efficient way to provide the best data experience for smartphone users whom are hungry for data services, yet keep an adequate performance for circuit switched voice service for the legacy technologies in WCDMA and GSM that are still serving customers with handsets that are not supporting LTE. The advancement in clustering techniques could allow operator to optimize their network parameters in a more efficient way, so they can achieve better spectrum utilization for the legacy technologies that will allow more spectrum refarming for LTE needs.In this paper, we propose a new approach for optimizing GSM power control algorithm to enhance GSM voice performance in operators with limited spectrum allocated to this technology. The new approach has been tried in real mobile network with positive results in terms of voice quality and dropped call rate. The approach could be extended to other radio network algorithms to better exploiting current network resources.