Centralized self-optimization of pilot powers for load balancing in LTE

Optimization of radio access networks is a challenge for cellular operators. With the deployment of new radio access technologies and advanced features, network optimization needs to be automated for performance improvement and OPerational EXpenditures (OPEX) reduction. In this paper, we propose a method for centralized self-optimization of pilot powers to perform load balancing between base stations. The proposed method uses a stochastic surrogate function to model the functional relationships between noisy Key Performance Indicators (KPIs) and network parameters, and subsequently performs optimization using a pattern search algorithm in a recursive manner. The method is applied to solve a high dimensional optimization problem of pilot power based load balancing in an LTE network. Results obtained using a flow level analytical network simulator show the advantages of the proposed recursive modeling and optimization approach as a promising solution in terms of robustness to noisy data and fast convergence to the optimum point for a high dimensional network optimization.