Surrogate based centralized automated optimization applied to LTE mobility load balancing

Deployment of Long Term Evolution (LTE) & LTEAdvanced networks will be challenged by cost and complexity. Self Organizing Network (SON) functionalities promise significant improvement of the network in terms of reducing OPerational EXpenditure (OPEX) and performance improvement. In this paper, we propose a recursive automated optimization method which builds statistical models of the functional relationships between noisy Key Performance Indicators (KPIs) and network parameters; and performs stochastic optimization during the model building process. The proposed methodology is applied to a centralized intra-LTE Mobility Load Balancing (MLB) problem and its performance is evaluated through system level simulations. The results show that the proposed modeling and optimization approach is a promising solution for centralized intra-LTE MLB in terms of optimization performance and convergence under noisy data.