Centralized self-optimization in LTE-A using Active Antenna Systems

Active Antenna Systems (AAS) have the flexibility to electronically orient the antenna beams thereby providing a flexible and efficient control over the performance of the network in terms of coverage, capacity and Quality-of-Service (QoS). In this paper we propose a new centralized self-optimizing method which adapts antenna tilts according to a specified objective. The proposed method uses a surrogate function to model the functional relationships between noisy Key Performance Indicators (KPIs) and antenna parameters, and subsequently performs optimization of the model using a pattern search algorithm in an iterative manner. The methodology is applied to solve several multi-parameter self-optimization problems in a Long Term Evolution Advanced (LTE-A) network. Results obtained using a flow level simulator shows the advantages of the proposed AAS optimization method in terms of fast convergence, performance and flexibility in multi-parameter network optimization.