Computational complexity reduction in Taguchi method based joint optimization of antenna parameters in LTE-A networks

In Long Term Evolution-Advanced (LTE-A) cellular networks, minimizing inter-cell interference is the key to maximizing coverage and capacity. This can be achieved by setting the antenna parameters such as azimuth orientations and tilts to the optimal values. Due to the interdependencies between these parameters, finding the optimal configuration is a time-consuming and complex task. Among the various algorithms proposed for this task, the joint optimization approach based on the Taguchi method (TM) is a recent development that has been shown to be promising. This paper presents some further improvements to the existing approach aiming at enhancing optimization performance and reducing computational complexity. The proposed improvements include the use of the mixed-level Nearly-Orthogonal Array (NOA) to cater for the different optimization ranges of different types of parameters, an improved mapping function to select testing values that are more representative of the optimization range, and a hybrid approach using multiple NOAs with decreasing number of experiments to exchange small degradation in optimization performance for significant reduction in computational complexity. The effectiveness of the proposed improvements is demonstrated by numerical examples.