Computational complexity reduction in Taguchi method based joint optimization of antenna parameters in LTE-A networks : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Telecommunication and Network Engineering

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Massey University
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Long Term Evolution-Advanced (LTE-A) system is operated with cellular technology based on frequency reuse. Due to the co-channel interference between cells, one cell‟s performance is decided by not only its own configurations but also other cell‟s settings. Therefore, joint optimization of antenna parameters in LTE-A cellular networks 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. Nevertheless, the large number of cell parameters and the interdependencies between these parameters make it difficult and time-consuming to optimize a cellular network. In practice, the joint setting of the parameters of all cells with irregular layout and coverage areas becomes an important and challenging task. There are several methods to search for the optimal settings of a cellular network. One commonly used search method is Simulated Annealing (SA). SA can produce good results in cellular network optimization, but it takes a long time and its performance can easily be degraded if the input parameters are misconfigured. Other methods include the trial-and-error approach that requires manual selection of parameter values and has no guarantee for good results, and the brute-force approach that searches through all possible combinations of parameter values and is thus computationally prohibitive. Among the various algorithms proposed for this time-consuming optimization task, the iterative approach based on the Taguchi method (TM) is a recent development that has been shown to be promising. This thesis presents some further improvements to the TM-based approach aiming at enhancing optimization performance and reducing computational complexity. The proposed improvements include the use of the mixedlevel 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.
Long Term Evolution Advanced (LTE-A), Mobile communication, Antenna parameters, Cellular networks, Network optimization, Nearly-Orthogonal Array (NOA), Taguchi method