Neural network optimization for the 5G positioning of the result domain

To meet the demand of the 5G positioning requirements in indoor environment, we proposed a method to optimize the rough positioning results by using neural network algorithms, which reduced the positioning error caused by multipath and non-line-of-sight propagation, and improved the positioning accuracy of the result domain. The optimization algorithm used the time of arrival (TOA) method and the time difference of arrival (TDOA) method in ranging positioning to obtain rough positioning results, and combined separately with BP neural network, Elman neural network, as well as genetic algorithm (GA)-BP network and GA-Elman network to obtain a better positioning results, then the four neural network algorithms were analyzed and evaluated. Compared with the BP algorithm, Elman algorithm has the characteristics of fast iteration convergence, few iterations and good error correction, which is more suitable for the optimization of the 5G localization result domain. The accuracy of the results is improved after incorporating the GA, among which the GA-Elman algorithm can be trained to obtain the best localization results.