5G mobile network signal strength interpolation using machine learning GAN algorithm

This article presents a new interpolation algorithm for received signal strength in mobile networks. The algorithm aims to fill in the missing values in a network environment by creating a full signal strength map. It is based on the deep learning Generative Adversary Network (GAN), which has previously been used successfully only for fixing pixels in image repairs. The algorithm was trained and tested using publicly available data on outdoor mobile network signal strength under 6GHz, and the results were evaluated using two performance metrics: accuracy of measurement and size of predicted values. The results show that the accuracy of the algorithm depends heavily on the characteristics of the input data. When the interpolating points are evenly distributed, the accuracy is high and the number of predicted values is satisfactory. Our algorithm reduces the number of manual measurements required to obtain a complete signal strength map, simplifying network planning and quality assurance processes.