Research on a high-performance signal distribution reconstruction algorithm for wireless communication networks

With the rapid development of communication technology and the increasing demand for coverage refinement in wireless communication networks, the optimization of wireless communication networks is faced with unprecedented challenges. Obtaining the signal distribution map of wireless communication networks efficiently has become a popular area of study in this field. This paper considers a distributed sensing network architecture, a radial basis function neural network is used to process electromagnetic data and optimize the parameters of the random forest model. Then, interpolation processing of incomplete electromagnetic data is achieved by the improved random forest model, based on which a signal distribution map of the wireless communication network is reconstructed. The results indicate that the proposed algorithm yields high interpolation accuracy. The average error between the real signal distribution and the reconstructed signal distribution is 2.7973 dBm when the proportion of sampled nodes is 1%, and the similarity of the reconstructed signal distribution map to the original signal distribution map is good, demonstrating certain application prospects.