Fast solution of 5G channel path loss in substation based on improved ray tracing method

Due to the high frequency of fifth-generation (5G) signals, which leads to an extremely large computational scale when traditional algorithms solve the channel path loss, it is necessary to seek a fast solution method for the 5G channel path loss in substations in order to achieve a fast adaptation of the channel to the signal receiver and to ensure the reception quality of the 5G signals. Aiming at the problem that traditional algorithms suffer from extremely high computational complexity when dealing with the dyadic reflection–diffraction coefficients, a method based on singular value decomposition is proposed to reduce the dimensionality of the channel matrix for solution. Firstly, the ray tube model is used to divide the channel, and the incident angle information within the channel matrix is chunked through the nodes to discard the duplicates and those that contribute very little to the channel path loss. Then, matrix dimensionality reduction is achieved by the singular value decomposition algorithm, and the dimensionality-reduced channel matrix is substituted into the sum-vector inverse wrap-around coefficient solution formula to achieve the fast solution of 5G channel path loss. Finally, a comparison of the computational results of the proposed algorithm with those of the traditional algorithm is carried out by taking the 5G base station antenna of AAU5270E as an example and using the computational results of the experimental measurement data as a benchmark. The results show that the accuracy loss of the method proposed in the paper is only 1.31%, the compression of the data is 84.89, and the order of magnitude of the computation is 10 5 lower than that of the traditional algorithm. Future research could further integrate real-time channel data to achieve dynamic adaptive optimization, while extending this dimensionality reduction framework to high-dimensional complex channel modeling such as the sixth generation (6G), thereby promoting the continuous development of the algorithm in terms of real-time performance and generalization capability.