Dubhe: a deep-learning-based B5G coverage analysis method

In recent years, with the rapid development of various technologies such as the Internet of Things and the Internet, the demand for massive device connections and a variety of differentiated new business applications has continued to increase. In order to better cope with the rapid growth of mobile data in the future, 5G also came into being. Then, B5G was proposed and applied in industries such as traditional voice/video, smart city, automotive car or ship, unmanned aerial vehicle, marine monitoring, IoT, and intelligent industry. In these scenarios, B5G is required to achieve seamless global coverage. As these scenarios are complex and changeable, analysis of the coverage of 5G base stations has become a challenge. We decompose the environment around the base station into multiple grids, and analyze the signal strength of each grid. A signal propagation model needs to be constructed to predict whether each grid is covered. The commonly used wireless propagation model is an empirical model based on a mathematical formula for statistical analysis of a large amount of test data during the establishment of a 5G local area network. It has universal applicability, but has insufficient prediction accuracy for specific scenarios. Therefore, it is necessary to calibrate and modify the typical propagation model according to the specific environment to obtain an accurate propagation model that matches the current area. We improved the traditional wireless communication model, and proposed a deep-learning-based B5G coverage analysis method named Dubhe which is one of the planets of the Big Dipper. In a real cell scenario, the mean square error of the link budget of the typical UMa model is 17.9 dBm, while the mean square error of the proposed Dubhe model constructed in this article is only 6.78 dBm. The recognition rate of weak coverage can reach 42.86%.