Study on Wireless Signal Propagation in Residential Outdoor Activity Area Based on Deep Learning
Sunying Hu, Liguo Shuai, Qiang Yang, Huiling Chen · 2021
As explosive growth of mobile data traffic brings great challenges to the mobile networks, how to reasonably deploy the latest generation mobile networks to meet the needs of users for communication becomes an urgent technical problem to be solved. Since machine learning technology has shown its superiority in processing big data in recent years, researches on the application of machine learning technology in wireless communication are expanded and explored gradually. In this paper, a wireless signal propagation model based on deep learning neural network is proposed to evaluate the wireless signal propagation characteristics and signal coverage status in different scenarios in the complex and changeable residential outdoor activity areas. The massive data generated in practical applications are reasonably utilized to predict the signal propagation characteristics of each cell with high accuracy and stability. The model has is of good adaptability to different complex scenarios and can providing provide a certain reference value and engineering guidance for further wireless network deployment and optimization development.