Distributed Split Learning for Map-Based Signal Strength Prediction Empowered by Deep Vision Transformer
Published in IEEE Transactions on Vehicular Technology, 2023
This article focuses on predicting the received signal strength (RSS) of mobile users, which is a fundamental problem for improving the coverage of cellular networks. Traditional methods for RSS prediction are based on ray tracing or stochastic radio propagation models. However, the former requires detailed environmental information that may not be practically available, while the latter cannot use the environmental data to its full potential and is not accurate enough. To address these issues, we design a practical RSS prediction system utilizing the trajectory information of users and satellite maps around base stations without sharing raw data. Specifically, we propose a map-based deep neural network (DNN) for the RSS prediction, empowered by the deep vision transformer (DeepVIT). Furthermore, to avoid sharing raw data, a novel split learning (SL) framework is developed. It splits the map-based DNN into two …