AI-Empowered Propagation Prediction and Optimization for Reconfigurable Wireless Networks

Abstract

Vehicular ad-hoc network (VANET) is one of the most important components to realizing intelligent connected vehicles, which is a high-commercial-value vertical application of the fifth-generation (5G) mobile communication system and beyond communications. VANET requires both ultrareliable low latency and high-data rate communications. In order to evolve towards the reconfigurable wireless networks (RWNs), the 5G mobile communication system is expected to adapt the key parameters of its radio nodes rapidly. However, the current propagation prediction approaches are difficult to balance accuracy and efficiency, which makes the current network unable to perform autonomous optimization agilely. In order to break through this bottleneck, an accurate and efficient propagation prediction and optimization method empowered by artificial intelligence (AI) is proposed in this paper. Initially, a path loss model based on a multilayer perception neural network is established at 2.6 GHz for three base stations in an urban environment. Not like empirical models using environment types or deterministic models employing three-dimensional environment models, this AI-empowered model explores the environment feature by introducing interference clutters. This critical innovation makes the proposed model so accurate as ray tracing but much more efficient. Then, this validated model is utilized to realize a coverage prediction for 20 base stations only within 1 minute. Afterward, key parameters of these base stations, such as transmission power, elevation, and azimuth angles of antennas, are optimized using simulated annealing. This whole methodology paves the way for evolving the current 5G network to RWNs.