Ensemble SARSA and LSTM for User-Centric Handover Decisions in 5G Vehicular Networks

Abstract

5G and vehicular networks have enabled Intelligent Transportation Systems (ITS) with better safety and infotainment services where connected vehicles are critical components for data sharing. However, a stable connection is mandatory to transmit data successfully across the network. The 5G technology enhances bandwidth, stability, and reliability but suffers from low communication ranges, which results in frequent and unnecessary handovers and connection drops. In this paper, we introduce a user-centric approach, Factor-distinct SARSA Reinforcement Learning (FD-SRL), which combines a time series data-oriented model LSTM and adaptive method SARSA Reinforcement Learning for Virtual Cell (VC) and handover (HO) management. Our proposed approach maintains stable connections by reducing the number of HOs, given the fast-paced changes due to mobility, network load, and communication conditions. Realistic simulations demonstrated that FD-SRL reduced the number of HOs and the average cumulative HO time, showing potential improvements in connection stability for 5G-based ITS.