Adaptive User-centric Virtual Cell Handover Decision-making in 5G Vehicular Networks

Connected vehicles enable massive data sharing and support intelligent transportation services. Consequently, a stable connection is compulsory to transmit across the network successfully, where 5G technology introduces more bandwidth, stability, and reliability. However, 5G communication is susceptible to frequent handovers and connection drops. A user-centric perspective helps cope with the smaller communication range in ultra-dense 5G networks. We thus introduce a Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) algorithm for user-centric to efficiently handle virtual cell (VC) management and reduce the number of handovers (HO). The adaptability of the algorithm copes with high vehicular mobility and dynamic traffic and communication, deciding on in-rage cellular towers and VC size. Realistic simulated analyses showed CO-SRL reduced the number of handovers and the cumulative handover time.