Adaptive Handover Management in High-Mobility Networks for Smart Cities.

The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios. [ABSTRACT FROM AUTHOR]