Demo: Mobility-Aware 5G Vehicles Handover via Location-Based RTT Prediction

Autonomous mobility, particularly in regards to self-driving vehicular connectivity, has stringent service level agreements (SLA) that require a stable 5G connection with good quality-of-service (QoS), even when the user is on the move, alongside a significant cooperation with the environment. The dynamic nature of vehicular networks, in which the terminal device is constantly moving, leads to the occurrence of potentially problematic handovers, such as leaving a public 5G network and entering a private one. There is still a lack of integrated solutions that consider both latency requirements and the spatial context of the user. Handover decisions in current deployments are typically reactive and have undergone minimal evolutions since 2G, as they are still mostly based on radio signal strength indicators such as the Reference Signal Received Power (RSRP) or SignalNoise Ratio (SNR). While this scheme is easy to implement, these metrics are not appropriate for the SLAs of dynamic mobility scenarios. In these cases, radio signal quality does not directly reflect the end-to-end latency experienced by the application, and poorly timed handovers can result in significant latency spikes, which often violate application-level SLAs, especially in Ultra-Reliable Low-Latency Communication (URLLC) use cases. To address these challenges, we propose a demo of a statistical location-aware layer that refines 5G vehicular handover decisions by integrating a mechanism that optimizes network selection with minimal overhead, utilizing real-time geolocation, rule-based constraints, and a predictive Round-Trip Time (RTT) function.