Open RAN-based Network Slicing for Connecting Flying and Ground-based Cars Serving Urban Areas

Recently, companies have focused on developing new technologies for air mobility using flying cars to alleviate road congestion in urban areas. A critical aspect to consider is the seamless integration of flying cars with their ground-based counterparts in the 5G network, where ground-based cars can provide transit functions, including access to vertiports and urban amenities. Additionally, flying and ground-based cars require various services with different requirements, such as path planning, remote diagnosis, and autonomous driving/piloting. Supporting these services in 5G networks is challenging due to the high mobility and stringent network latency requirements. Network slicing can be a promising solution to meet these requirements. However, the literature lacks comprehensive research on combining flying and ground-based cars in network slicing, where resource under-provisioning can cause the violation of service requirements. We propose three-level closed-loops for sliced resource block management to satisfy the delay budget constraint of flying and ground-based cars while avoiding resource under-provisioning. We present a reward function and continual learning that links these closed-loops. Furthermore, we use Ape-X as distributed deep reinforcement learning to maximize reward and continual learning to improve resource allocation via prediction. The simulation results demonstrate that the proposed approach maximizes delay requirement satisfaction.