5G Open RAN-Based Network Slicing for Connecting Ground-Based and Flying Cars Serving Urban Areas

Recently, companies have increasingly developed new technologies for urban air mobility using flying cars to alleviate road congestion. Unfortunately, the seamless integration of flying cars with their ground-based counterparts in the 5G network, where ground-based cars can support flying cars in proving transit functions, has not yet been fully investigated. Flying and ground-based cars require various services, such as autonomous driving/plot, path planning, and remote di-agnosis. Supporting these services in 5G networks is challenging due to the high mobility and stringent network latency requirements. Network slicing is a promising solution. However, a comprehensive research on combining flying and ground-based cars in network slicing is still missing in the literature. Under-provisioning of radio resources can result in the violation of service requirements, while radio resource over-provisioning can cause resource under-utilization. We propose two-level closed-loops for Resource Block (RB) management to satisfy the delay budget constraint of flying and ground-based cars simultaneously while avoiding radio resource under/over provisioning. We design two closed-loops to map slices and services to Open RAN elements for radio resource scheduling and to allocate RB to cars. We propose a zero-touch RB adjustment approach and link these two closed-loops through the reward function of a deep reinforcement learning algorithm that optimizes slice resources in real time. Results show that our approach maximizes delay requirement satisfaction while preventing RB under/over-provisioning.