5G Open RAN-Based Network Slicing for Connecting Ground-Based and Flying Cars Serving Urban Areas
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".