Digital-Twin-Empowered Interference Management for Multihop Internet of Vehicles Networks Over Millimeter Wave Bands
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Bibliographic record
Abstract
The Internet of Vehicles (IoV) generates massive data traffic and demands reliable end-to-end connectivity to achieve multi-Gbps throughput between vehicles and roadside units. Millimeter-wave (mmWave) bands, with their abundant bandwidth, are promising for high-throughput IoV networks. However, in this context, significant propagation losses, intermittent line-of-sight availability, and dynamic topology changes due to vehicle mobility present critical challenges. This article introduces resource allocation for vehicular networks (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {RAVEN}$ </tex-math></inline-formula>), a centralized resource management framework designed to address these challenges effectively. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {RAVEN}$ </tex-math></inline-formula> leverages a digital twin network (DTN) to optimize the end-to-end system capacity of multihop mmWave IoV networks by effectively managing co-channel interference among vehicles. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {RAVEN}$ </tex-math></inline-formula> comprises the following three steps: 1) a channel prediction step that utilizes DTN’s awareness of vehicular mobility and environmental contexts to predict site-specific channel gains for vehicular communication links; 2) a clustering step that partitions vehicles into nonoverlapping clusters, allowing vehicles within each cluster to share the same mmWave channel for data transmission, while simultaneously reducing co-channel interference; and 3) a multihop connectivity optimization step that provides a connected vehicular networking topology by jointly optimizing vehicle-to-vehicle and vehicle-to-infrastructure connectivity using a graph theory approach. A proof-of-concept of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {RAVEN}$ </tex-math></inline-formula> is developed by implementing a DTN on the Microsoft Azure Digital Twins platform while integrating real-world vehicular mobility traces, edge-cloud collaboration, and parallel computing. Extensive simulations demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {RAVEN}$ </tex-math></inline-formula> outperforms several benchmark schemes, and offers scalability and near real-time decision-making capabilities for managing interference in large-scale IoV networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it