Spectrum Sharing in Internet-of-Vehicles Networks: Digital Twin-Empowered Proactive Interference Management Approach
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Bibliographic record
Abstract
Internet-of-Vehicles (IoV) is envisioned to connect vehicles with each other, the surrounding environment, and central control centers. Spectrum sharing among active vehicular links is imperative to enhance the utilization of the spectrum licensed to IoV networks. However, co-channel interference among neighboring vehicular communication links poses a fundamental challenge when enabling spectrum sharing in IoV networks. This paper introduces a resource optimization framework, entitled PRISM (<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>roactive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u>esource optimization for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u>nterference and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>pectrum <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u>anagement), to mitigate co-channel interference in IoV networks. PRISM proactively allocates resources among a set of Vehicle-to-Infrastructure (V2I) communication links by accurately predicting the links’ positions and multi-path channel gains, thereby preventing outdated resource scheduling in dynamic IoV networks. PRISM is a three-step approach. In the first step, a multi-layer long short-term memory neural network and transfer learning are employed to predict the vehicles’ positions. In the second step, a digital twin network incorporating high-fidelity 3D maps and a ray tracing tool entitled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {Sionna}^{\textrm {TM}}$ </tex-math></inline-formula> is used to predict the V2I links’ multi-path channel gains. In the third step, a resource allocation algorithm is executed to efficiently determine V2I clusters and their transmit power allocations to maximize the overall system capacity. Simulation results show that PRISM enhances IoV network’s capacity up to 33% compared to non-proactive schemes, as validated through a simulation framework using real-world vehicular mobility traces.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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