An Efficient Handover Trigger Scheme for Vehicular Networks Using Recurrent Neural Networks
Bibliographic record
Abstract
The future of intelligent transportation systems has become in- creasingly dependent on the integration of heterogeneous wireless technologies over connected vehicular networks. In order to pro- vide efficient safety, traffic control management, and assistance to drivers. Managing the transition and migration of active commu- nication session of vehicles between different point of access is essential for seamless mobility. However, the rapid mobility of vehi- cles creates a challenging problem toward the efficiency of wireless communication between vehicles and access routers. To address this issue, an accurate mobility management protocol is needed, which anticipate the vehicles movement and network quality in order to derive a handover decision. In this paper, we present an efficient neural network-based handover trigger scheme for vehicu- lar networks to accurately predict the handover trigger time using time-series quality measurements of the network. We adopt a re- current neural network model to predict the upcoming sequence of received signal quality to derive a handover trigger estimation. In the performance evaluation, the proposed time-series estimation method shows high accuracy rates compared to several machine learning methods over generated mobility traces.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".