LSTM-oriented Handover Decision-making with SVR-based Mobility Prediction in 5G Vehicular Networks
Bibliographic record
Abstract
The advancement of 5G technology is initiating a transformation era for Vehicular Networks (VN), enabling seamless communication among vehicles and other entities. Connected vehicles hold significant potential for improving traffic safety, and enhancing in-vehicle entertainment. With the increasing of vehicular applications, the necessity for reliable, high-bandwidth, and low-latency connections has become increasingly paramount. Ensuring consistent connections in dynamic vehicular settings remains an ongoing challenge, especially given the necessity for smooth Handovers (HO) between transmission points as vehicles move rapidly. Frequent handovers, due to the limitations of communication range, can impact user experiences, especially in safety-critical situations. One potential solution involves transitioning to network virtualization to address the challenges posed by ultra-dense networks and the limited communication range in 5G. To tackle these challenges, we present an approach based on mobility prediction for selecting virtual cells using Support Vector Regression (SVR) and making Handover (HO) decisions using Long Short-Term Memory (LSTM). Our method, named M-LSVR, focuses on forming user-centric virtual cells based on network attributes and real-time data. The dynamic adjustment of virtual cell size using predictive mobility ensures stability and reduces unnecessary handovers. Integrating mobility prediction with HO decision-making aims to establish a more stable connection, enhancing the quality of virtual cells in high-mobility vehicular environments. This approach aims to optimize the user experience by minimizing unnecessary tower switches and creating efficient, high-quality virtual cells in the 5G vehicular network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".