Digital Twin-Aided Vehicular Edge Network: A Large-Scale Model Optimization by Quantum-DRL
Bibliographic record
Abstract
This paper presents an innovative large model framework for optimizing the task offloading efficiency in vehicular edge networks, with a focus on ultra-reliable low-latency communication. We introduce a comprehensive model that integrates quantum computing with a deep reinforcement learning (DRL) model, supported by long short-term memory (LSTM) networks and a digital twin framework. This integration is designed to address the complexities of distributed vehicular edge computing networks, targeting efficient latency, energy, and quality-of-service management. Our model utilizes the parallel processing capabilities of quantum computing to enhance the DRL algorithm, effectively handling high-dimensional decision spaces. LSTM networks provide predictive insights into future network states in a digital twin framework and ensure real-time synchronization and adaptive strategy optimization. We employ a multi-agent framework, encompassing vehicles, unmanned aerial vehicles, and base stations, each utilizing a Nash equilibrium-based strategy for optimal decision-making, supplemented by incentive and penalty functions for reward optimization. Simulation results demonstrate notable improvements in task offloading efficiency, highlighting the model's efficacy over conventional DRL models.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".