Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-Based Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power vehicles to offload computation-intensive and delay-sensitive applications, making it a promising solution. However, optimal resource allocation between edge servers is a complex issue due to vehicle mobility and dynamic data traffic. To address this issue, we propose a Lyapunov-based Multi-Agent Deep Deterministic Policy Gradient (L-MADDPG) method that jointly optimizes computing task distribution and radio resource allocation to minimize energy consumption and delay requirements. We evaluate the trade-offs between the performance of the optimization algorithm, queuing model, and energy consumption. We first examine delay, queue and energy models for task execution at the vehicle or RSU, followed by the L-MADDPG algorithm for jointly optimizing task offloading and resource allocation problems to reduce energy consumption without compromising performance. Our simulation results show that our algorithm can reduce energy consumption while maintaining system performance compared to existing algorithms.
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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 | 0.000 |
| 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) | 0.001 | 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".