Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks
Bibliographic record
Abstract
Energy-efficiency (EE) optimization of long-term evolution (LTE) networks dedicated to vehicle-to-vehicle communications (LTE-V2V) is critical for connected vehicles. In this paper, we integrate perimeter control methodologies from transportation science into EE optimization to make vehicular communications adaptive to temporal-spatial dynamics of macroscopic traffic flows in multiple urban regions. Specifically, we develop a hierarchical framework of joint LTE-V2V EE optimization and perimeter traffic flow control. Its goal is to minimize the total traffic network delay, defined as the integral of the vehicle accumulations in the urban regions over a prediction horizon time, meanwhile maximizing the energy efficiency of the LTE-V2V communications in the same regions. We propose a model predictive perimeter controller at a low level, using a macroscopic fundamental diagram (MFD) to capture the relationship between the traffic density and the outflow of each urban region. We also propose a high-level EE optimization model and an iterative algorithm, considering the multi-region coordinated traffic dynamics, to jointly optimize vehicular transmission power and beacon frequency. Simulation results validate our proposed models and show that our method outperforms the latest solutions by improving at least 9.57% EE of the multiple regions. Our method can also provide 27.69% improvement in resource utilization fairness, indicating a fairer EE performance distribution among these regions.
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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) | 0.001 | 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".