Leveraging Dynamic Stackelberg Pricing Game for Multi-Mode Spectrum Sharing in 5G-VANET
Bibliographic record
Abstract
5G enabled Vehicular ad hoc network (5G-VANET) plays a promising role to support diverse intelligent transportation system (ITS) applications. There are three types of communication modes in 5G-VANET: cellular mode, reuse mode and dedicated mode, i.e., vehicle users (VUEs) communicate with each other using the cellular network spectrum directly, in an underlay sharing way, and utilizing the allocated dedicated spectrum, respectively. However, how to dynamically share the multi-mode spectrum to optimize the network performance (i.e., network throughput) in 5G-VANET is a challenging task due to the high dynamic VANET environment and network resource heterogeneity. In this paper, we propose a dynamic Stackelberg pricing game enabled multi-mode spectrum sharing solution in 5G-VANET. In specific, we develop an access price strategy for different spectrum sharing modes considering the cellular BS's revenue and whole network throughput, while VUEs can select communication modes in a distributed way and dynamically change the selections through an evolutionary game. Through testing different traffic scenarios generated by SUMO, we demonstrate the effectiveness of the proposed algorithm. Specifically, the proposed algorithm can improve the total transmission rate of VANET by at least 20% compared with the random selection method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".