TacNet: A Tactic-Interactive Resource Allocation Method for Vehicular Networks
Bibliographic record
Abstract
To support safety driving and various on-board services, efficient resource allocation is crucial for the promising implement of vehicle platooning in intelligent transportation systems (ITSs). The resource allocation of vehicle-to-everything (V2X) communications for vehicular platoons is studied in this article. First, a multiobjective function is formulated to jointly optimize sub-band and power allocation to satisfy Quality-of- Service (QoS) in vehicular networks. With the advantage of dealing with complex decision-making problems in multiagent systems, distributed multiagent deep reinforcement learning (MADRL) stands out for resource allocation of vehicular networks. However, it faces the challenge of cooperation aging when every agent is only learning from information of others to form a cooperation model in the training process. Considering the random and dynamic combination of vehicles in vehicle platooning, a tactic-interactive MADRL method named as TacNet is then proposed to improve the cooperation efficiency of multiple agents. In TacNet, the tactics of other agents will be encoded and transmitted through interactive communications among agents. In addition, with the development of vehicular edge computing (VEC), digital twin (DT) networks are constructed to assist offloading computation-intensive resource allocation tasks in vehicles to the edge. The superiority of the proposed method is verified through extensive simulation results, which refers to convergence and performance of satisfying diversified QoS requirements compared with state-of-the-art MADRL methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".