A joint power and bandwidth allocation method based on deep reinforcement learning for V2V communications in 5G
Bibliographic record
Abstract
Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation system. When maintaining the high reliability and low latency in the vehicle-to-vehicle (V2V) links as well as large capacity in the vehicle-to-infrastructure (V2I) links, it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V communication. This paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization method. In this radio resources allocation framework, a vehicle or V2V link that is designed as an agent. And through interacting with the environment, it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power level. Because the proposed method can output continuous actions and multi-dimensional actions, it greatly reduces the implementation complexity of large-scale communication scenarios. The simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications.
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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 | 0.000 |
| Open science | 0.001 | 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".