An Adaptive Power Level Control Algorithm for DSRC Congestion Control
Bibliographic record
Abstract
Vehicular industries and researchers have invested efforts to reduce avoidable accidents through the means of Vehicle to Vehicle (V2V) wireless communication using Vehicular Ad Hoc Networks (VANETs). Up-to-date information on the location, speed and other important parameters for each vehicle is shared with neighboring vehicles through the periodic exchange of Basic Safety Messages (BSMs). With a high vehicular density, network congestion can quickly arise in the 5.9GHz spectrum, rendering the system as unreliable because safety messages are not delivered and received on time. To alleviate this problem, there has been considerable research, in recent years, on distributed congestion control algorithms for VANETs. These approaches are generally based on rate control or power control of the transmitted packets. In this paper, we propose a novel, adaptive power control algorithm to reduce packet congestion in VANETs. Instead of requiring all vehicles to use the same power level, our approach allows each vehicle to dynamically adjust the transmit power of BSM packets, depending on its current speed. The goal is to prioritize which other vehicles will receive BSM packets from a given vehicle. Our simulation results demonstrate the advantages of the proposed algorithm regarding commonly used metrics such as packet loss, Beacon Error Rates(BER), Channel Busy Time(CBT) and Inter-Packet Delay(IPD).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".