Compressive sensing based vehicle information recovery in vehicular networks
Bibliographic record
Abstract
Vehicular ad hoc networks are expected to provide a reliable networking platform for cooperative safety communication systems. Those systems are of a broadcast nature and require to deliver both safety messages and vehicle tracking information while being interfered by other types of lower priority messages on the same channel. Vehicle tracking information are necessary to enable safety communication systems and intelligent transportation systems. Due to the large number of communicating vehicles and the amount of traffic exchanged in the broadcast mode, network congestion often occurs in vehicular communication systems. In this paper, a new methodology for avoiding such a network congestion is proposed. We identify the sparsity of the vehicle tracking information and propose a novel information recovery scheme. The proposed scheme reduces the amount of data exchanged due to vehicle tracking packets while providing a robust information reception at the receivers. Essentially, it utilizes compressive sensing to transmit a few measurements of the vehicles velocity vector that allows perfect recovery of the original vector at the receiver with a minimal error. Extensive simulation results demonstrate the effectiveness of applying compressive sensing in recovering vehicles tracking information, and relieving the network from unnecessary congestion due to the large amount of data exchanged.
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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 | 0.000 |
| 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 | 0.000 |
| 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".