Interference Detection and Reporting in IEEE 802.11p Connected Vehicle Networks
Bibliographic record
Abstract
Recent proposals to permit Wi-Fi to share the 5.9 GHz spectrum currently reserved exclusively for Dedicated Short Range Communications (DSRC) have elicited strong reactions from both the wireless and connected vehicle communities. A variety of lab-based studies and simulation-based investigations have been conducted in an attempt to resolve the issue but the results have not resolved the issue. One possible solution is to provide DSRC networks with the capability to detect and report interference to a central authority so that appropriate action can be taken by Wi-Fi operators or spectrum regulators to resolve the issue. Here we propose and demonstrate that interference to DSRC networks can be detected simply and inexpensively using capabilities already incorporated into the IEEE 802.11p standard. We further propose that a simple and inexpensive method for reporting interference to DSRC networks would be to clone a second instance of the subsystem used to report untrustworthy digital certificates within the DSRC Security Credential Management System (SCMS) and deliver reports of possible interference events to a Spectrum Misbehavior Authority. Such a combined capability would resolve a longstanding but underappreciated concern that DSRC networks are vulnerable to a variety of short-range interferers but lack the capability to detect or report same. Although our focus is on DSRC, similar considerations apply to related schemes such as C-V2X.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".