Detection and Reporting of Wireless Channel Congestion and Interference in Connected Vehicle Networks
Bibliographic record
Abstract
Connected vehicle (CV) wireless networks based on dedicated short-range communications (DSRC), European Telecommunications Standards Institute (ETSI) intelligent transportation systems (ITS-G5), and C-V2X technologies are susceptible to interference from both unintentional emitters and non-CV devices that may be authorized to share the same or adjacent bands. Such interference may lead to unreliable communication and disruption of CV services with a particular impact on safety-related applications. Surprisingly, considering the safety-critical nature of CV applications, there is no simple mechanism for detecting congestion or interference in such networks over wide areas. To address this gap, we propose and demonstrate that both interference and congestion in DSRC and ETSI ITS-G5 networks can be detected simply and inexpensively using capabilities that are already incorporated into the IEEE 802.11p standard, specifically the flags and statistics generated mostly in the physical layer (physical layer convergence procedure and physical medium dependent) state machines. Such a capability could be realized through a relatively minor software upgrade but would resolve a longstanding but underappreciated concern that CV networks are vulnerable to both congestion and a variety of short-range interferers but lack the capability to detect or report this. Although our focus was on DSRC and ITS-G5, 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".