Characterization of UWB Channel Impulse Responses Within the Passenger Cabin of a Boeing 737-200 Aircraft
Bibliographic record
Abstract
With its confined volume, cylindrical structure and high density of seating, the passenger cabin of a typical midsize airliner is significantly different from the residential, office, outdoor and industrial environments previously considered by IEEE 802.15.4a. We have characterized the shape of the ultrawideband (UWB) channel impulse response (CIR) and the fading statistics experienced by individual multipath components (MPCs) within that environment based upon 3300 complex frequency responses that we measured over the range 3.1-10.6 GHz at various locations aboard a Boeing 737-200 aircraft. We found that: (1) the shape of the CIR generally follows IEEE 802.15.4a's dense single-cluster model, but with negligible rise time if the link is line-of-sight, (2) both the mean and variance of the exponential decay constant tend to increase with transmitter-receiver separation and also as the receiving antenna drops from the headrest to the footrest of the passenger seats, and (3) small-scale fading of individual MPCs at each measurement location within the aircraft tends to follow a Nakagami distribution with a lognormally-distributedm-parameter that has a mean value of 0.2 dB and a standard deviation of 1.1 dB. We have modified IEEE 802.15.4a's CIR simulator to generate responses similar to those seen in the cabin.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".