System Distortion Model for the Cross-Validation of Millimeter-Wave Channel Sounders
Bibliographic record
Abstract
Because millimeter-wave directional channel measurements are time-consuming and expensive to collect, there is considerable interest in combining measurement data obtained with different channel sounders in order to yield more comprehensive datasets. The simplest way to verify that the results obtained with these different instruments in a given environment are comparable would be to transport the channel sounders to that environment, collect and process measurement data, and then compare the results. Because this is rarely feasible, we propose an alternative method that is much more practical. It involves: 1) Generating an ideal three-dimensional channel impulse response that corresponds to a scenario of interest, 2) Degrading the ideal response by applying a distortion model that capture the factors that limit the spatio-temporal resolution and dynamic range of each channel sounder, and 3) Applying the multipath component (MPC) extraction techniques used by the channel sounder to the distorted response. After the last step, one will observe: a) correctly estimated, b) incorrectly estimated, c) missing, and d) spurious MPCs. Discrepancies between the ideal and distorted responses will be readily apparent and the performance of the channel sounders can be easily compared in a given environment. The effort required to fully characterize the three-dimensional patterns of the transmitting and receiving antennas is considerable and further work is required to determine the corresponding accuracy requirements.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".