Recommended Practices for Millimetre-Wave Channel Sounder Verification
Bibliographic record
Abstract
Various hardware and software defects may distort or impair the channel-measurement data produced by wireless channel sounders.These effects may be especially pronounced at millimeter-wave frequencies.Users, developers and manufacturers of such instruments require standardized methods either to identify and correct such defects or to give confidence that a set of channel measurement data produced by a given channel sounder is suitable for inclusion in a pooled database.The ad hoc and incomplete verification methods in common use today are inadequate for the task.The IEEE Standard Association P2982 standards development effort and its consensus-based approach is the best method to achieve methods that are complete, effective, and economical and which will be widely accepted and adopted.The P2982 standard will recommend methods for verifying millimeter-wave channel sounder performance based upon comparison of processed channel measurement data to either theory or an artifact having known characteristics.Such measurement data may be collected in situ, under controlled conditions or by comparison to a reference measurement.The verification results may be used to: 1) identify and correct shortcomings in channel sounder performance and/or post-processing techniques or 2) give confidence that a given set of channel measurement data is suitable for inclusion in a pooled database.
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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 | 0.057 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.031 |
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".