Application of cognitive radio principles to wireless channel sounding
Bibliographic record
Abstract
Channel sounding is generally conducted under the assumption that the wireless channel is free from interfering signals, i.e., in clear channels. As regulators begin to deploy new services in spectrum that is already occupied by other services, channel sounding must often be conducted in occupied channels with all the obvious attendant difficulties. Here, we propose alternative sense-decide-act strategies based upon cognitive radio principles that can mitigate interference when a conventional vector network analyzer is used to collect swept-frequency measurements of static channels. We show that a three-step process involving: 1) the use of sensing to reduce the probability that the channel sounder will interfere with a transmission that is underway, 2) robust estimation to eliminate outliers due to measurements corrupted by transmissions that began while the measurement was underway, and 3) estimation of the number of samples required to yield estimates of the channel response that fall within an acceptable confidence interval. Implementation of the scheme using an Agilent E8362C PNA vector network analyzer augmented by an external sensing receiver and control software yields pristine channel measurements even in the presence of significant interference.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".