On the Double Doppler Effect Generated by Scatterer Motion
Bibliographic record
Abstract
In a time-varying transmission channel, the received signals are subject to frequency shifts due to the Doppler effect. The Doppler frequency is dependent on the carrier frequency and channel variation rate. In a fixed wireless channel, the channel variations are caused by scatterer motion. In this paper, we investigate analytically the Doppler effects generated by scatterer motion under different scatterer velocity distributions using the ring-of-scatterers geometric model. The proposed model considers Doppler frequency components caused by scatterer mobility to both received and reflected signals at each scatterer, and therefore is called the double Doppler model. The analytical curves are compared and statistically tested with several measurement results published in the literature. At low scatterer speeds, e.g., generated by moving foliage, the exponential velocity distribution is an appropriate model to describe the time-varying nature of the fixed wireless channels. The curve fitting results also show that our analytical model better approaches the empirical curves than the single Doppler model does. However, further investigation is still needed to find a suitable scatterer velocity distribution that closely describes the double Doppler effect in fast-variation fixed wireless channels, e.g., caused by passing vehicles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".