Applications of Level Crossing Theory to Clipping Noise Characterization in Filtered OFDM Signals
Bibliographic record
Abstract
This paper applies the level crossing rate (LCR) and average fade duration (AFD) analysis to characterize clipping noise in filtered OFDM signals. Because orthogonal frequency division multiplexing (OFDM) signals exhibit complex Gaussian process behavior, well established results for the Rayleigh enve- lope of the correlated Gaussian process are used to derive the LCR and AFD statistics of the OFDM signal at the output of a square-root raised cosine shaping filter. The LCR and AFD information is then used to determine the statistics of the OFDM signal that passes through a soft-limiter, approximating at the baseband high power amplifier (HPA) memory-less nonlinearity. Simulation results corroborate theoretical derivations for the LCR and AFD statistics in real OFDM signals. The results obtained are important in predicting the clipping noise impact on the in-band and adjacent channel 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".