Interference excision in spread spectrum communications using adaptive positive time-frequency distributions
Bibliographic record
Abstract
There have been several techniques proposed to excise the interference in spread spectrum communications using time-frequency distributions (TFDs). TFDs localize any interference both in time and frequency domain, and are idealy suited for interference excision. Unfortunately, the commonly used TFDs suffer from a trade-off between time-frequency (TF) resolution and cross-terms suppression. This paper focuses on a new excision technique based on constructing a positive TFD of the received spread spectrum signal using an adaptive signal decomposition technique. By decomposing a signal into components, the interaction between components can be avoided, and the TFD constructed by combining the TFDs of the individual components would be free of cross-terms. Also, by using Gaussian functions as bases for decomposition, a high TF resolution of interference signals can be achieved. Construction of positive TFDs by signal decomposition techniques facilitates automatic denoising, and extraction of marginal and local properties of a signal such as instantaneous energy, power spectral density, instantaneous frequency and group delay. Interference excision is then achieved by suitably thresholding energy values in the TF plane. Initial results with synthetic models have shown successful performance with linear and quadratic chirp interferences. The interference excisions are highly localized in the TF plane with no cross-terms
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".