Non-linear trigonometric and hyperbolic chirps in multiuser spread spectrum communication systems
Bibliographic record
Abstract
Novel non-linear trigonometric and hyperbolic chirp waveforms are introduced in multiuser spread spectrum communication environment for binary data transmission. Three subclasses of non-linear trigonometric and hyperbolic chirp signals namely sinusoidal (sine-CSS), hyperbolic arc sinusoidal (asinh-CSS) and hyperbolic tangent (tanh-CSS) are described and their properties are given. Cross-correlations of these trigonometric and hyperbolic chirp signals are derived and presented as closed-form expressions. Moreover, the chirp rates in these signals are derived as a function of user in the multiuser environment using the orthogonal structure inherent in nonlinear chirp signals. A generic multiuser communication system model that employs non-linear chirp signals is presented and its bit error rate (BER) performance is analyzed in additive white Gaussian noise (AWGN) channel, and Rayleigh and Nakagami-m fading environments as a function of the number of users in the system, signal-to-noise ratio (SNR), and multiple access interference (MAI). Numerical results demonstrate that these proposed chirp signals with proper chirp rate assignment are very effective in reducing MAI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".