Hardware Implementation of Nakagami and Weibull Variate Generators
Bibliographic record
Abstract
An efficient implementation of Nakagami-mand Weibull variate generators on a single field-programmable gate array (FPGA) is presented. The hardware model first generates a correlated Rayleigh fading variate sequence and then transforms it into a sequence of Nakagami-mor Weibull fading variates. A biquad processor facilitates the compact implementation of a Rayleigh variate generator with arbitrary autocorrelation properties. A combination of logarithmic and linear domain segmentations along with piece-wise linear approximations is used to accurately implement the nonlinear numerical functions required to transform the correlated Rayleigh fading process into Nakagami-m or Weibull fading processes. When implemented on a Xilinx Virtex-5 5VSX240TFF1738-2 FPGA, the fading simulator uses only 1.6% of the configurable slices, 1.2% of the DSP48E modules and 3 block memories, while operating at 120 MHz, generating 120 million complex variates per second. The throughput can be increased up to 373 MHz with this FPGA if two separate clock sources are utilized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".