A high speed complex adaptive filter for an asymmetric wireless LAN using a new quantized polynomial representation
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a new number representation, the Quantized Polynomial Representation (QPR), which is used for building FIR filters where some quantization errors can be tolerated. The technique is based on the previously published Modulus Replication Residue Number System (MRRNS) but considerable savings are possible if polynomial quantization can be tolerated. The QPR can be used as a vehicle for Quadratic Residue Number System (QRNS) mapping of complex data, and the main computational architecture can be built with independent finite ring computational channels. We demonstrate this new technique on an asymmetrical Gigabit wireless LAN where the adaptive filter computes with complex arithmetic. We demonstrate area savings of up to 28% and power savings of up to 50%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it