Reducing Quantization Noise to Boost Efficiency and Signal Bandwidth in Delta–Sigma-Based Transmitters
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Bibliographic record
Abstract
This paper introduces two new techniques to enhance both efficiency and signal bandwidth in delta-sigma-based transmitters. At first step, a technique called quantization noise reduction (QNR), is introduced to enhance the coding efficiency. By filtering out part of the quantization noise in the whole band of the signal, while the signal envelope is maintained almost constant, the coding efficiency is improved without imposing any additional nonlinearity or distortion to the system. By utilizing this technique for an orthogonal frequency division multiplexing (OFDM) signal with 1.25-MHz bandwidth and 80 times oversampling, with 8.1-dB peak-to-average power ratio (PAPR), the coding efficiency is improved from 8.8% to 14.5% while the signal-to-noise distortion ratio (SNDR) of the system remains 43 dB. In the next step by using a controlled filtering on in-band quantization noise along with QNR technique, the bandwidth of the signal and efficiency are increased simultaneously without losing as much linearity. The second technique is called quantization noise reduction with in-band filtering or (QNRIF). QNRIF is applied on an OFDM signal with 1.25-MHz bandwidth, with the same PAPR and only 16 times oversampling. The result for the coding efficiency is improved from 7.7% to 18.7% with 41-dB SNDR.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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