Reducing Quantization Noise to Boost Efficiency and Signal Bandwidth in Delta–Sigma-Based Transmitters
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".