Spectral Regrowth Reduction for Digital Audio Broadcasting Using EER Amplifiers
Bibliographic record
Abstract
For the amplitude modulation (AM) radio broadcast band, there are several standards being considered for digital audio broadcast (DAB), all using orthogonal frequency-division multiplexing (OFDM). One of the main challenges to implementing digital audio broadcasting is that existing broadcast equipment was not designed for it. Unlike analog AM, which is a low bandwidth amplitude modulated signal, OFDM is a noise-like signal with significant amplitude and phase modulation. Most AM transmitters use an envelope elimination and restoration (EER) amplifier architecture, where the amplitude and phase components of the signal are amplified separately then recombined at the high power stage. The magnitude and phase component bandwidths are several times that of the input signal, and any filtering in the amplifier will result in spectral regrowth due to poor cancellation of this high frequency content. This paper develops the concept of using time-domain preprocessing on the signal to reduce the bandwidth expansion in EER amplifiers. The processing exploits the localization of the bandwidth expansion and its correlation with spectral regrowth at the output. The proposed algorithm identifies distortion- causing signal sections and replaces each one with an alternative signal trajectory. Using the processing, out of band emissions are reduced at the expense of increased computational complexity and error vector magnitude. Promising results are shown for the DRM 10 kHz digital signal, with reductions in spectral regrowth of up to 10 dB.
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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) | 0.002 | 0.001 |
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".