On the calibration of the feedback receiver using reduced sampling rate and its application to digital predistortion of 5G power amplifiers
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an advanced calibration routine is proposed to determine the frequency response of a feedback receiver over a targeted linearization bandwidth, when only sub-Nyquist (aliased) samples are available. A new approach is then devised to apply a direct learning algorithm along with the proposed receiver calibration routine, and thus linearize a millimeter wave power amplifier (PA), driven by a modulated signal, using digital pre-distortion (DPD) with a reduced feedback sampling rate. The proposed new calibration routine and DPD approach are successfully applied to linearize a PA under test, operating at 24GHz and driven by single carrier 16QAM and carrier aggregated LTE signals of 200MHz modulation bandwidth using a feedback receiver with sampling rates of 2Gsps, 1Gsps and 500Msps. Adjacent channel power ratio of about 49dBc and normalized mean square error of about 2% are obtained at the linearized PA output using the three sampling rates.
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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