Joint RF pre-distortion and post-distortion linearization of small cell power amplifiers
Bibliographic record
Abstract
In this paper a power amplifier (PA) linearization technique is proposed where an RF predistorter (RFPD) is used in conjunction with a linearization amplifier (LA) to achieve a more effective joint pre-distortion and post-distortion linearization. This approach is suitable for use with small cells PAs because its power and cost overhead are scalable with the power range and cost of the PA. To demonstrate the technique's linearization capability, a prototype of the linearization scheme was implemented and applied to a 6-W class AB PA with a centre frequency of 850-MHz. When stimulated with a 15-MHz bandwidth modulated signal, the PA's adjacent channel leakage ratio and error vector magnitude were improved by more than 13 dB and 5 percentage points, respectively, at peak power, and up to 18 dB and 5.2 percentage points, respectively, at power backoff with the proposed linearization scheme. Furthermore, similar linearization was achieved when the PA was driven with a 40-MHz bandwidth modulated signal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".