Multi-Rate Hybrid Predistortion with Reduced Sampling Rate and Resolution for 6G Power Amplifier Linearization
Bibliographic record
Abstract
abstract-To achieve cost-effective and energy-efficient 6G base station (BS) deployment, digital predistortion (DPD) is essential to concurrently maintain high power efficiency and optimal linearity of power amplifiers (PA). However, dramatically increased communication signal bandwidth and carrier frequency in 6 G lead to extremely high sampling rate requirements in conventional DPD, which significantly increases DPD implementation cost and overall power consumption. In this paper, we propose a novel multi-rate hybrid predistortion scheme to reduce the cost and power consumption for 6 G PA linearization by lowering the sampling rate and analog-to-digital converter (ADC) resolution concurrently. Specifically, a new adaptive PA distortion estimation method is proposed using pseudo-preambles with varying lengths to mitigate the impact of low ADC resolution and low feedback loop sampling rate, while guaranteeing the estimation accuracy without inducing additional hardware complexity. Furthermore, a new PA distortion characteristics specified multi-rate hybrid predistortion scheme is proposed that tailors different sampling rates to the decomposition and linearization of estimated PA static and dynamic distortions, thus further mitigating low baseband sampling rate impact with guaranteed linearization performance. Simulation results demonstrate the proposed predistortion scheme achieves satisfactory PA linearization performance and power efficiency with dramatically reduced sampling rates and resolution.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".