Centralized Digital Predistortion in 6G: Distributed Task Offloading and Scheduling for Complexity-Reduced PA Linearization
Bibliographic record
Abstract
To improve power efficiency while using ultra-wide bandwidth, digital predistortion (DPD) is essential in 6G base station (BS) design to linearize the nonlinearity distortion of power amplifiers (PA). However, traditional DPD methods rely exclusively on localized PA distortion estimation in distributed BSs, which could become extremely complex and costly due to the increased number of BSs and growing scale of antenna arrays in 6G. Therefore, simplified and centralized DPD design strategies tailored to 6G are urgently required to reduce the DPD complexity in distributed BSs and the exponentially in-creased costs throughout the network. In this paper, we propose a centralized DPD scheme by offloading and scheduling the distributed PA distortion estimation tasks from local BSs to a centralized training device so that the overall complexity of the PA linearization is significantly reduced. To avoid unnecessary PA data transmission, lightweight onsite analysis is designed at each BS by comparing the PA operating conditions, which can minimize the communication resource wastage for task offloading. Long-term distortion analysis is further conducted at the training device to support intelligent scheduling of distortion re-estimation and model sharing among similar PAs. Simulation results demonstrate that the proposed approach significantly achieves efficient PA distortion estimation and linearization without sacrificing accuracy.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".