Power Scalable Beam-Oriented Digital Predistortion for Compact Hybrid Massive MIMO Transmitters
Bibliographic record
Abstract
This article proposes a power scalable beam-oriented digital predistortion (PSBO-DPD) architecture suitable for linearizing PAs in compact hybrid massive multiple-input multiple-output (MIMO) transmitters, without the need to implement a dedicated observation path for each PA. Based on an assumption that all PAs are similar, the feedback configuration for only one PA is sufficient to acquire the nonlinear information of PAs in a given subarray, which are driven at different power levels due to amplitude beamforming. Therefore, the PSBO-DPD resolves the deficiency of current DPD techniques in hybrid beamforming array by estimating the output signal of each PA from the only captured output signal from one PA to construct the main beam signal of the subarray. The predistorter is identified based on the estimated main beam signal and the input signal driving the subarray. To estimate the outputs of each PA efficiently, a power scalable cascade PA model is proposed to reduce the computational complexity and associated overhead in terms of cost and energy consumption. Measurements on a 4-element antenna array with up to 100 MHz bandwidth signal are carried out to validate and bench mark the proposed PSBO-DPD against the existing DPD technique in hybrid massive MIMO transmitters.
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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) | 0.000 | 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) | 0.001 | 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 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".