Single-Input Single-Output Digital Predistortion of Multi-user RF Beamforming Arrays
Bibliographic record
Abstract
We investigate the application of single-input single-output (SISO) digital predistortion (DPD) to mitigate the nonlinearity in millimeter-wave multi-user RF beamforming arrays. In principle, SISO DPD may not be sufficient to linearize such arrays as a multiple-input one may be required due to coupling between the sub-array antennas. Nevertheless, in practice, the coupling between antennas of different sub-arrays may be much smaller than the coupling between antennas of the same sub-array. It is first shown mathematically that if the coupling between sub-arrays is small, then SISO DPD is sufficient to linearize such arrays. This is then confirmed in practice by linearizing two co-located 64-element sub-arrays driven by 800 MHz modulated signals at 28 GHz. Using four sets of SISO DPD coefficients, the EVM and ACPR were improved from as much as 8% and 30 dBc to better than 2% and 40 dBc, respectively, across all steering angle combinations of the two sub-arrays.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".