Averaged and Cluster DPDs for Beamforming Applications
Bibliographic record
Abstract
This paper addresses the problem of beam angle dependence of the Digital Predistortion (DPD) algorithm in the case of beamforming 5G transmitters. While a beam dependent DPD solution based on characterizing the system for each beam angle can offer good linearity performance, its implementation in practice requires continuous and fast adaptation of the DPD coefficients given that the beam forming vector is set to change every few milliseconds. This work proposes two alternative DPD solutions, the averaged DPD (A-DPD) and the cluster DPD (C-DPD), that trade-off signal quality for reduced dependence on the beamforming angle. Experimental results using a developed MIMO/beamforming test bed using 16 element phased array antenna operating at 2.35 GHz, demonstrates that the proposed C-DPD method is able to maintain an ACPR level within 5 dB of best-case scenario obtained by beam-dependent DPD, while using only a set of 3 DPDs over 90 degrees beam angle range.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".