On the Viability of Using a Subset of Transmitter- Observation Receivers for Training a Common DPD in Fully Digital MIMO Transmitters
Bibliographic record
Abstract
This letter lays the foundation for reducing the required number of transmitter-observation receivers (TORs) for training digital predistortion (DPD) in fully digital massive multiple-input, multiple-output (MIMO) transmitters. Specifically, it investigates the viability of applying the same, common set of DPD coefficients to linearize all RF chains in fully digital massive MIMO transmitters. First, it is shown that if all RF chains are operated at the same output power, the common set of DPD coefficients can be found by simply averaging the coefficients obtained by training each RF chain on its own. This suggests that only a few chains may be needed for training provided the chains are a representative sample. Experimental results are then conducted where one and three chains are used for training. It is found that when training for one chain, significant variations in normalized mean square error (NMSE) and adjacent channel power ratio (ACPR) of up to 9 dB across the chains are realized. For training with three chains, the common set of DPD coefficients can reduce the variation to 1–2 dB. Finally, after over-the-air (OTA) combining, excellent linearization performance is found for three chains.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".