New Digital Predistortion Training Method with Cross-Polarization Channel De-Embedding for Linearizing Dual-Polarized Arrays using Far-Field Observation Receiver
Bibliographic record
Abstract
This paper proposes a far-field (FF) -based digital predistortion (DPD) training method for linearizing dual-polarized (dual-pol) beamforming arrays in the presence of cross-polarization channel (XPC) interference experienced in the DPD FF-based observation receiver (OR). The cross-polarization interference (XPI) in the XPC can be attributed to the transmitter’s (TX’s) antennas, the probe used in the DPD FF OR, the OR’s over-the-air channel, as well as the mechanical misalignment between the TX antenna and the FF-based OR probe. Specifically, an XPC estimation and de-embedding technique using interleaved multi-tone test signals is proposed. Experiments conducted using a 4x4 dual-pol RF beamforming array operated at 38 GHz and excited by a 5G NR 200 MHz 256-QAM orthogonal frequency division multiplexing test signal are presented. The measurement revealed the capacity of the proposed technique to correct for the nonidealities in the XPC where the XPI was reduced from -10 dB to -40 dB. Furthermore, using the proposed DPD training method, the adjacent channel power ratio (ACPR) and error vector magnitude (EVM) improved from 26.7 dB and 10.96% to 36.38 dB and 3.3%, respectively, when a single-input-single-output DPD function was used. The ACPR and EVM were further improved by 3 dB and 1.1% when a dual-input-single-output DPD function was used.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".