Composite Neural Network Digital Predistortion Model for Joint Mitigation of Crosstalk, $I/Q$ Imbalance, Nonlinearity in MIMO Transmitters
Bibliographic record
Abstract
Multi-input multi-output (MIMO) is anticipated to be a prominent technique proposed in the wireless communications to improve the system capacity and data rates of the wireless networks. However, the MIMO transmitter suffers from imperfections, such as crosstalk, power-amplifier (PA) nonlinearity, in-phase and quadrature (I/Q) imbalance, and dc offset. Investigating these effects, this paper proposes neural network (NN)-based digital predistortion (DPD) as an integral solution to compensate for crosstalk, PA nonlinearity, I/Q imbalance, and dc offset imperfections simultaneously in MIMO transmitters. The proposed NN DPD model provides a one-step single-model digital mitigation solution to multibranches of MIMO transmitters. With the increase in the dimensions of MIMO transmitter, the proposed NN-based DPD model provides a better compensation for transmitter imperfections and also reduces the complexity as compared to the state-of-the-art DPD methods. The proof-of-concept is provided with the 2×2 and 3×3 MIMO transmitters in the presence of strong PA nonlinearity, crosstalk, I/Q imbalance, and dc offset for homodyne as well as heterodyne transmitters' cases.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".