Crossover Digital Predistorter for the Compensation of Crosstalk and Nonlinearity in MIMO Transmitters
Bibliographic record
Abstract
This paper proposes a novel crossover digital predistorter (CO-DPD) model to compensate for crosstalk and nonlinearity in multiple-input multiple-output (MIMO) radio systems. Crosstalk can take place before or after the power amplifiers, designated herein as nonlinear and linear crosstalk, respectively. This paper demonstrates that, contrary to linear crosstalk, nonlinear crosstalk significantly affects the performance of the digital predistortion algorithm; and, it cannot be embedded and compensated for by the conventional channel matrix inversion algorithm at the receiver side of MIMO links. Based on a parametric study of system-level simulations and measurements, it was found that for a -20-dB nonlinear crosstalk level, the use of a memory multibranch polynomial predistorter, along with the channel matrix inversion algorithm, bounds the adjacent channel power ratio (ACPR) for a wideband code division multiple access (WCDMA) signal to -46 dBc and the error vector magnitude (EVM) for a world interoperability for microwave access (WiMAX) signal to -43 dB in MIMO links. The proposed CO-DPD was investigated and analyzed for the MIMO transmitter withN= 2, whereNis the number of RF front-ends. Its performance was evaluated through measurements, the experimental results obtained show that, in the presence of -20-dB nonlinear crosstalk, the proposed CO-DPD improve the ACPR of the WCDMA signal by 13 dB to -56.81 dBc from those obtained using a conventional digital predistorter. The same improvement was observed in the EVM measurement of the WiMAX signal, where the EVM decreases from -21.22 dB for the conventional DPD to -49.71 dB for the proposed CO-DPD.
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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) | 0.000 | 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 | 0.000 | 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".