Compensation of Transmitter <i>I/Q</i> Imbalance in Millimeter-Wave MIMO Systems Using a Single Transmitter Observation Receiver
Bibliographic record
Abstract
This article proposes a new method to concurrently identify and compensate for the I/Q imbalance in millimeter-wave (mm-wave) multiple-input multiple-output (MIMO) direct-conversion transmitters (Txs) using a single transmitter observation receiver (TOR) fed with the combined outputs of the individual Tx chains. In addition, a signal training approach is proposed that minimizes in-band distortion while maintaining acceptable performance in the out-of-band region. The proposed I/Q imbalance mitigation method is validated by both the simulation and measurement results, using quadrature modulators arranged in one, two, and four Tx configurations for a compensation bandwidth of 4 GHz. The simulation results demonstrate the ability of the proposed method to obtain the same performance for one, two, and four Txs after compensation using an ideal power combiner. Simulations also reveal the sensitivity of the output signal quality to the isolation of the combiner. The experimental measurement results show a normalized mean-square error improvement from 14.5% to 2.22% and 3.46% for the one and four Tx configurations, respectively, using a power combiner with limited isolation. Finally, the method's ability to improve compensation accuracy is demonstrated as part of the digital predistortion (DPD) linearization of an mm-wave power amplifier (PA).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".