I/Q Imbalance Compensation in Wideband Millimeter-Wave Transmitters Using a Single Undersampling ADC
Bibliographic record
Abstract
In this paper, a new method to identify and compensate for the I/Q imbalance in wideband millimeter-wave transmitters using a single undersampling analog-to-digital converter (ADC) is proposed. The proposed method alleviates the need for an I/Q demodulator; hence, simplifying the structure and calibration procedure of the required transmitter-observation receiver (TOR). In addition, an algorithm for estimating the delay and phase offsets between the transmitter and the single-ADC TOR is devised. The presented algorithm improves upon the exhaustive search methods employed in the literature. Measurement results using an off-the-shelf I/Q modulator, driven by an 800 MHz test signal, show about a 5% reduction in the root normalized mean-square error (RNMSE) of its output signal when the proposed method is utilized. Further measurements were conducted to assess the performance of this method, in conjunction with digital predistortion (DPD), when a power amplifier (PA) is added to the transmitter chain. When compared with the application of DPD only, the I/Q imbalance correction allowed additional reductions of about 4% in the RNMSE and 1.5 dB in the adjacent-channel power ratio of the PA output signal. These improvements were obtained with ADC sampling rates as low as 20 times below the Nyquist rate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".