A Low Complexity Approach for Calibration and Characterization of a Millimeter Wave Phased-Array Transceiver-Antenna Module
Bibliographic record
Abstract
A low complexity and cost-effective automated test method for estimation and compensation of combined TX/RX IQ imbalance and calibration of the phased array antenna elements in millimeter wave transceivers integrated with an antenna in package has been presented and validated. The proposed technique is an effective way of characterizing phase and amplitude imbalances even for a DUT (Device Under Test) that does not provide a direct access to the internal Local Oscillator (LO) or a divided version of that. A high-resolution digital-to-analog converter is used to provide analog input IQ signals to the input of transmitter chip and an analog-to-digital converter is used to digitize the output IQ voltages. The required voltage values equivalent to I and Q signals are generated in MATLAB or using the internal integrated processor and then applied to the IQ input of the radio transceiver. For the test in this approach, the transmitter and receiver can be either directly connected through a cable to waveguide or coupled through the antennas. By comparing input and output signals in the proposed setup, IQ imbalances are extracted and used for amplitude and phase compensation. Additionally, the method can be used for calibration of phased array transceivers. This approach can be periodically applied for different levels of the input power and over various conditions throughout the life time of the system in the field. Measurement results for a 60-GHz radio transceiver in package shows a very good agreement with the simulated ones and validates the proposed method.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".