Mitigation of distortion and memory effect in a concurrent dual-band six port receiver
Bibliographic record
Abstract
A new calibration technique for a concurrent dual band six port receiver (SPR) is presented. This calibration technique uses a modified memory polynomial (MP) to model the non-idealities and imperfections in the six port receiver architecture which includes the six port wave correlator and the diode detectors used. Using an inverse model, the in-phase and quadrature component of a transmitted signal is estimated. This is a black box model and the calibration coefficients are estimated by sending and receiving a known training signal. The least square algorithm is used in coefficient estimation. The calibration technique is used to receive concurrently two signals with different modulation and characteristics. First 64-quadrature amplitude modulation (QAM) and 16-QAM signals are received concurrently. In a second measurement test, WCDMA and LTE signals were concurrently received to validate the suitability of the proposed technique for realistic communication signals. The performance of the presented calibration technique was compared with the simple linear combination (LC) calibration technique. The MP calibration technique had EVMs of 1.6% and 1.3% while the EVMs using the LC calibration technique are 15.4% and 14.1% for the 64 QAM and 16 QAM signal pair respectively.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".