Gain and phase mismatch effects on double image rejection transmitter
Bibliographic record
Abstract
Gain and phase mismatch effects of a double image rejection transmitter (DIRT) are examined by using error vector magnitude (EVM), image rejection ratio (IRR) and a union bound on the symbol error rate (SER). Although the DIRT has been utilised in many applications, the relationship between EVM and the IRR has not been previously reported. To analyse the relationship between EVM and IRR, the EVM functions are obtained using a complex envelope based matrix model and the IRR functions are approximated to provide insight into the gain and phase mismatch effects. Furthermore, the transmitter architecture has a lower sensitivity on both gain and phase mismatches under a proposed intermediate frequency (IF) gain condition, defined as gain condition-II. The system simulation results show that the IRR greater than 40 dBc can be achieved with 1 dB IF gain mismatch over the phase mismatch variations of −8° to 8°. The SER simulation results are also given for evaluating the system performance.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".