Adaptive compensation for imbalance and offset losses in direct conversion transceivers
Bibliographic record
Abstract
It is pointed out that analog implementations of quadrature modulators and demodulators have deficiencies-primarily amplitude and phase imbalances and DC offset-that result in several troublesome problems for digital transceivers. An analysis and quantitative assessment of the losses due to analog implementations are presented. They include spurious tones and intermodulation products at the transmitter and a degraded BER (bit error rate) due to distortion of the signal constellation. The asymptotic degradation in BER performance in 16 QAM (quadrature amplitude modulation) for example is shown to be 1.1 dB for a 5 degrees phase imbalance and 0.65 dB for 5% gain imbalance. An adaptive procedure at the receiver for minimization of the imbalance and offset errors is developed. It is demonstrated that the LMS (least mean square) algorithm can be used to adapt the compensator in a manner similar to an equalizer, and that the computational load is the same as that of a three-tap equalizer.>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".