The application of complex quantized feedback in integrated wireless receivers
Bibliographic record
Abstract
Integrated wireless receiver architectures, such as direct-conversion receivers, offer many advantages over the conventional heterodyne receivers including smaller size, lower cost, and reduced power consumption. However, the design of monolithic receivers, using direct-conversion, involves many challenges including dealing with low-frequency disturbances, namely, dc-offset and 1/f noise (especially in CMOS implementations), in-phase (I) and quadrature (Q) amplitude and phase mismatch, local oscillator (LO) leakage, and even-order distortions. A cost-effective method to minimize the low-frequency disturbances is to use ac-coupling in the baseband signal path. However, it results in baseline wander effects, especially in spectrally efficient modulation schemes such as quadrature amplitude modulation (QAM) where the baseband signal spectrum contains a significant amount of energy near dc. A system solution to mitigate the effects of low-frequency disturbance is presented in this paper. The quantized feedback (QFB) technique is used in conjunction with ac-coupling to minimize the baseline wander effects. A cross-coupled (CC) QFB extension to compensate for the receiver local oscillator phase error as well as the IQ mismatch is also described. Simulation results are presented to demonstrate the effectiveness of this complex QFB technique
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".