A Blind Background Calibration Technique for Super-Regenerative Receivers
Bibliographic record
Abstract
In this brief, a blind background calibration technique for super-regenerative receivers (SRRs) is presented. The proposed calibration scheme is designed to help SRRs to maintain their high sensitivity and immunity to negative transconductance (<inline-formula> <tex-math notation="LaTeX">${\mathrm {-}\mathrm {G}}_{\mathrm {m}}$ </tex-math></inline-formula>) variations under process-voltage- temperature (PVT) variations. Unlike the conventional foreground <inline-formula> <tex-math notation="LaTeX">${-\mathrm {G}}_{\mathrm {m}}$ </tex-math></inline-formula> variations calibration techniques that require interruption of the receiver input, the proposed calibration technique employs input signal statistics and does not require interruption of the input bit-stream for extraction of the errors. The proposed scheme is based on an adaptive algorithm that compares the probability distribution of the pseudorandom-input (PI) stream and the output of the super-regenerative oscillator (SRO) and forces them to coincide at the end of the calibration. The proposed technique is implemented using a mixed-signal detection circuit and a finite state machine (FSM) that drives an 8-bit successive approximation register (SAR) to adjust the compensation current in the SRO. The simulation results successfully verify the effectiveness and reliability of the proposed calibration technique and show significant improvements in terms of SRR sensitivity under different process corners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".