Wideband Cascaded and Stacked Receiver Front-Ends Employing an Improved Clock-Strategy Technique
Bibliographic record
Abstract
A wideband cascaded receiver and a stacked receiver using an improved clock strategy are proposed to support the software-defined radio (SDR). The improved clock strategy reduces the number of mixer switches and the number of LO clock paths required to drive the mixer switches. This reduces the dynamic power consumption. The cascaded receiver includes an inverter-based low-noise transconductance amplifier (LNTA) using a feed-forward technique to enhance the noise performance; a passive mixer; and an inverter-based transimpedance amplifier (TIA). The stacked receiver architecture is used to reduce the power consumption by sharing the current between the LNTA and the TIA from a single supply. It utilizes a wideband LNTA with a capacitor cross-coupled (CCC) common-gate (CG) topology, a passive mixer to convert the RF current to an IF current, an active inductor (AI) and a 1/f noise-cancellation (NC) technique to improve the noise performance, and a TIA to convert the IF current to an IF voltage at the output. Both cascaded and stacked receivers are simulated in 22 nm CMOS technology. The cascaded receiver achieves a conversion-gain from 26 dB to 36 dB, a double-sideband noise-figure (NFDSB) from 1.4 dB to 3.9 dB, S11<−10 dB and an IIP3 from −7.5 dBm to −10.5 dBm, over the RF operating band from 0.4 GHz to 12 GHz. The stacked receiver achieves a conversion-gain from 34.5 dB to 36 dB, a NFDSB from 4.6 dB to 6.2 dB, S11<−10 dB, and an IIP3 from −21 dBm to −17.5 dBm, over the RF operating band from 2.2 GHz to 3.2 GHz. The cascaded receiver consumes 11 m from a 1 V supply voltage, while the stacked receiver consumes 2.4 m from a 1.2 V supply voltage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".