Analysis and Comparison of Low-Power 6-GHz <i>N</i>-Path-Filter-Based Harmonic Selection RF Receiver Front-End Architectures
Bibliographic record
Abstract
$N$-path switching systems using switched-series R-C networks are analyzed in the context of RF receiver front-ends, and it is shown that it is possible to mitigate the need to generate an accurate low power clock at high frequencies by operating at higher order harmonics of the switching frequency. For values of$N$that are an integer factor of 4 (i.e.,$N$= 4, 8, and 16), harmonic selection RF receivers’ architectures are presented using two feed-forward$N$-path switching filters and harmonic recombination at the baseband. Moreover, it is demonstrated how the harmonic recombination stage at the baseband can be reconfigured to select the third harmonic of the switching frequency rather than the fundamental to reduce the input frequency and power consumption of the multi-phase clock generator by a factor of 3. In addition, to analyze the performance of the proposed RF receiver architecture, multiple receivers have been designed and post-layout simulated in two CMOS technologies, TSMC 130 nm and TSMC 65 nm. The resulting 5.7–7.2-GHz RF receiver architectures allow for operation at the third harmonic of the LO frequency (i.e., 1.9–2.4 GHz), reducing power consumption and allowing for good performance metrics at both studied technology nodes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.002 |
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".