Millimeter-Wave Receiver With Non-Uniform Time-Approximation Filter
Bibliographic record
Abstract
This article presents a non-uniform (NU) time-approximation filter (TAF) technique for a wireless receiver (RX) to reject unwanted blockers. The proposed NU TAF leverages the alias-spreading property of NU sampling (NUS) and a TAF that approximates a finite impulse response (FIR) filter response in the time domain, achieving an overall flexible filter response with a higher attenuation factor. The filter response can be readily reconfigured by changing the NU sequence and/or the TAF waveform without adjusting the passive component value. In addition, a quad-switch gated integrator is proposed to significantly reduce the power consumption by sharing the current among the${G}_{m}$cells. Detailed theoretical analysis on NU-TAF operation and RX implementation considering circuit non-idealities are provided to explore the design tradeoffs of the proposed techniques. A proof-of-concept millimeter-wave RX is implemented in the 28-nm CMOS process. Thanks to the NU TAF, the RX prototype achieves >45-dB blocker rejection with a 33.7-GHz carrier frequency. The EVM measures −30.9 dB using a 100-MSymbol/s 64-QAM signal in the presence of a 10-dBc out-of-band (OOB) blocker.
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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) | 0.001 | 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 | 0.001 |
| 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".