13.6 A 600μW Bluetooth low-energy front-end receiver in 0.13μm CMOS technology
Bibliographic record
Abstract
One of the main goals for the next generation of radios for wireless sensor and body-area networks (WSN and WBAN) is a sub-mW receiver (RX) compliant with energy-harvested supplies. In this direction, the Bluetooth standard has introduced a low-energy operative mode (BLE) with wider channel spacing (2MHz) and relaxed blocker tolerance. The minimum sensitivity required is −70dBm but even with a sensitivity 10dB lower the BLE receiver can have a noise figure close to 19dB [1]. Although linearity and noise specs have been significantly relaxed, the design of a sub-mW solution remains challenging since the power dissipation cannot be simply scaled with the spurious-free-dynamic-range (SFDR). In fact, the ultimate bound is set by the power burned in the voltage-controlled oscillator (VCO), which is used for the generation of the local oscillator (LO) necessary for the signal downconversion. Since, for a targeted phase noise, the current required by the VCO is inversely proportional to the quality factor of the resonator adopted, a straightforward approach is to use a high-Q tank like the FBAR used by Wang et al. [2]. However in low-cost CMOS processes, when high-Q resonators are not present, an alternative strategy is to share the VCO bias current with the other blocks of the RF front-end as in the LMV cell proposed by Tedeschi et al. [3].
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.054 |
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".