An energy efficient fully integrated OOK transceiver SoC for wireless body area networks
Bibliographic record
Abstract
This work presents a low-power high-speed system-on-chip (SoC) for wireless body area networks (WBANs). The SoC is fully integrated with a 10 Mb/s on-off keying (OOK) RF transceiver, digital processing units, an 8051 micro-controlled unit (MCU), a successive approximation (SAR) ADC, and etc. The receiver adopts envelop detector (ED) based structure to improve the energy efficiency. Conventional ED based structure has a poor sensitivity when reaching a bit rate of Mb/s level. To resolve the problem, we design a receiving (Rx) front-end with 77 dB gain at 10 Mb/s data rate, and propose a novel supply isolation scheme to avoid the instability induced by such a high gain. The transmitter is based on a 2 GHz digitally controlled oscillator (DCO), which uses bond wires as inductors to further reduce the power at transmitting (Tx) mode. The digital baseband is designed by a near-threshold design (NTD) method for low power consumption. The chip is implemented with 0.13 μm CMOS technology, measured results show that the receiver consumes 0.214 nJ/bit at -65 dBm sensitivity, and the Tx energy efficiency is 0.285 nJ/bit at an output power of -5.4 dBm. In addition, the digital baseband consumes 34.8 pJ/bit with its supply voltage lowered to 0.55 V, indicating its energy per bit is reduced to nearly 1/4 of the super-threshold operation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".