Low‐power multi‐band injection‐locked wireless receiver in 0.13 <i>μ</i> m CMOS
Bibliographic record
Abstract
Abstract The design and analysis of a low‐power multi‐band injection‐locked wireless receiver, implemented in complementary metal–oxide–semiconductor (CMOS) 130 nm technology, for wireless sensor network (WSN) applications are presented. The proposed receiver composed of an injection‐locked oscillator (ILO), low‐noise amplifier (LNA), and an envelope detector utilizes non‐coherent detection based on the frequency‐to‐amplitude conversion property of the injection‐locking phenomena. A lock range enhancement method is proposed through analytically and numerically determining the optimum biasing point of the injection transistor. The lock range of divide‐by‐4 super‐harmonic injection‐locking dictated by the third‐order non‐linear coefficient of the injection transistor is first investigated. The receiver applies divide‐by‐4, divide‐by‐2, and fundamental injection to demodulate the frequency‐shift‐key (FSK) and ON/OFF‐key (OOK) modulated signals from 433, 860–868, 902–928, 950–956, and 2360–2400 MHz frequency bands while keeping the power consumption in sub‐mW range. Post‐layout simulation results demonstrate that the proposed design achieves a maximum data rate of 5 Mbps for both FSK and OOK signals. With two modes of operation (high‐band and low‐band), the receiver consumes 762 and 675 μ W of static power from a 0.7 V supply, achieving a sensitivity of −77 and −70 dBm at BER of 2 × 10 −3 . The FOMs for each mode are 152 and 135 pJ/b, respectively.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".