A Low-Power Wideband Receiver Front-End for NB-IoT Applications
Bibliographic record
Abstract
A low-power wideband receiver front-end is proposed for the narrow-band internet of things (NB-IoT) wireless standard to cover frequency bands from 0.6GHz to 1.4GHz. The front-end receiver integrates a programmable gain quadrature RF-to-baseband (BB) current-reuse receiver (CRR) architecture using conventional double-balanced passive-mixer with 25% duty-cycle local oscillator (LO) followed by a passive polyphase filter (PPF), high pass filter (HPF), programmable gain gm-C filter and RC-low pass filter (LPF). Low-power consumption is achieved by employing RF-to-BB CRR that shares a single supply with a wideband transconductor and a transimpedance amplifier (TIA). Moreover, power consumption is reduced by employing a single path PPF and gm-C filter to attenuate the blocker frequency at 7.5MHz above the intermediate frequency (IF). The proposed architecture is post-layout simulated in TSMC 130-nm CMOS technology. It achieves a voltage gain of 59.4dB, a noise figure (NF) of 4.9dB and a out-of-band IIP3 of -34.2dBm at 900MHz while consuming 1.8mW from a 1.2V supply at the maximum gain setting. In addition, the input matching covers several NB-IoT frequency-bands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".