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Record W2799902197 · doi:10.1109/cicc.2018.8357013

A low-power sub-GHz RF receiver front-end with enhanced blocker tolerance

2018· article· en· W2799902197 on OpenAlexaff
Zhonghong Jiang, D.A. Johns, Antonio Liscidini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBasebandRF front endElectrical engineeringCMOSNoise figureRadio frequencyTransistorPower (physics)RF power amplifierLow-power electronicsFront and back endsLow-noise amplifierSensitivity (control systems)Radio receiver designEngineeringElectronic engineeringPower consumptionPhysicsAmplifierTransmitterVoltage

Abstract

fetched live from OpenAlex

This paper presents a class-AB sub-GHz RF receiver front-end suitable for ultra-low power application. By exploiting transistors' class-AB operation in both the RF and baseband sections, the receiver front-end achieves a very low sensitivity and an elevated blocker tolerance while keeping a low power consumption. Such performance makes the receiver suitable for both short-range (e.g. 802.15.4) and long-range (e.g. LoRa) applications. The proposed RF front-end has been implemented in 0.13um CMOS technology, operates in the 868/915MHz ISM bands, and exhibits an in-band gain of 50dB, noise figure of 2.7dB, out-of-band HP3 of +2dBm, out-of-band IIP2 of +37dBm, out-of-band P1dB of -10.5dBm, while draining 2.1mA from a 1.2 V supply.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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.

Opus teacher head0.006
GPT teacher head0.189
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2018
Admission routes1
Has abstractyes

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