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A Noise-Cancelling Harmonic Selection Receiver Using an N-Path Filter for 5G Applications

2021· article· en· W3177376899 on OpenAlexaff
Nakisa Shams, Frédéric Nabki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsBasebandNoise figureElectronic engineeringLocal oscillatorNoise (video)HarmonicFilter (signal processing)WidebandPhysicsRadio frequencyCMOSElectrical engineeringComputer sciencePhase noiseTopology (electrical circuits)EngineeringAcousticsAmplifier

Abstract

fetched live from OpenAlex

A wideband noise-cancelling harmonic rejection (NC-HR) RF receiver using two separate N-path filter-based down-conversion paths is presented to avoid amplification at harmonic blocker frequencies. The proposed harmonic blocker-tolerant architecture suppresses blockers placed at or around integer multiples of the local oscillator (LO) frequency. Moreover, the differential HR N-path switching system with resistive coefficients used in the main down-conversion path allows for the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> harmonic of the LO frequency to be selected, helping to reduce the dynamic power consumption of the multi-phase LO generator by a factor of three. Post-layout simulation results show that the 3.6 -7.2 GHz receiver implemented in a 65 nm CMOS process achieves a harmonic-rejection ratio (HRR) of 56 dB, a noise figure (NF) of less than 2.6 dB at a 130 MHz baseband frequency for a 7.2 GHz RF signal, with a power consumption of 12.6 mW including the LO current.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.954
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.258
Teacher spread0.214 · 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 teacher head, 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

Citations1
Published2021
Admission routes1
Has abstractyes

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