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Record W1487502429 · doi:10.1109/mssc.2015.2417096

Fundamentals of Modern RF Wireless Receivers: A Short Tutorial

2015· article· en· W1487502429 on OpenAlexafffund
Antonio Liscidini

Bibliographic record

VenueIEEE Solid-State Circuits Magazine · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsCMOSTransceiverWirelessRadio frequencyBandwidth (computing)RF front endElectronic engineeringElectrical engineeringComputer scienceLinearityAnalog signal processingSignal processingTelecommunicationsEngineeringDigital signal processing

Abstract

fetched live from OpenAlex

To be compliant with multistandard applications, the RF front end of a modern transceiver must satisfy several challenging tasks such as large operative bandwidth, low noise, and high linearity. Over the years, addressing such requirements has significantly changed the radio architecture toward an ultimate solution based on current mode signal processing and passive mixers. In this article, after a brief description of the typical structure of a wireless receiver, voltage and current mode signal processing will be compared showing why a fully current-mode approach is more suitable in deep-scaled CMOS technologies [1], [2].

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.022

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 head0.040
GPT teacher head0.262
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations10
Published2015
Admission routes2
Has abstractyes

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