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Record W2952588148 · doi:10.1109/tmtt.2019.2916788

A Quad-Channel 11-bit 1-GS/s 40-mW Collaborative ADC Enabling Digital Beamforming for 5G Wireless

2019· article· en· W2952588148 on OpenAlexaff
Fnu Aurangozeb, Farshid Aryanfar, Masum Hossain

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2019
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceSuccessive approximation ADCElectronic engineeringFlash ADCChannel (broadcasting)Signal-to-noise ratio (imaging)BeamformingMIMOCMOSElectrical engineeringTelecommunicationsEngineeringCapacitorVoltage

Abstract

fetched live from OpenAlex

A 4 × 11 bit 1-GS/s 40-mW collaborative analog-to-digital converter (ADC) is presented in a 65-nm CMOS for a four-channel multiple-input and multiple-output (MIMO) receiver. This work extends the maximal-ratio-combining (MRC) approach to define the ADC resolution in a multichannel environment to maximize the signal-to-noise ratio (SNR) in a power-constrained application. The ADC takes the advantage of the channel diversity by distributing the resolution according to the channel SNR. In addition, it utilizes the correlated information between channels to perform energy-efficient digitization of received signals. The collaborative ADC is designed with eight successive-approximation-register (SAR) ADC units each having a 6-bit of resolution and a 2-bit flash to monitor SNR. With the help of a coarse 2-bit flash, the ADC can detect change in channel SNR and accordingly reconfigure the four ADCs with a variable resolution from 6 to 11 bits with less than 1-ns mode switching time. This collaborative ADC performance is compared with four channel ADCs with uniform 11 and 9 bits of resolution. It reduces area and power by half and 41%, respectively, with only 10% degradation of overall signal-to-noise and distortion ratio (SNDR).

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 categoriesMeta-epidemiology (narrow)
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 score0.901
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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