MétaCan
Menu
Back to cohort
Record W3153018133 · doi:10.1109/tcsii.2021.3073059

Spacetime Frequency-Multiplexed Digital-RF Array Receivers With Reduced ADC Count

2021· article· en· W3153018133 on OpenAlexaff
Najath Akram, Viduneth Ariyarathna, Soumyajit Mandal, Leonid Belostotski, Theodore S. Rappaport

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Calgary
FundersDivision of Electrical, Communications and Cyber Systems
KeywordsBeamformingElectronic engineeringReduction (mathematics)Path lossAntenna (radio)Computer scienceChannel (broadcasting)Transmission (telecommunications)Analog-to-digital converterAntenna arrayDegrees of freedom (physics and chemistry)Array gainWirelessElectrical engineeringMathematicsPhysicsEngineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

Wireless systems operating at mm-wave frequencies require dense antenna arrays to achieve directional gain for overcoming high path loss. Digital mm-wave arrays retain spatial degrees of freedom, but require a dedicated analog to data converter (ADC) per spatial channel, leading to undesirably high receiver complexity, large ADC count, and power consumption. This brief exploits directional sparsity to reduce the number of receivers and ADCs with minimal loss in performance. A multidimensional (MD) linear transformation using transmission lines and a K : 1 combiner is used to reduce the number of ADCs by a factor K. Simulations verify that the proposed method can lead to better than 50% ADC complexity reductions (for K ≥ 2) for linear arrays and more than 75% ADC complexity reduction (for K ≥ 4) for rectangular arrays when sparsity conditions are met. Unlike in analog-digital hybrid beamforming, where a phased-array combines K channels to a single ADC, the proposed method does not lead to loss of spatial degrees of freedom.

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 score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Circuits & Systems II Express BriefsSame topicMicrowave Engineering and WaveguidesFrench-language works237,207