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Record W3172697875 · doi:10.1109/access.2021.3089582

Highly Efficient Front End Direct Conversion Receiver for 28-GHz Wireless Access Point

2021· article· en· W3172697875 on OpenAlexafffund
M. Akbari, Mohammadmahdi Farahani, Mansoor Dashti Ardakani, Ali Lalbakhsh, Saman Zarbakhsh, Serioja Ovidiu Tatu, Abdel-Razik Sebak, Omar M. Ramahi, Tayeb A. Denidni

Bibliographic record

VenueIEEE Access · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia UniversityUniversity of WaterlooInstitut National de la Recherche ScientifiqueMcMaster University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMacquarie University
KeywordsDemodulationPhase shift modulePhase-shift keyingRF front endComputer scienceQAMElectronic engineeringRadio receiver designWidebandQuadrature amplitude modulationAntenna (radio)TelecommunicationsElectrical engineeringChannel (broadcasting)EngineeringMicrowaveBit error rateTransmitter

Abstract

fetched live from OpenAlex

The manuscript presents the design and characterization of a wideband front-end receiver, based on hollow waveguide technology, for the wireless access point (WAP) applications. The Ka-band receiver is comprised of a low-loss beam-switching antenna network (BSAN) with two-dimensional (2D) scanning property. For demodulation, the receiver uses an efficient six-port network (SPN) that is composed of four 90° hybrid couplers and a 90° phase shifter. To suppress the phase imbalance on the entire band of interest, 27 GHz to 33 GHz, a frequency-independent phase shifter with a minimum phase error is suggested. The SPN receiver is integrated with antenna-in-package in waveguide technology to decrease the insertion losses and noise levels at the first stages. To determine the SPN phase errors, a theoretical and experimental analysis of real-time wireless data transmission utilizing phase-shift keying (PSK) and quadrature amplitude modulated (QAM) signal is conducted. Besides, channel capacity along with beamformer influence on channel performance improvement are determined.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations15
Published2021
Admission routes2
Has abstractyes

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