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Record W2972671096

An E-band Compact Frequency Division Duplex Radio Front-end Based on Gap Waveguide Technology

2019· article· en· W2972671096 on OpenAlexaff
Abbas Vosoogh, Milad Sharifi Sorkherizi, Vessen Vassilev, Ashraf Uz Zaman, Zhongxia Simon He, Jian Yang, Ahmed A. Kishk, Herbert Zirath

Bibliographic record

VenueChalmers Research (Chalmers University of Technology) · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsDiplexerBackhaul (telecommunications)RF front endTransmitterElectronic engineeringRadio frequencyElectrical engineeringFront and back endsMicrowaveWirelessEngineeringTelecommunicationsChannel (broadcasting)
DOInot available

Abstract

fetched live from OpenAlex

A compact module consisting of a novel integration of an antenna, a diplexer, and millimeter-wave active circuits for low latency wireless backhaul links working at E-band is presented in this paper. The proposed radio front-end module is built by four distinct layers which are vertically stacked with no electrical contact requirement between them based on gap waveguide technology. A 16×16 corporate-fed slot array antenna is successfully integrated× with a 5th order diplexer, as well as a transmitter (Tx) and a receiver (Rx) monolithic microwave integrated circuits (MMICs) in one package with a novel architecture and a compact form. The integrated radio front-end is able to simultaneously send and receive data by using a frequency division duplex (FDD) transmission scheme at 71-76 GHz and 81-86 GHz bands. A wireless data transmission is successfully demonstrated showing a data rate of 6 Gbit/s using 64 quadrature amplitude modulated (QAM) signal with a spectral efficiency of 4.4 bit/s/Hz. The proposed radio front-end provides the advantages of low loss, high efficiency, compact integration, and a simple mechanical assembly, which makes it a suitable solution for small cell backhaul links.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.258
Teacher spread0.236 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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