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Record W2892314816 · doi:10.1109/lmwc.2018.2860781

A Six-Port Transceiver for Frequency-Division Duplex Systems

2018· article· en· W2892314816 on OpenAlexaff
Xiaoxiong Song, Jianqiang Li, Yuting Fan, Feifei Yin, Yitang Dai, Kun Xu, Ke Wu

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersShanghai Jiao Tong UniversityBeijing University of Posts and TelecommunicationsNational Natural Science Foundation of China
KeywordsTransceiverDuplex (building)Port (circuit theory)Electronic engineeringElectrical engineeringDivision (mathematics)EngineeringFrequency dividerComputer scienceTelecommunicationsPower dividers and directional couplersMathematicsCMOS

Abstract

fetched live from OpenAlex

In this letter, a six-port transceiver for frequency-division duplex (FDD) systems is proposed and experimentally demonstrated. The proposed transceiver that consists of a single six-port correlator and four Schottky diodes can realize both modulation and demodulation simultaneously at different frequencies. To verify the proposed architecture, the six-port transceiver system prototypes are developed, and modulation and demodulation performances are evaluated by using quadrature amplitude modulation signals at 2.9 and 2.68 GHz, respectively. Moreover, the heterodyne demodulation for a six-port transceiver is proposed to mitigate the interferences from baseband modulation signals. The error vector magnitudes of FDD systems based on the proposed transceiver are found below 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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.225
Teacher spread0.203 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

Explore more

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