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Record W2797830882 · doi:10.1109/lmwc.2018.2805459

Concurrent Dual-Band Low Intermediate Frequency Receiver Based on the Multiport Correlator and Single Local Oscillator

2018· article· en· W2797830882 on OpenAlexaff
Weiwei Zhang, Abul Hasan, Fadhel M. Ghannouchi, Mohamed Helaoui, Yongle Wu, Cuiping Yu, Yuanan Liu

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsLocal oscillatorMulti-band devicePhysicsElectronic engineeringElectrical engineeringIntermediate frequencyDual (grammatical number)Radio frequencyComputer scienceTelecommunicationsEngineeringAntenna (radio)

Abstract

fetched live from OpenAlex

A concurrent dual-band low intermediate frequency receiver, which is based on the multiport correlator, is proposed in this letter. Only one single local oscillator signal is used to demodulate the dual-band RF signals at the same time, which greatly reduce the receiver complexity. The system configuration, output power theory, as well as the power spectrum are analyzed in detail, and then an appropriate baseband signal recovery theory is put forward. Finally, a concurrent dual-band six-port receiver is tested under the long-term evolution signals with 3- and 5-MHz bandwidths. The measured error vector magnitudes are all lower than 2%, which verify the proposed system structure and the signal recovery theory.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.191
Teacher spread0.176 · 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
Published2018
Admission routes1
Has abstractyes

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