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Record W1600009275 · doi:10.1109/wisnet.2015.7127401

Six-port technology for MIMO and cognitive radio receiver applications

2015· article· en· W1600009275 on OpenAlexaff
Abul Hasan, Mohamed Helaoui, Noureddine Boulejfen, Fadhel M. Ghannouchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRadio receiver designPort (circuit theory)Electronic engineeringCognitive radioMIMOComputer scienceRadio frequencyRF front endMicrowaveSIGNAL (programming language)Frequency bandMicrowave transmissionElectrical engineeringSoftware-defined radioTelecommunicationsEngineeringTransmitterAntenna (radio)WirelessChannel (broadcasting)

Abstract

fetched live from OpenAlex

Six-port technique is a very accurate and cost effective method to determine the amplitude and phase of any input signal with respect to a known signal using only power or voltage measurements. This property of six-port technique has been variously exploited in a number of RF (radio frequency) and microwave applications. The present paper provides a very brief but wide review of the six-port technique for communication receiver applications. After providing a basic theory of six-port technique for single-band receiver application, the paper shows the usefulness of the technique for multi-band, MIMO (multiple-input and multiple-output) and CR (cognitive radio) receiver applications.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.028
GPT teacher head0.249
Teacher spread0.220 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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