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Record W2030203700 · doi:10.1049/iet-cds.2010.0156

Gain and phase mismatch effects on double image rejection transmitter

2011· article· en· W2030203700 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Circuits Devices & Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTransmitterDirtPhase (matter)MathematicsStatisticsSensitivity (control systems)Image responseControl theory (sociology)Electronic engineeringComputer scienceTelecommunicationsEngineeringPhysicsRadio frequencyIntermediate frequencyArtificial intelligence

Abstract

fetched live from OpenAlex

Gain and phase mismatch effects of a double image rejection transmitter (DIRT) are examined by using error vector magnitude (EVM), image rejection ratio (IRR) and a union bound on the symbol error rate (SER). Although the DIRT has been utilised in many applications, the relationship between EVM and the IRR has not been previously reported. To analyse the relationship between EVM and IRR, the EVM functions are obtained using a complex envelope based matrix model and the IRR functions are approximated to provide insight into the gain and phase mismatch effects. Furthermore, the transmitter architecture has a lower sensitivity on both gain and phase mismatches under a proposed intermediate frequency (IF) gain condition, defined as gain condition-II. The system simulation results show that the IRR greater than 40 dBc can be achieved with 1 dB IF gain mismatch over the phase mismatch variations of −8° to 8°. The SER simulation results are also given for evaluating the system performance.

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.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.234
Teacher spread0.209 · 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