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Record W3093606440 · doi:10.1109/access.2020.3032555

Compensation of Phase Noise and IQ Imbalance in Multi-Carrier Systems

2020· article· en· W3093606440 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

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCyclic prefixPreambleComputer sciencePhase noiseCompensation (psychology)Impulse (physics)Channel (broadcasting)Control theory (sociology)Transmission (telecommunications)AlgorithmOrthogonal frequency-division multiplexingElectronic engineeringTelecommunicationsArtificial intelligenceEngineeringPhysics

Abstract

fetched live from OpenAlex

Compensation for the impacts of phase noise (PN) and in-phase and quadrature (IQ) imbalance on cyclic-prefix (CP) based multi-carrier modulation systems in the presence of imperfect channel estimation is considered in this paper. A unified two-stage algorithm is proposed. In the first stage, IQ imbalance parameters and channel impulse response are estimated based on the transmission of a preamble which is designed in such a way that the estimation of IQ imbalance does not require any knowledge about the channel and PN. Given the estimates from the first stage, the impacts of IQ imbalance and PN are subsequently compensated in the second stage based on the transmission of pilot symbols. The proposed algorithm is further extended to a MIMO system that employs a diversity technique. Simulation results are presented for a wide range of PN and IQ imbalance scenarios to corroborate the effectiveness of the proposed algorithm.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.287

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 head0.062
GPT teacher head0.322
Teacher spread0.260 · 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