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Record W1831550379 · doi:10.1109/pacrim.2001.953688

Frequency domain equalization for high data rate multipath channels

2002· article· en· W1831550379 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceAlgorithmMultipath propagationFrequency domainBit error rateFast Fourier transformPhase-shift keyingElectronic engineeringDelay spreadTime domainAdaptive equalizerEqualization (audio)Channel (broadcasting)TelecommunicationsDecoding methodsEngineering

Abstract

fetched live from OpenAlex

High data rate transmission over multipath channels requires equalizers of long impulse response. In such cases, frequency domain implementation of the block least mean square (BLMS) algorithm offers low complexity growth relative to time domain techniques. The work presented herein is devoted to a study of the fast BLMS (FBLMS) algorithm implemented in the frequency domain using overlap-save sectioning and the fast Fourier transform (FFT). We examine the bit error rate (BER) performance for high data rate quadrature phase shift keying (QPSK) transmission over a multipath channel as well as the computational complexity of the FBLMS equalizer in comparison to the time domain implementation. Finally, we show how normalizing the step size of the FBLMS algorithm according to the power distribution of the input process results in a significant improvement in the equalizer convergence relative to the time domain methods.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score0.521

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 head0.081
GPT teacher head0.277
Teacher spread0.196 · 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

Quick stats

Citations16
Published2002
Admission routes2
Has abstractyes

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