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Record W2142712288 · doi:10.1109/vetecf.2000.883292

Channel precoding for π/4-DQPSK and MSK over frequency-selective slow fading channels

2002· article· en· W2142712288 on OpenAlexaff
J.S.Y. Lee, Weihua Zhuang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPrecodingPhase-shift keyingIntersymbol interferenceFadingMinimum-shift keyingElectronic engineeringComputer scienceZero-forcing precodingChannel (broadcasting)Adjacent-channel interferenceKeyingTelecommunicationsInterference (communication)Bit error rateEngineeringMIMO

Abstract

fetched live from OpenAlex

This paper presents channel precoding schemes to combat intersymbol interference (ISI) over a frequency-selective slow fading channel in wireless communication systems using /spl pi//4 differential quadrature phase shift keying (/spl pi//4-DQPSK) or minimum shift keying (MSK). Based on the dimension partitioning technique, the precoders predistort the phase of the transmitted signals in the forward link to combat ISI, keeping the transmitted signal amplitude constant. The precoding schemes can (i) ensure the stability of the precoders even in equalizing a non-minimum-phase channel, (ii) achieve ISI-free transmission without increasing the complexity of the portable unit receiver and (iii) reduce the envelope variations of transmitted signals such that a power efficient nonlinear amplifier can be used without causing undue distortion. Theoretical and simulation results are presented to demonstrate that the proposed channel precoders can outperform DFE when the ISI is severe and the precoding scheme for /spl pi//4-DQPSK can achieve a smaller envelope variation than the standard /spl pi//4-DQPSK.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.026
GPT teacher head0.246
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 designSimulation or modeling
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

Citations0
Published2002
Admission routes1
Has abstractyes

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