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Record W2071229443 · doi:10.1049/ip-com:20050133

Filter-bank design for multicarrier modulation systems with MPSK based on symbol-error-rate evaluation

2006· article· en· W2071229443 on OpenAlexaff
Y. Wang, Xiaobo Zhang

Bibliographic record

VenueIEE Proceedings - Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingCyclic prefixFilter bankModulation (music)AlgorithmDiscrete Fourier transform (general)Computer scienceFilter (signal processing)Bit error rateElectronic engineeringPhase-shift keyingCommunications systemMathematicsTelecommunicationsFourier transformEngineeringChannel (broadcasting)Fractional Fourier transformFourier analysisPhysicsAcoustics

Abstract

fetched live from OpenAlex

A novel complex-valued filter-bank design method, for generic multicarrier-modulation (MCM) systems with M-ary phase-shift keying (MPSK), is presented to minimise the symbol error rate (SER). The SER is evaluated by a newly derived closed-form formula, which can be used to evaluate both discrete-Fourier-transform (DFT)-based orthogonal-frequency-division-multiplexing (OFDM) and discrete-wavelet-multitone (DWMT) systems. Monte-Carlo numerical simulations are performed to verify the theoretical SER analysis and compare the error performance of the proposed MCM system with DFT-based OFDM and DWMT systems. It is shown that the derived formula is consistent with simulation results and the newly designed complex-valued filter-bank-based MCM system outperforms conventional DWMT systems in terms of the SER and has comparable SER performance with DFT-based OFDM systems with cyclic prefix (CP).

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.071
GPT teacher head0.289
Teacher spread0.218 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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Same venueIEE Proceedings - CommunicationsSame topicPAPR reduction in OFDMFrench-language works237,207