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Record W4292347984 · doi:10.1109/access.2022.3199696

Filter Optimization for MFTN-OQAM Systems

2022· article· en· W4292347984 on OpenAlexaff
Nghia H. Nguyen, Ha H. Nguyen, Brian Berscheid

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceFilter bankRoot-raised-cosine filterFilter designFilter (signal processing)Prototype filterControl theory (sociology)Raised-cosine filterAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a novel and effective method for designing the pulse shaping filter for multicarrier faster-than-Nyquist with offset quadrature amplitude modulation (MFTN-OQAM) systems is proposed. The connection between the signal-to-interference ratio (SIR) and the filter coefficients is first established. Then, for a desired overall compression level taking into account compressions in both frequency and time domains, a simple convergence search is suggested to jointly find the optimal values of time and frequency compression factors as well as the filter coefficients to maximize the SIR under a spectrum localization constraint. The obtained results show that higher SIRs, and consequently better bit error rates, can be achieved by the proposed filters over the Martin filter that is commonly used in the filter-bank multicarrier OQAM (FBMC-OQAM) systems (i.e., without time or frequency compression). Moreover, when applying our method to FBMC-OQAM systems, the obtained results show that the original Martin filter is suboptimal as shorter filters are found having the same SIR, which translates to lower implementation cost and latency.

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 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: none
Teacher disagreement score0.903
Threshold uncertainty score0.355

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 head0.035
GPT teacher head0.277
Teacher spread0.242 · 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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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