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Record W2065977230 · doi:10.1587/transcom.e92.b.2231

Reduced Constraint Set Linear Program for Tone Reservation in Multicarrier Modulation

2009· article· en· W2065977230 on OpenAlexaff
A. Ghassemi, T. Aaron Gulliver

Bibliographic record

VenueIEICE Transactions on Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceReduction (mathematics)Fast Fourier transformBasebandReservationAlgorithmLinear programmingComputational complexity theoryOrthogonal frequency-division multiplexingInverseModulation (music)Tone (literature)Mathematical optimizationTelecommunicationsMathematicsComputer network

Abstract

fetched live from OpenAlex

Tone reservation (TR) has been proposed for peak to average power reduction (PAPR) in real-baseband multicarrier systems [1]. In this technique, the peak reduction signal is computed by optimization via linear programming (LP). As shown in [1], the computational complexity of the LP optimization is largely determined by the complexity of the inverse fast Fourier transform (IFFT) algorithm. In this paper, we use submatrices of the inverse fast Fourier transform (IFFT) to reduce the number of constraints in the LP-based optimization. We show that a significant complexity reduction can be achieved compared to the conventional TR algorithm, with similar PAPR reduction.

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 score0.697
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.065
GPT teacher head0.370
Teacher spread0.304 · 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
Published2009
Admission routes1
Has abstractyes

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