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Record W2170656022 · doi:10.1109/wiad.2011.5983276

Fast transform for multi-carrier wireless communications

2011· article· en· W2170656022 on OpenAlex
Mike Sabelkin, François Gagnon

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceOrthogonal frequency-division multiplexingWavelet transformMultiplexingHaar waveletWavelet packet decompositionWirelessDiscrete wavelet transformConstant Q transformFast Fourier transformHarmonic wavelet transformFrequency-division multiplexingAlgorithmWaveletTelecommunicationsChannel (broadcasting)Artificial intelligence

Abstract

fetched live from OpenAlex

The proposed fast transform is originated from the Haar wavelet. The N-point (N=2 power d) fast transform requires no multiplications in case d is even, and N real multiplications with constant in case d is odd, and it uses at least 33 percent less real additions than the Fast Fourier Transform. The proposed fast transform is developed to reduce complexity of Wavelet Packet Multiplexing (WPM). The same fast transform algorithm can be used for both multiplexing and demultiplexing of data streams. Simulations show that multi-carrier wireless communication systems can profit from use of WPM based on the proposed transform, because, in terms of complexity, WPM outperforms the most used now-a-days Orthogonal Frequency Division Multiplexing (OFDM).

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.271

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 head0.091
GPT teacher head0.285
Teacher spread0.194 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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