MétaCan
Menu
Back to cohort
Record W2262590824 · doi:10.1109/wcnc.2015.7127542

An adaptive matching pursuit algorithm for sparse channel estimation

2015· article· en· W2262590824 on OpenAlex

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
TopicSparse and Compressive Sensing Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMatching pursuitCompressed sensingA priori and a posterioriAlgorithmComputer scienceOrthogonal frequency-division multiplexingChannel (broadcasting)Mean squared errorComputational complexity theoryMatching (statistics)EstimationMathematicsStatisticsTelecommunications

Abstract

fetched live from OpenAlex

This paper examines the problem of compressed sensing-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In particular, we present an improved estimation algorithm based on the sparsity adaptive matching pursuit (SAMP), which is referred to as the adaptive step size SAMP (AS-SAMP), and compare it with the existing algorithms. Without requiring a priori knowledge of the sparsity, the proposed algorithm adjusts the step size adaptively to approach the true sparsity, thus improving the estimation accuracy. Simulation results show that the proposed algorithm provides a better trade-off between the mean squared error (MSE) performance and complexity when compared with conventional methods.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.410

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 head0.048
GPT teacher head0.275
Teacher spread0.226 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicSparse and Compressive Sensing TechniquesFrench-language works237,207