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Record W2901332218 · doi:10.1049/el.2018.6406

Forward–backward processing for efficient underground channel estimation in 60 GHz MISO FBMC systems

2018· article· en· W2901332218 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

VenueElectronics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsChannel (broadcasting)EstimationElectronic engineeringComputer scienceTelecommunicationsEngineeringSystems engineering

Abstract

fetched live from OpenAlex

In this Letter, a novel low complexity sparse channel estimation method based on forward–backward processing is proposed for mm‐Wave underground channel incorporating a space‐time‐block coding filter bank multi‐carrier (STBC‐FBMC) system, which employs quadrature amplitude modulation. The proposed estimation algorithm relies on the idea of the channel support forward selection and backward removal, which iteratively expands and compresses the support estimate, hence improving the ability to cover up for the errors made in the conventional orthogonal matching pursuit (OMP) algorithm. By contrast to the latter, the most innovative feature of the proposed approach is its capability of effective channel reconstruction without prior information of the sparsity level and its adaptive step size method to approach the true sparsity level. The proposed approach is shown to achieve a higher estimation accuracy than OMP alternative.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score0.863

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 head0.009
GPT teacher head0.236
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