MétaCan
Menu
Back to cohort
Record W2103781515 · doi:10.1109/tcomm.2010.03.070333

UWB receiver designs based on a gaussian-laplacian noise-plus-MAI model

2010· article· en· W2103781515 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

VenueIEEE Transactions on Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdditive white Gaussian noiseRake receiverElectronic engineeringFadingMultipath propagationInterference (communication)Bandwidth (computing)RakeComputer scienceGaussian noiseMultipath interferenceMatched filterGaussianAlgorithmTelecommunicationsWhite noiseEngineeringPhysicsChannel (broadcasting)Decoding methods

Abstract

fetched live from OpenAlex

A more appropriate statistical model for the multiple access interference than the generally used Gaussian approximation is proposed to reflect the heavy-tailed nature of the multiple access interference in ultra-wide bandwidth systems. Novel receiver structures which surpass the performance of the conventional matched filter receiver are studied for ultrawide bandwidth multiple access communications in both AWGN and fading multipath channels. A proposed Rake receiver is advantageous over the conventional Rake receiver when multiuser interference dominates ambient Gaussian noise and originates from a small to moderate number of interferers. Explanation is provided for the inaccuracy of a Gaussian approximation with regard to the multiple access interference, and the heavy-tailed nature of the probability density function of the multiple access interference is also discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.031
GPT teacher head0.256
Teacher spread0.225 · 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