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Record W2131329456 · doi:10.1109/vetecf.2004.1404735

Sequential blind beamforming algorithm using combined CMA/LMS for wireless underground communications

2005· article· en· W2131329456 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
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMultipath propagationBeamformingAdaptive beamformerComputer scienceBlind signal separationAlgorithmInterference (communication)Blind equalizationWirelessChannel (broadcasting)Smart antennaAntenna (radio)Rayleigh fadingElectronic engineeringTelecommunicationsEqualization (audio)FadingEngineeringOmnidirectional antenna

Abstract

fetched live from OpenAlex

In this contribution, we propose a new smart antenna array (SAA) structure using sequential blind beamforming for multipath correlated signals in an underground mining environment. This adaptive receiver is dedicated to underground environments, where the multipath problem is more severe than the co-channel interference. In this confined area, the received path arrivals are not only highly correlated but actually belong to the same signal source. Consequently, we exploit the idea that these arrival paths are delayed replicas from an identical source. Simulation results confirm the effectiveness of the proposed blind beamformer. It outperforms the CMA beamformer by approximately 2.5 dB and 4.1 dB for Gaussian and Rayleigh channels, respectively.

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)0.001
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: Methods
Teacher disagreement score0.960
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.083
GPT teacher head0.353
Teacher spread0.270 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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