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Record W2135365575 · doi:10.1109/vtcf.2006.107

Efficient Sequential Blind Beamforming for Wireless Underground Communications

2006· article· en· W2135365575 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 Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBeamformingComputer scienceMaximal-ratio combiningLeverage (statistics)AlgorithmWirelessAdaptive beamformerPath (computing)Electronic engineeringTelecommunicationsComputer networkEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a new technique enabling path diversity maximum ratio combining (MRC) is proposed to leverage the performance of the previously proposed Sequential Blind Beamforming (SBB) method. The latter mitigates the inter-symbol and intra-symbol interferences and recovers the signal and its integer and non-integer (fractional) multiple replicas using jointly CMA, LMS and adaptive fractional time delay estimation. While implementing EGC at the combining step can give an acceptable performance in the SBB, since the resolved paths have common phase ambiguity, more substantial improvement can be obtained by implementing coherent MRC with hard decision feedback identification. Simulations results in different scenarios validate the superiority of the SBB using the proposed MRC compared to the EGC path combiner.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.583
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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