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Record W2060012946 · doi:10.1109/iscas.2013.6572308

An approach for joint blind space-time equalization and DOA estimation

2013· article· en· W2060012946 on OpenAlexaff
Iman Moazzen, P. Agathoklis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceJoint (building)Interference (communication)FadingAlgorithmBlind equalizationEqualization (audio)Control theory (sociology)Sequence (biology)SIGNAL (programming language)Constant (computer programming)Property (philosophy)Channel (broadcasting)TelecommunicationsArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

A multi-stage structure to jointly combat multi-user interference and fading channels is presented. Neither the DOA nor a training sequence is assumed to be available for the receiver. The only assumption is that the transmitted signal satisfies the constant modulus property which is valid for many modulation schemes, and can be exploited by the multimodulus algorithm. Taking advantage of virtual subarrays, the DOA at each stage is estimated and used to compute the next stage input. Each stage operates in two modes, the starting and the tracking mode. Thanks to the adaptive structure, it can deal with time-varying DOAs and channels. Simulation results illustrate the performance of this method.

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.

How this classification was reachedexpand

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: Methods
Teacher disagreement score0.962
Threshold uncertainty score0.404

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 communication0.0000.001
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 head0.034
GPT teacher head0.289
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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