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Record W2158053656 · doi:10.1109/aps.2004.1330145

Coherence signal impact on the performance of a spatio-temporal GSC receiver for CDMA applications

2004· article· en· W2158053656 on OpenAlexaff
Khalida Ghanem, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBeamformingRobustness (evolution)Adaptive beamformerComputer scienceCoherence (philosophical gambling strategy)Single antenna interference cancellationElectronic engineeringCoherence timeA priori and a posterioriWirelessCode division multiple accessSignal processingSIGNAL (programming language)Channel (broadcasting)TelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

The goal of adaptive beamforming is the reception of the desired signal in a specified direction and the cancellation of the interference signals without a priori knowledge of the interference environments. An efficient algorithm called generalized sidelobe canceller (GSC) was proposed (Rude, M.J. and Griffiths, L.J., Proc. IEEE ICASSP, p.968-71, 1989). However, one major problem is the desired signal cancellation that is caused by coherence between the desired signal and one or more interfering signals. We proposed a new spatio-temporal scheme based on GSC (Ghanem, K., Proc. IEEE Radio & Wireless Conf. p.119-22, 2003). The new receiver only prevents the desired signal cancellation but also provides robustness against channel variations. This paper presents the performance of the new efficient technique, combining spatial and temporal diversities, in coherent and non-coherent environments. Beamforming efficiency and convergence rates are investigated and presented. It is shown that the scheme performs well at higher interference levels, and, at the same time, the beamforming efficiency of the proposed technique presents a high robustness against signal coherence.

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: Empirical · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.038
GPT teacher head0.312
Teacher spread0.274 · 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
GenreEmpirical

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

Citations1
Published2004
Admission routes1
Has abstractyes

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