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Record W2127238581 · doi:10.1109/vetec.1995.504900

A PHASE algorithm for blind adaptive optimum diversity combining

2002· article· en· W2127238581 on OpenAlexaff
Jian Cui, D.D. Falconer, A.U.H. Sheikh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsAlgorithmCyclostationary processComputer scienceDiversity combiningInterference (communication)WirelessSample matrix inversionComputationAdaptive filterRate of convergenceComputational complexity theoryAdaptive algorithmChannel (broadcasting)Covariance matrixTelecommunicationsFadingDecoding methods

Abstract

fetched live from OpenAlex

A new PHASE algorithm is proposed for blind adaptive extraction of signal in the presence of interference by cyclostationary signal processing using an antenna array. The algorithm operates in an interference-limited system in which the desired and interfering signals have equal symbol rates, but slightly different carrier frequencies. Compared to the SCORE algorithm and a modified version of SCORE, the new algorithm provides a simpler and faster converging means to estimate the channel phase for diversity combining. There is no need to solve eigenequations or to calculate the stochastic gradient. Analytical and simulation results are presented and performances are compared with DMI (direct matrix inversion), and SCORE algorithms in terms of their convergence rate, steady state SINR, and the computation complexity. This method is relatively simple and very promising in application to indoor wireless communication for its ability to reject heavy interference and increase the spectrum efficiency. Analysis and simulation results are presented to confirm this ability.

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 score0.987
Threshold uncertainty score0.357

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 science0.0010.000
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 head0.077
GPT teacher head0.303
Teacher spread0.226 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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