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Record W2143635038 · doi:10.1109/vtcf.2006.57

Spatial-Smoothing-Based Direction-of-Arrival, Propagation Delay and Channel Estimation for Antenna-Array DS/CDMA Systems

2006· article· en· W2143635038 on OpenAlexaff
Wu Ren, Ioannis Psaromiligkos

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultipath propagationDirection of arrivalComputer scienceEstimatorDelay spreadSmoothingAngle of arrivalAntenna arrayAlgorithmChannel (broadcasting)FadingCode division multiple accessAntenna (radio)Electronic engineeringNarrowbandSmart antennaSignal subspaceTelecommunicationsNoise (video)Directional antennaEngineeringMathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

We consider the problem of joint estimation of direction-of-arrival (DoA), propagation delay, and complex channel gain for antenna-array-based DS/CDMA communication systems over frequency selective multipath channels. We propose a MUSIC-type estimation algorithm which utilizes the spatial smoothing preprocessing technique. The proposed algorithm essentially breaks the multipath-induced coherency within the received signals and recovers the full signal subspace spanned by all dominant signal paths of all users. This allows the use of MUSIC-type DoA and delay estimators for the individual paths of the user of interest. Based on the angle and timing information, we then estimate the multipath fading coefficients. Simulation results illustrate the effectiveness of this approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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
Published2006
Admission routes1
Has abstractyes

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