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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 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
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.

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: Methods · Consensus signal: none
Teacher disagreement score0.857
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
Bibliometrics0.0010.001
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 head0.013
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