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Record W2122761179 · doi:10.1109/icupc.1997.627217

Space/time multipath separation and equalization in asynchronous CDMA using the spatio-temporal array-receiver

2002· article· en· W2122761179 on OpenAlexaff
Sofiène Affes, P. Mermelstein

Bibliographic record

VenueProceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications · 2002
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMultipath propagationComputer scienceAsynchronous communicationEqualization (audio)Multipath interferenceTransmitterInterference (communication)Channel (broadcasting)Code division multiple accessDelay spreadElectronic engineeringDoppler effectSingle antenna interference cancellationArray processingAlgorithmSignal processingTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

We propose a spatio-temporal array-receiver for asynchronous CDMA using a new space/time structural approach. First, STAR performs blind identification and equalization of the propagation channel from each mobile transmitter. Second, it provides fast and accurate estimates of the number, relative magnitude and delay of the multipath components. From this space/time separation, we reconstruct the identified channel with respect to a partially revealed space/time structure and reduce identification errors by an order of the processing gain over the number of paths. Simulations confirm good multipath acquisition properties of STAR in the presence of strong interference and fast Doppler.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.001

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 head0.111
GPT teacher head0.336
Teacher spread0.225 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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