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Record W2041772334 · doi:10.1002/ett.1245

Channel estimation for chip‐level Alamouti coded multi‐rate CDMA: blind subspace algorithms and performance analysis

2007· article· en· W2041772334 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

VenueEuropean Transactions on Telecommunications · 2007
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsSpace–time block codeAlgorithmSubspace topologyChannel (broadcasting)Code division multiple accessBlock codeComputer scienceMathematicsStatisticsTelecommunicationsDecoding methodsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this work we consider the problem of blind channel estimation for the downlink of a variable spreading gain (VSG) multi‐rate code‐division‐multiple‐access (MR CDMA) system which uses the orthogonal space‐time block coding (STBC) proposed by Alamouti. In contrast to traditional symbol‐level STBC CDMA systems, we consider a chip‐level ST block coding system for which we derive a simple subspace‐based blind channel estimation method. For the derived algorithm we investigate the necessary and sufficient conditions for the channel to be uniquely identifiable up to a complex multiplicative constant. We also analyse the behaviour of the channel estimation algorithm as a function of the data samples utilised, establishing its unbiasedness and providing a closed‐form expression for the mean‐square‐error (MSE) as well as the Cramer—Rao bound (CRB) for the channel estimation performance. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.323
Teacher spread0.243 · 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