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Record W2162128351 · doi:10.1109/tsp.2006.890903

A Novel Signaling Scheme for Blind Unique Identification of Alamouti Space-Time Block-Coded Channel

2007· article· en· W2162128351 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 Transactions on Signal Processing · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAlgorithmSpace–time block codeBlock codeBlock (permutation group theory)Coprime integersChannel (broadcasting)MathematicsNoise (video)Relaxation (psychology)Gaussian noiseComputer scienceDecoding methodsTelecommunicationsArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

In this paper, we present a simple signaling scheme to blindly and uniquely identify the Alamouti space-time block-coded channel, first under a noise-free environment, and then, under a complex Gaussian noise environment in which pth- and qth-order statistics (p and q are coprime) of the received signals are available. In both cases, closed-form solutions to determine the channel coefficients are obtained by exploiting specific properties of the Alamouti space-time block code (STBC) and the linear Diophantine equation theory. Under Gaussian noise, when the length of the received data is finite, we propose to use the semidefinite relaxation (SDR) algorithm to approximate maximum-likelihood (ML) detection so that the joint estimation of the channel and symbols can be efficiently implemented. Simulation results show that while other existing blind methods fail, our signaling scheme works well

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)0.001
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.280
Teacher spread0.255 · 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