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Record W2007946941 · doi:10.1109/icassp.2013.6638607

Distributed concatenated Alamouti code designs for one-way relay networks using uniquely-factorable PSK constellation

2013· article· en· W2007946941 on OpenAlexaff
Zheng Dong, Jian‐Kang Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSpace–time block codeRelayComputer scienceBlock codeCode (set theory)Phase-shift keyingTopology (electrical circuits)AlgorithmConstellationChannel (broadcasting)Theoretical computer scienceTelecommunicationsMathematicsDecoding methodsBit error rateCombinatoricsPhysicsPower (physics)

Abstract

fetched live from OpenAlex

This paper develops a novel uniquely-factorable constellation pair (UFCP) by carefully factorizing phase-shift keying (PSK) constellation. With this unique factorization, a new distributed concatenated Alamouti code is proposed for a one-way relaying network consisting of two single-antenna terminals and one relay having two antennas. This design allows the relay to transmit its own information while forwarding the source information which it has received to the destination. By making use of the Alamouti coding scheme twice and jointly processing the signals from the two antennas at the relay node, such a code design also renders the equivalent channel between source and destination be a product of the two Alamouti channels and therefore, is called distributed concatenated Alamouti space-time block code (STBC). In addition, the asymptotic symbol error probability (SEP) formula is derived with the maximum likelihood (ML) detector, showing that the optimal diversity gain function is achieved and proportional to ln SNR/SNR2.

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.132
GPT teacher head0.305
Teacher spread0.172 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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