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Record W2124058479 · doi:10.1109/vetecf.2008.343

A Novel Distributed Space-Time Block Coding Protocol for Cooperative Wireless Relay Networks

2008· article· en· W2124058479 on OpenAlexaff
Hamed Rasouli, Alagan Anpalagan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRelayComputer scienceBlock codeSpace–time codeCoding (social sciences)Cooperative diversityWirelessSpace–time block codeSpectral efficiencyDiversity gainBlock (permutation group theory)Antenna diversityComputer networkSpace timeChannel (broadcasting)AlgorithmFadingTopology (electrical circuits)Decoding methodsTelecommunicationsMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Wireless relay has recently gained a lot of interest in the research community. It was suggested to use space-time block codes in the cooperative relaying system to increase the spectral efficiency of the relaying protocol. Using space-time codes in the relaying scheme also provides the diversity benefits of multiple antenna techniques. In this paper, a new method of distributed space-time coding based on Alamouti codes with one regenerative relay called "on-channel distributed space-time block coding" is introduced and analyzed. By a slight modification in the proposed scheme, "recursive on-channel relaying" technique is proposed which is shown to have better performance. After showing theoretically that the proposed methods achieve spatial diversity gain, performance of them is then evaluated via Monte-Carlo simulation. It is shown that the proposed space-time coded relaying protocols perform better in moderate to high SNR, compared to repetition-based relaying protocols, and requires 3 dB less SNR to achieve a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .

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 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 categoriesnone
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 score0.880
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.057
GPT teacher head0.302
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2008
Admission routes1
Has abstractyes

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