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Record W2164347731 · doi:10.1109/twc.2010.04.090059

Signal space cooperative communication

2010· article· en· W2164347731 on OpenAlexaff
Seyed Ali Ahmadzadeh, Seyed Abolfazl Motahari, Amir K. Khandani

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2010
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRelayConstellationComputer scienceConstellation diagramSIGNAL (programming language)Cooperative diversityAntenna diversityTelecommunicationsModulation (music)Relay channelTopology (electrical circuits)Electronic engineeringDecoding methodsWirelessBit error rateFadingElectrical engineeringEngineeringAcoustics

Abstract

fetched live from OpenAlex

In this paper, a single-hop single-relay system with a direct link between the source and the destination is considered when the relay operates in the half-duplex mode. Motivated by the concept of signal space diversity, this paper introduces signal space cooperation, in which cooperation between the source and the relay is achieved using a novel constellation design. In this approach, the original constellation is expanded so that the expanded constellation consists of all possible combinations of different components of signal points in the original constellation. The expanded constellation enables the relay to extract the required information in order to effectively cooperate in the relay phase, and it helps the destination to efficiently combine received signals during the broadcast phase and the relay phase. The analytical study of the proposed scheme leads to the development of two design criteria for the constellation expansion. Numerical results depict superior performance in comparison with other cooperative schemes, such as the distributed turbo coded cooperative schemes and the trans-modulation scheme.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.288
Teacher spread0.253 · 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

Citations50
Published2010
Admission routes1
Has abstractyes

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