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Record W1993685308 · doi:10.1109/ccece.2010.5575189

Optimal amplify-and-forward scheme based on superposition modulation over relay channels

2010· article· en· W1993685308 on OpenAlexaff
Leonardo Jiménez Rodríguez, Nghi H. Tran, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcGill University
Fundersnot available
KeywordsSuperposition principleComputer scienceTransmission (telecommunications)Topology (electrical circuits)RelayModulation (music)Quadrature amplitude modulationEuclidean distanceChannel (broadcasting)Coding (social sciences)Phase-shift keyingAlgorithmBit error rateMathematicsTelecommunicationsPhysicsMathematical analysisArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents an optimal transmission scheme based on superposition modulation for a bandwidth efficient bit interleaved coded modulation (BICM) system over a nonorthogonal amplify-and-forward (NAF) half-duplex single-relay channel. Based on the asymptotic analysis, it is first demonstrated that in the first time slot, the source only needs to send the superposition of the first symbol and the rotated version of the second symbol, while being silent in the remaining slot to achieve the best asymptotic performance. It is then shown that a rotation angle that maximizes the minimum Euclidean distance of the superposition constellation should be used for good convergence behavior. An optimal rotation angle is then analytically determined for various modulation schemes. Both analytical and simulation results show that the proposed transmission scheme not only exploits full cooperative diversity but also offers a significant coding gain over previously proposed systems for NAF channels with good convergence property.

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: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.271
Teacher spread0.249 · 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
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

Citations4
Published2010
Admission routes1
Has abstractyes

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