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Record W2307882650 · doi:10.1109/iccnc.2016.7440646

Design of uniquely factorable hexagonal constellations for noncoherent SIMO systems

2016· article· en· W2307882650 on OpenAlexaff
Eleanor Leung, Jian‐Kang Zhang

Bibliographic record

Venue2016 International Conference on Computing, Networking and Communications (ICNC) · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHexagonal crystal systemTransmitterQuadrature amplitude modulationConstellationQAMTopology (electrical circuits)Coding (social sciences)MathematicsTransmission (telecommunications)Lattice (music)Coding gainAlgorithmElectronic engineeringComputer scienceBit error rateAcousticsDecoding methodsTelecommunicationsPhysicsCombinatoricsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we propose the design of a uniquely factorable hexagonal constellation for a noncoherent wireless communication system with a single transmitter antenna and multiple receiver antennas (SIMO). The hexagonal constellation has a densely packed two dimensional "honeycomb" structure and is more energy-efficient than the commonly used cross quadrature amplitude modulation (QAM) constellations. By using the hexagonal lattice formed from the Eisenstein integers and the recently developed concept of the uniquely factorable constellation (UFC) for the QAM constellation, an algorithm is developed to effectively and efficiently construct unitary hexagonal UFCs of various sizes. In addition, an optimal energy scale is found to maximize the coding gain for the unitary training hexagonal UFC schemes subject to a transmission bit rate constraint. Computer simulations show that the hexagonal unitary UFC proposed in this paper has the best error performance in comparison to the current literature results for the noncoherent SIMO system.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.648

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 science0.0010.000
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 head0.089
GPT teacher head0.296
Teacher spread0.207 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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