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Record W2034084319 · doi:10.1109/tvt.2014.2344036

Quadrature Spatial Modulation

2014· article· en· W2034084319 on OpenAlexaff
Raed Mesleh, Salama Ikki, Hadi M. Aggoune

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsLakehead University
Fundersnot available
KeywordsPairwise error probabilityQuadrature (astronomy)Rayleigh fadingQuadrature amplitude modulationMonte Carlo methodMathematicsMIMOAlgorithmSynchronizingSpatial modulationControl theory (sociology)Computer scienceElectronic engineeringTopology (electrical circuits)Bit error rateFadingEngineeringStatisticsBeamforming

Abstract

fetched live from OpenAlex

This paper proposes a new multiple-input-multiple-output (MIMO) technique called quadrature spatial modulation (QSM). QSM enhances the overall throughput of conventional SM systems by using an extra modulation spatial dimension. The current SM technique uses only the real part of the SM constellation, and the proposed method in this paper extends this to in-phase and quadrature dimensions. It is shown that significant performance enhancements can be achieved at the expense of synchronizing the transmit antennas. Additionally, a closed-form expression for the pairwise error probability (PEP) of generic QSM system is derived and used to calculate a tight upper bound of the average bit error probability (ABEP) over Rayleigh fading channels. Moreover, a simple and general asymptotic expression is derived and analyzed. Obtained Monte Carlo simulation results corroborate the accuracy of the conducted analysis and show the significant enhancements of the proposed QSM 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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 designTheoretical or conceptual
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

Citations542
Published2014
Admission routes1
Has abstractyes

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