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Record W2283892817 · doi:10.1109/wcnc.2015.7127526

On the impact of imperfect channel knowledge on the performance of quadrature spatial modulation

2015· article· en· W2283892817 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsLakehead University
FundersUniversity of Tabuk
KeywordsQuadrature (astronomy)Spatial modulationSpectral efficiencyRayleigh fadingImperfectQuadrature amplitude modulationMIMOMonte Carlo methodMathematicsAlgorithmUpper and lower boundsComputer scienceChannel (broadcasting)TelecommunicationsStatisticsFadingElectronic engineeringBit error rateMathematical analysisEngineeringDecoding methods

Abstract

fetched live from OpenAlex

Quadrature spatial modulation (QSM) is a new multiple-input multiple-output (MIMO) transmission technique that enhances the overall spectral efficiency of conventional spatial modulation (SM). QSM extends the single dimension spatial constellation to another dimension by considering the inphase and the quadrature components of the data symbol. It has been shown that spectral efficiency can be significantly increased while most inherent advantages of SM are retained. In this paper, the impact of Gaussian imperfect channel estimation on the performance of QSM system is studied. A closed-form expression for the pair-wise 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. Also, simple asymptotic expression is derived and analyzed. Obtained Monte Carlo simulation results highlight the accuracy of the conducted analysis.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.195

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 head0.027
GPT teacher head0.267
Teacher spread0.240 · 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