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Record W2557427393 · doi:10.1109/lcomm.2016.2634014

Analytical Framework for Space Shift Keying MIMO Systems With Hardware Impairments and Co-Channel Interference

2016· article· en· W2557427393 on OpenAlexafffund
Ali Afana, Salama Ikki

Bibliographic record

VenueIEEE Communications Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMIMOTransmitterExpression (computer science)Interference (communication)Rayleigh fadingChannel (broadcasting)KeyingUpper and lower boundsTopology (electrical circuits)Computer scienceMathematicsTransmitter power outputFadingTelecommunicationsControl theory (sociology)CombinatoricsMathematical analysis

Abstract

fetched live from OpenAlex

This letter provides a general analytical framework for multiple-input multiple-output space shift keying systems considering hardware impairments, at the transmitter and receiver sides, and co-channel interference. Specifically, a closed-form expression for the average bit error probability (ABEP) in the case of two transmit antennas and arbitrary number of receive antennas is derived. As well, a tight upper bound ABEP expression in the general case of arbitrary number of transmit and receive antennas is obtained. Besides, an asymptotic simple expression is found over Rayleigh fading channels. Analytical results, which are validated via simulation ones, explicitly demonstrate that non-zero bounds of the ABEP exist in the high power region, which is in contrast to the case of ideal hardware, where the ABEP asymptotically goes to zero.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
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 head0.034
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

Citations17
Published2016
Admission routes2
Has abstractyes

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