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Exact error probability analysis of rectangular QAM for single- and multichannel reception in nakagami-m fading channels

2009· article· en· W2107428208 on OpenAlexaff
Amine Maaref, Sonia Aı̈ssa

Bibliographic record

VenueIEEE Transactions on Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsNakagami distributionFadingQuadrature amplitude modulationHypergeometric functionQAMMathematicsIndependent and identically distributed random variablesAlgorithmStatisticsTopology (electrical circuits)Random variableCombinatoricsBit error rateMathematical analysis

Abstract

fetched live from OpenAlex

In this contribution, we derive exact closed-form expressions for the average symbol error probability (SEP) of arbitrary rectangular quadrature amplitude modulation (QAM) for single- and multichannel diversity reception over independent but not-necessarily identically distributed Nakagami-m fading channels. The diversity branches may hence exhibit identical or distinctive power levels and their associated Nakagami indexes need not be the same. Our work extends previous results pertaining to nondiversity reception of M-ary rectangular QAM over Rayleigh fading channels and multichannel reception of M-ary square QAM over Nakagami-m fading channels. For a given number L of diversity branches and a corresponding set of arbitrary real-valued Nakagami indexes not less than 1/2, our SEP results are expressed in terms of Gauss's hypergeometric function2F1and Lauricella's multivariate hypergeometric function FD(L)of L variables, both of which can be efficiently evaluated using standard numerical softwares.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.055
GPT teacher head0.306
Teacher spread0.250 · 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

Citations48
Published2009
Admission routes1
Has abstractyes

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