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Record W2151980008 · doi:10.1109/icc.2007.187

Exact Average Bit-Error Probability for Maximal Ratio Combining with Multiple Cochannel Interferers and Rayleigh Fading

2007· article· en· W2151980008 on OpenAlexaff
Amir Ali Basri, Teng Joon Lim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRayleigh fadingMaximal-ratio combiningFadingFading distributionPhase-shift keyingAdditive white Gaussian noiseMathematicsAlgorithmStatisticsSignal-to-noise ratio (imaging)Bit error rateMonte Carlo methodKeyingComputer scienceTelecommunicationsWhite noiseDecoding methods

Abstract

fetched live from OpenAlex

An exact closed-form expression is derived for the average bit-error probability (BEP) of binary phase-shift keying signals with multiple-antenna reception. We consider maximal ratio combining technique in the presence of multiple cochannel interferers with identical or different powers and additive white Gaussian noise. It is assumed that both the desired signal and interference are subject to flat Rayleigh fading, and the fading channels of different users are independent of each other. In this paper, the derivation of the average BEP is different from the conventional PDF-based approach and is based on the decision variable at the output of the maximal ratio combiner conditioned only on the fading channel of the desired user. The analytical result is verified by Monte Carlo simulations.

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)0.006
metaresearch head score (Gemma)0.021
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0020.001
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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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