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Record W2168074781 · doi:10.1109/icupc.1996.562703

The information theoretic reliability function for multipath fading channels with diversity

2002· article· en· W2168074781 on OpenAlexaff
W.K.M. Ahmed, P.J. McLane

Bibliographic record

VenueProceedings of ICUPC - 5th International Conference on Universal Personal Communications · 2002
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsFadingMultipath propagationRayleigh fadingComputer scienceFading distributionDiversity schemeChannel state informationChannel (broadcasting)Additive white Gaussian noiseElectronic engineeringTelecommunicationsWirelessMathematicsEngineering

Abstract

fetched live from OpenAlex

With the emergence of wireless and mobile communication systems and technology new channel models and impairment phenomena have appeared. We derive the information theoretic reliability function for a number of ideal fading channels with diversity. It has been shown by Buz (see Ph.D. Dissertation, Dept. of Elec. and Comp. Eng., Queen's University, Kingston) that the asymptotic SNR loss in the capacity, relative to the AWGN channel, for ideal Rayleigh fading is no more than 2.51 dB, which is not very dramatic. By looking at the reliability function, we have found, on the other hand, that the loss in the cutoff rate due to Rayleigh fading is monotonically increasing with SNR and considerably longer codes are required for transmission over the Rayleigh channel at rates below capacity. Nevertheless, this loss can be regained by means of diversity, where it has been found that most of the loss can be reclaimed with the use of 2 or 3 antennas.

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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0030.001
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 head0.065
GPT teacher head0.279
Teacher spread0.214 · 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 teacher head, 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

Citations2
Published2002
Admission routes1
Has abstractyes

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