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Record W1566962014 · doi:10.1109/vetec.1996.501481

Differentially coherent fading-resistant transmission from several antennas

2002· article· en· W1566962014 on OpenAlexaff
V.M. DaSilva, E.S. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFadingRayleigh fadingFading distributionChannel state informationTransmitterComputer scienceElectronic engineeringTransmission (telecommunications)Maximal-ratio combiningTelecommunicationsDiversity schemeWirelessChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

This paper proposes a bandwidth-efficient fading resistant transmission scheme which implements transmitter diversity using L antennas at the base station. When the antennas are spaced sufficiently far apart, the transmission from each antenna undergoes independent fading. These transmissions are coordinated to mitigate the effects of Rayleigh fading and the mobile receiver can recover the entire L-dimensional transmitted vector as long as the signal energy of at least one coordinate is large enough. L-dimensional fading-resistant signal constellations are generated by maximizing a figure of merit for the Rayleigh fading channel, assuming differential encoding and detection. This scheme offers a significant performance improvement over a conventional single-antenna differential BPSK scheme when coding is ineffective due to slow fading.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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