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Record W2294088060 · doi:10.1109/tbc.2015.2505411

LDM Core Services Performance in ATSC 3.0

2016· article· en· W2294088060 on OpenAlexaff
Cristina Regueiro, Jon Montalbán, Jon Barrueco, Manuel Vélez, Pablo Angueira, Yiyan Wu, Liang Zhang, Sung-Ik Park, Jae-Young Lee, Heung Mook Kim

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2016
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
FundersEuropean Regional Development FundEuskal Herriko UnibertsitateaEusko Jaurlaritza
KeywordsDigital televisionComputer scienceBroadcasting (networking)Digital Video BroadcastingDigital broadcastingMultiplexingDigital audio broadcastingChannel (broadcasting)Enhanced Data Rates for GSM EvolutionElectronic engineeringTelecommunicationsComputer networkEngineering

Abstract

fetched live from OpenAlex

Advanced Television Systems Committee (ATSC) 3.0, the new generation digital terrestrial television standard, has been designed for facing the new challenges of the future broadcasting systems. ATSC 3.0 has been built using the most recent cutting-edge technologies. Layered division multiplexing (LDM) is one of the major components of the new system baseline. LDM provides a tool to make flexible use of the spectrum for delivering simultaneous services to stationary and mobile services. This paper presents the performance evaluation of ATSC 3.0 core services in mobile scenarios using LDM. Simulation results are presented to analyze the influence of different LDM ensemble configuration modes for mobile reception. The simulation results have been also confirmed by laboratory tests under different channel models. The signal to noise ratio threshold values confirm the excellent behavior of ATSC 3.0 and LDM in mobile and portable scenarios.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.222
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 designBench or experimental
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

Citations54
Published2016
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on BroadcastingSame topicTelecommunications and Broadcasting TechnologiesFrench-language works237,207