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Record W2151114778 · doi:10.1109/mvt.2006.343629

TEDS: A high speed digital mobile communication air interface for professional users

2006· article· en· W2151114778 on OpenAlexfundno aff
Mehdi Nouri, Vincenzo Lottici, R. Reggiannini, Diana Ball, Mark Rayne

Bibliographic record

VenueIEEE Vehicular Technology Magazine · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsnot available
FundersInstituto de TelecomunicaçõesPartenariat Canadien Contre Le CancerUniversità di Pisa
KeywordsComputer scienceComputer networkPayload (computing)Physical layerQuality of serviceNetwork packetAir interfaceWiMAXData link layerHeaderWirelessTelecommunicationsBase station

Abstract

fetched live from OpenAlex

The TETRA enhanced data service (TEDS) standard has been developed within the Technical Committee TETRA of the European Telecommunications Standards Institute as a major upgrade to the existing narrow-band ETSI TETRA system to supply professional users with high-speed IP packet data services over wireless mobile channels. Multicarrier-based modulation format, powerful payload and header encoding and link adaptation methods are among techniques employed for this purpose. The above enhancements are efficiently combined with improved higher layer protocols enabling a number of new services such as multiple multimedia access with QoS re-negotiation during the session, various priority mechanisms, provision of scheduled access for delay-sensitive applications and support for sectored antenna usage. The aim of this paper is to give a comprehensive overview of the TEDS features with particular emphasis on the physical layer and medium access control features together with representative link and system performance results

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.017

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 head0.005
GPT teacher head0.243
Teacher spread0.238 · 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 designNot applicable
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

Citations33
Published2006
Admission routes1
Has abstractyes

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