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Record W2808115598 · doi:10.1115/jrc2018-6116

CBTC DCS Based on LTE Unlicensed Wireless Access: Assessment of Coexistence Performance With Wi-Fi

2018· article· en· W2808115598 on OpenAlexaff
Arash Aziminejad, Yan He

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsSpectrum managementComputer scienceSoftware deploymentLTE AdvancedStandardizationFemtocellComputer networkWireless broadbandContext (archaeology)WirelessTelecommunicationsThroughputBroadband networksCommunications systemBroadbandWireless networkBase stationCognitive radioTelecommunications link

Abstract

fetched live from OpenAlex

As the existing communication technologies which for about a decade have supported railway operations and the huge transition from conventional to modern communication-based signaling approach the extent of their performance capabilities, the railway industry strives to migrate to a proven solution aiming to support the new and diverse broadband services and reduce cost. Long Term Evolution (LTE) radio access technology has been globally accepted because of the unparalleled performance, off-the-shelf convenience, and well-developed standardization. An LTE solution, however, brings both the opportunities and challenges to a Data Communication System (DCS) underlying a Communication-Based Train Control (CBTC) system. The presented research targets one of the main LTE deployment challenges; the spectrum availability. To cope with the increasing scarcity of spectrum resources, LTE/LTE-A has envisaged an extension to the unlicensed band which is already heavily populated with incompatible legacy systems such as the immensely popular Wi-Fi networks. In this paper, a design framework is established to dimension the LTE system according to the CBTC DCS sub-system level requirements. Furthermore, the LTE/Wi-Fi coexistence performance is evaluated and studied in a train control application’s context by using a Markov chain analysis approach.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.252
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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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