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Record W2044878086 · doi:10.1109/tbc.2015.2400819

Providing Vehicular Infotainment Service Using VHF/UHF TV Bands via Spatial Spectrum Reuse

2015· article· en· W2044878086 on OpenAlexaff
Jiacheng Chen, Bo Liu, Haibo Zhou, Lin Gui, Ning Liu, Yiyan Wu

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsCommunications Research Centre Canada
FundersShanghai Key Laboratory of Digital Media Processing and TransmissionMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsComputer scienceUltra high frequencyReuseThroughputComputer networkFrame (networking)Service (business)Frequency allocationDigital televisionResource allocationSpectral efficiencyReal-time computingTelecommunicationsWirelessEngineering

Abstract

fetched live from OpenAlex

In this paper, we identify the potential of the television bands to be used for providing vehicular infotainment service. Specifically, content delivery from the roadside unit to vehicular users is conducted by exploiting the spectrum holes, which is also referred to as spatial spectrum reuse. In this manner, the spectrum utilization efficiency is improved and the digital television service is not interfered. We first give a thorough description on such vehicular content delivery network and indicate the major design issues. After that, we respectively develop user and resource models tailored for our scenario by considering their characteristics and formulate the resource allocation as a system throughput maximization problem based on these models. For practical implementation, we devise a vehicular content delivery protocol incorporating a dynamic spectrum access algorithm and a maximal frame size design method, both of which aim at throughput and spectrum efficiency enhancement. Extensive simulations are conducted and the numerical results show the system performance is improved with our approaches.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.235
Teacher spread0.207 · 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.

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

Citations17
Published2015
Admission routes1
Has abstractyes

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