MétaCan
Menu
Back to cohort
Record W3159173436 · doi:10.1109/tvt.2022.3160586

Probabilistic Analysis of Operating Modes in Cache-Enabled Full-Duplex D2D Networks

2022· article· en· W3159173436 on OpenAlexaff
Mansour Naslcheraghi, Constant Wetté, Jean‐François Frigon, Brunilde Sansò

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2022
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsEricsson (Canada)Polytechnique Montréal
Fundersnot available
KeywordsBackhaul (telecommunications)CacheComputer scienceComputer networkProbabilistic logicPopularityCellular networkKey (lock)Transmission (telecommunications)Core networkDuplex (building)Base stationTelecommunicationsComputer security

Abstract

fetched live from OpenAlex

Cache-enabled Device-to-Device (D2D) communications, recognized as one of the key enablers of the fifth generation (5G) cellular network, are a promising solution for reducing the great burden on mobile core networks and backhaul links. Caching, however, imposes a new networking user Key Performance Index (KPI), which is the probability of user satisfaction. In other words, how likely is it that the user will obtain the necessary information from the network? Such probability depends on the type of transmission, (i.e., half duplex or full duplex) and on many elements related to the caching system, including the way the information is cached or the popularity of the cached information. The analysis of those elements produces different modes of operation. To evaluate the new KPI, the probabilities of each mode of operation must be extracted from the transmission and caching conditions. This paper presents a thorough analysis of those probabilities, including the relevance of the relationship between caching policies, content popularity and transmission types. Such relationships allow the smooth evaluation of user satisfaction under different conditions.

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: none
Teacher disagreement score0.534
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Vehicular TechnologySame topicCaching and Content DeliveryFrench-language works237,207