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Record W2735804801 · doi:10.1109/icnidc.2016.7974617

Content caching scheme for D2D communication underlaying cellular networks with capacoty restriction

2016· article· en· W2735804801 on OpenAlexaff
Jinchao Lu, Heli Zhang, Hong Ji, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceCacheCellular networkComputer networkScheme (mathematics)The InternetOptimization problemContent deliveryPopularityMobile deviceDistributed computingMobile telephonyMultimediaMobile radioWorld Wide WebAlgorithm

Abstract

fetched live from OpenAlex

With the rapid growth of Internet services and the popularity of social media, mobile network operators are facing a serious challenge to delivery multimedia content to multiple users. In this paper, we consider device-to-device (D2D) communication supported mobile content delivery networks (mCDNs) which enables controllable and direct delivery of multimedia contents. With this network, we regard mobile device as caching server device (CSD) which can store popular multimedia contents and provide these for other devices in proximity to it via D2D link. Besides, we propose an optimization problem to determine the caching probability for the individual content in each CSD. In this problem, we intend to maximize system utility with the consideration of cache capacity restriction in each CSD. Further, we present a low-complexity search algorithm, namely discrete binary searching algorithm (DBSA), for solving the proposed optimization problem. Simulation results show that proposed optimal solution can obtain the best performance on system utility.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.275

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 communication0.0000.001
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 head0.047
GPT teacher head0.215
Teacher spread0.168 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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