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Record W3031354173 · doi:10.1109/jlt.2020.2998001

Decentralized Coordination of Converged Tactile Internet and MEC Services in H-CRAN Fiber Wireless Networks

2020· article· en· W3031354173 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Lightwave Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsComputer scienceComputer networkCloud computingDistributed computingThe InternetWirelessBackhaul (telecommunications)Wireless networkAccess networkTelecommunications

Abstract

fetched live from OpenAlex

In order to meet the requirements of services and applications envisioned for post-5G and 6G networks, research efforts are heading towards the convergence of architectures aiming to support the wide variety of new compute-demanding and latency-sensitive applications in the context of Tactile Internet. In this article, we study the resource allocation and association of users with different delay requirements in a shared-backhaul fiber-wireless (FiWi) enhanced Heterogeneous Cloud Radio Access Network (h-cran) with Multi-access Edge Computing (mec) and offloading. As opposed to traditional resource and association management, we propose a decentralized algorithm based on a full dual decomposition of the optimization problem to operate the network. Results show that this approach outperforms the traditional one in terms of average delay and energy consumption, achieving up to 80% average delay improvement in high-load scenarios.

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.

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 score0.219
Threshold uncertainty score0.387

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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