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Record W4399548420 · doi:10.1049/pbte109e_ch5

Digital twin empowered Open RAN of 6G networks

2024· book-chapter· en· W4399548420 on OpenAlex
Antonino Masaracchia, Vishal Sharma, Muhammad Fahim, Octavia A. Dobre, Trung Q. Duong

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

VenueInstitution of Engineering and Technology eBooks · 2024
Typebook-chapter
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRanC-RANComputer scienceTelecommunicationsComputer networkRadio access network

Abstract

fetched live from OpenAlex

The open radio access network (O-RAN) Alliance's main mission is to lead to the evolution of the next-generation network RAN by incorporating principles of openness and intelligence. Simultaneously, digital twin (DT) technology is emerging as a cornerstone for developing services in the context of sixth-generation (6G) networks. This chapter provides a comprehensive perspective on how DT and O-RAN constitute two synergistic concepts. In particular, it illustrates how their mutual integration holds the potential to facilitate the deployment of a smart and resilient 6G RAN. Notably, DT concept will play a pivotal role in enhancing the core principles of intelligence, autonomy, and openness that underlie O-RAN. The chapter begins with a concise overview of both O-RAN and DT concepts. It then proceeds to illustrate and discuss potential use cases and services achievable through a DT-based O-RAN architecture. The chapter concludes by outlining current challenges and discussing future research direction toward the implementation of such innovative network architecture.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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