MétaCan
Menu
Back to cohort
Record W4213097848 · doi:10.1002/047134608x.w8429

Fiber‐Wireless (FiWi)‐Enhanced Mobile Networks in the 6G ERA

2022· other· en· W4213097848 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

VenueWiley Encyclopedia of Electrical and Electronics Engineering · 2022
Typeother
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueConcordia University
Fundersnot available
KeywordsComputer networkComputer sciencePassive optical networkThe InternetEnablingWireless10G-PONMobile edge computingWireless networkEthernetAccess networkTelecommunicationsServerWavelength-division multiplexingWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Although the original premise of 5G networks was to enable the Internet of Everything (IoE) services and applications, the current deployments of such networks prove otherwise. Shortcomings of 5G have recently attracted a great deal of attention from both the research community and the industry to define next‐generation 6G systems, as an enabler of a variety of disruptive applications ranging from extended reality (XR) to haptics. In this article, we review the 6G vision, paying particular attention to its underlying human‐centric premise. We then elaborate on the recently emerging concepts of Tactile Internet and Internet of No Things, which are envisioned to be enabled over FiWi‐enhanced low‐latency LTE‐Advanced (LTE‐A) heterogeneous networks (HetNets) using a TDM/WDM 1/10 Gb/s Ethernet passive optical network (PON) backhaul and a Wi‐Fi offloading front end with artificial intelligence (AI)‐enhanced multiaccess edge computing (MEC) servers placed at the optical‐wireless interfaces.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.757
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 communication0.0000.000
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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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