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Record W4290998100 · doi:10.1109/mcomstd.0001.2100092

Can 5G Fixed Broadband Bridge the Rural Digital Divide?

2022· article· en· W4290998100 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

VenueIEEE Communications Standards Magazine · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDigital divideTelecommunicationsBridge (graph theory)Context (archaeology)BroadbandRural areaMobile broadbandComputer scienceThe InternetInternet accessBroadband networksBusinessWirelessGeographyWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

The digital divide between rural and urban communities is a significant problem in today's connected world. Until recently, infrastructure costs have limited how effectively fixed broad-band (FB) Internet services could be offered to rural regions. However, with 4G, a convergence between FB and mobile services has started to emerge via fixed wireless access (FWA), which has made it possible for operators to provide (limited) FB to rural communities using existing cellular infrastructure. To bridge the digital divide, rural FWA must be able to provide an end-to-end experience comparable to urban FB. In this regard, 4G is inadequate, but 5G can make a difference. In this article we examine how 5G FWA could truly enable FB in rural regions. We present improvements to each area of the 5G architecture, including new and upcoming advances in 3GPP Releases 16 and 17, and examine how they can benefit rural FWA users. Despite these advances, 5G operators will face a number of challenges in planning and operating rural FWA networks. Hence, the second objective of this article is to outline future research directions in this context.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.668

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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