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Record W4367663484 · doi:10.1109/tbc.2023.3268948

Development of an Ultra-Long-Range Wireless Backhaul Solution Using ATSC 3.0

2023· article· en· W4367663484 on OpenAlexaff
Georges C. Livanos, Vladimir Anishchenko

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2023
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsBackhaul (telecommunications)TelecommunicationsComputer scienceWirelessComputer networkTerrainMicrowaveWireless broadbandElectronic engineeringWireless networkEngineeringGeography

Abstract

fetched live from OpenAlex

In rural and remote locations Internet access remains a challenge. Fiber optics cannot always be implemented due to cost and geographical terrain. Microwave and satellite links are expensive. An ultra-long-range wireless backhaul (ULRWB) solution using the ATSC 3.0 NextGen TV system is proposed, which can provide a backbone infrastructure for delivering inexpensive high-speed communications to rural and remote communities. The proposed ULRWB is based on the ATSC 3.0 Physical Layer (ATSC A/322) standard with all the advantages this modern advanced standard brings, including making the most efficient use of RF spectrum.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.058
GPT teacher head0.273
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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