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Record W2962808528 · doi:10.1109/mwc.2018.1700419

An Edge Computing Empowered Radio Access Network with UAV-Mounted FSO Fronthaul and Backhaul: Key Challenges and Approaches

2018· article· en· W2962808528 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 Wireless Communications · 2018
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBackhaul (telecommunications)Computer scienceComputer networkEdge computingSoftware deploymentLeverage (statistics)ArchitecturePayload (computing)Edge deviceTelecommunicationsEnhanced Data Rates for GSM EvolutionCloud computingBase station

Abstract

fetched live from OpenAlex

One promising approach to address the supply- demand mismatch between the terrestrial infrastructure and temporary and/or unexpected traffic demands is to leverage the UAV technologies. Motivated by the recent advancement of UAV technologies and retromodulator-based free space optical communication, we propose a novel edge-computing-empowered radio access network architecture where the fronthaul and backhaul links are mounted on UAVs for rapid event response and flexible deployment. The implementation of hardware and networking technologies for the proposed architecture are investigated. Due to the limited payload and endurance as well as the high mobility of UAVs, research challenges related to communication resource management and recent research progress are reported.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.788

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 science0.0010.000
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 head0.055
GPT teacher head0.276
Teacher spread0.220 · 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