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Record W3015106737 · doi:10.1109/lnet.2020.2984008

Performance Analysis for H-CRANs Under Constrained Capacity Fronthaul

2020· article· en· W3015106737 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Networking Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCloud computingComputer scienceMonte Carlo methodHigh data rateMillimeterExtremely high frequencyElectronic engineeringStochastic geometrySpace (punctuation)TelecommunicationsEngineeringWirelessPhysicsOpticsMathematicsStatistics

Abstract

fetched live from OpenAlex

In this letter, we investigate the impact of the capacity-limited hybrid millimeter waves/free space optical fronthaul on the performance of the heterogeneous cloud radio access network. By adopting stochastic geometry, analytical expressions for the average user data rate and Rate Coverage are derived, which are used to shed light on the impact of fronthaul capacity. Finally, Monte Carlo simulations are introduced to assess the accuracy of the analytical findings. In addition, the simulation results show that different biasing factors should be adopted for the different remote radio heads with different fronthaul capacities for a better Rate Coverage.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.746

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 science0.0000.000
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 head0.048
GPT teacher head0.225
Teacher spread0.178 · 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