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Record W2940883407

Introducing ReCPRI: A Field Re-configurable Protocol for Backhaul Communication in a Radio Access Network

2019· article· en· W2940883407 on OpenAlexaff
Juan Camilo Vega, Qianfeng Shen, Alberto Leon‐Garcia, Paul Chow

Bibliographic record

VenueImmunotechnology · 2019
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkBackhaul (telecommunications)EthernetSoftware-defined radioATA over EthernetSynchronous EthernetEmbedded systemBase stationOperating systemEthernet flow controlTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

We present the Re-configurable Ethernet Common Public Radio Interface (ReCPRI) protocol as a replacement of the existing Ethernet Common Public Radio Interface (eCPRI) standard. Using the same communication infrastructure, this protocol is shown to provide the same functionality as eCPRI, maintaining full backward compatibility, all while reducing the required data bit rate and increasing the ADC sample rate by 4.4x. This is achieved using hardware-based data pre-processing at the radio tower. This protocol also makes the backhaul network SDN compatible. ReCPRI is capable of modifying the data bit rate dynamically as usage changes, modifying the hardware infrastructure in real time, migrating towers dynamically between Base Band Units, dynamically adding new functionality, and modifying the baseline infrastructure. This is all accomplished in real time, without interrupting service, and all from the Base Band Unit’s software layer.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.303
Teacher spread0.287 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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