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Cloud Radio Access Networks

2013· book-chapter· en· W2481155970 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

VenueAdvances in systems analysis, software engineering, and high performance computing book series · 2013
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsTelus (Canada)
Fundersnot available
KeywordsCloud computingRadio access networkSoftware-defined radioComputer scienceCellular networkFlexibility (engineering)VirtualizationComputer networkBase stationRemote radio headSoftwareDistributed computingTelecommunicationsCognitive radioOperating system

Abstract

fetched live from OpenAlex

Radio virtualization and cloud signal processing are new approaches to building cellular Radio Access Networks (RAN) that are starting to be deployed within the cellular industry. For cellular operators, Cloud RAN architectures that centrally define or decode transmissions, placing most of the base-station software stack within a data-centre, promise improvements in flexibility and performance. The expected benefits range from standard cloud economies—statistical reductions in total processing, energy efficiency, cost reductions, simplified maintenance—to dramatic changes in the functionality of the radio network, such as simplified network sharing, capacity increases towards theoretical limits, and software defined radio inspired air interface flexibility. Because cellular networks have, in addition to complex protocols, extremely sensitive timing constraints and often high data-rates, the design challenges are formidable. This chapter presents the state of the art, hybrid alternatives, and directions for making Cloud Radio Access Networks more widely deployable.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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