MétaCan
Menu
Back to cohort
Record W2409338078 · doi:10.1017/cbo9781107297333

Design and Deployment of Small Cell Networks

2015· book· en· W2409338078 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

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSoftware deploymentComputer scienceResource (disambiguation)Context (archaeology)Wireless networkNetwork planning and designKey (lock)Resource management (computing)WirelessSystems engineeringEngineeringTelecommunicationsDistributed computingComputer networkSoftware engineeringComputer security

Abstract

fetched live from OpenAlex

This comprehensive resource covers everything you need to know about small cell networks, from design, to analysis, optimization and deployment. Detailing fundamental concepts as well as more advanced topics, and describing emerging trends, challenges and recent research results, this book explains how you can improve performance, decision making, resource management, and energy efficiency in next generation wireless networks. Key topics covered include green small cell networks and associated trade-offs, optimized design and performance analysis, backhauling and traffic overloading, context-aware self-organizing networks, deployment strategies and mobility management in large scale HetNets. Written by leading experts in academia and industry and including tools and techniques for small cell network design and deployment, this is an ideal resource for graduate students, researchers and industry practitioners working in communications and networking.

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: Other · Consensus signal: none
Teacher disagreement score0.515
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.172
Teacher spread0.154 · 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