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Record W2254705619 · doi:10.1017/cbo9781139084284

Green Radio Communication Networks

2012· book· en· W2254705619 on OpenAlexaff
Ekram Hossain, Yan Chen, Vinod Sharma, Neelesh B. Mehta, Panayiotis Kolios, Jamshid Abouei, Osama Amin, Nof Abuzainab, Oliver Holland, Tao Chen, Xiao Lu, Mohammad Reza Nakhai, Ashok Karmokar, Yifei Wei, Long Hoang, Umesh Phuyal, Gunther Auer, Alberto Conte

Bibliographic record

VenueCambridge University Press eBooks · 2012
Typebook
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStandardizationSoftware deploymentWirelessWireless networkTelecommunicationsBase stationEfficient energy useEngineeringKey (lock)Computer scienceRadio resource managementComputer networkElectrical engineeringComputer securitySoftware engineering

Abstract

fetched live from OpenAlex

The importance of reducing energy costs, reducing CO2 emissions and protecting the environment are leading to an increased focus on green, energy-efficient approaches to the design of next-generation wireless networks. Presenting state-of-the-art research on green radio communications and networking technology by leaders in the field, this book is invaluable for researchers and professionals working in wireless communication. Summarizing existing and ongoing research, the book explores communication architectures and models, physical communications techniques, base station power-management techniques, wireless access techniques for green radio networks, and green radio test-bed, experimental results and standardization activities. Throughout, theoretical results are blended with practical insights and coverage of deployment issues. It serves as a one-stop reference for key concepts and design techniques for energy-efficient communications and networking and provides information essential for the design of future-generation cellular wireless systems.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0340.027

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 head0.012
GPT teacher head0.178
Teacher spread0.165 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations86
Published2012
Admission routes1
Has abstractyes

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