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Design for sustainability - an imperative for future mobile networks

2023· article· en· W4321606827 on OpenAlexaff
Derrick Remedios, Lieven Levrau, Satish Kanugovi, Susanna Kallio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsNokia (Canada)
Fundersnot available
KeywordsSustainabilityEnvironmental economicsEnergy consumptionEfficient energy useResource scarcityScarcityResource efficiencyKey (lock)Computer scienceResource (disambiguation)Natural resource economicsEngineeringEconomicsComputer security

Abstract

fetched live from OpenAlex

While the pressure significantly increases in acting towards energy efficiency of communication networks, there is the ever-increasing demand for faster data rates and higher capacities to support a plethora of applications across all industrial and societal sectors driven by exponential traffic growth. Besides, there is a growing concern towards the adverse impacts of climate change, scarcity of raw materials, the rising cost of energy, as well as inequity and bias in the use of technology. Therefore, in addition to the conventional KPIs, key dimensions to be considered for now and in the future is the environmental footprint, energy consumption, resource usage, as well as, inclusivity and fairness. The paper discusses the opportunities, design aspects, challenges and tradeoffs to improve energy efficiency and responsible use of technology, in building sustainable networks for the future.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations4
Published2023
Admission routes1
Has abstractyes

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