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Record W4236741062 · doi:10.32920/ryerson.14643795.v1

Feasibility of CoMP usage in base station switching off during off-peak periods

2021· preprint· en· W4236741062 on OpenAlexaff
Abid Jan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBase stationEnergy consumptionTransmission (telecommunications)Power consumptionEfficient energy useComputer networkComputer scienceBase (topology)Power (physics)Environmental scienceTelecommunicationsElectrical engineeringEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Existing cellular networks remain operational throughout the year irrespective of traffic. The usage of Coordinated Multipoint (CoMP) transmission to provide service in the coverage area of a switched off base station (BS) during off-peak traffic hours has been investigated in this work. The switching off of a BS reduces its energy consumption to zero, however to cover the switched off BS coverage area by neighbouring BS’s, CoMP transmission causes an increase in energy consumption of the neighbouring BS’s. With increasing the number of base stations taking part in CoMP transmission the power consumption of CoMP base stations and site air conditioning unit increases. Results show that the aggressive usage of CoMP is not feasible in most of the twelve switching modes investigated. From the Energy Efficiency Ratio the most energy efficient switching mode is identified. It is then applied to part of a cellular network and the amount of power saving and Carbon Dioxide equivalent (CO2e) is determined. It is found that within a network of 42 cells 7.26% power can be saved by switching off seven base stations during off-peak traffic hours.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.265
Teacher spread0.245 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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