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Dynamic Traffic Offloading and Transmit Point Muting for Energy and Cost Efficiency in Virtualized Radio Access Networks

2015· article· en· W1531722533 on OpenAlexaff
Mohamed Salem, Mohammad Hadi Baligh, Keyvan Zarifi, Amine Maaref, Jianglei Ma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsComputer scienceComputer networkEnergy consumptionQuality of serviceEfficient energy useScheduling (production processes)WirelessWireless networkCellular networkNetwork topologyReal-time computingEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A virtualized radio access network (VRAN) is envisaged in next generation wireless networks. Therein, users experience a seamless ubiquitous service without cell-specific signaling through transparent grouping of densely deployed transmit points (TPs) and helping UEs. Aiming at reducing the Carbon footprint as well as the operational expenditure while maintaining users' QoS, we propose an energy/cost-aware dynamic wideband muting and traffic offloading scheme for the VRAN. The proposed scheme favors muting hypotheses with greater energy/cost savings from TPs with relatively light traffic loads. Such loads are opportunistically offloaded to adjacent TPs to improve their energy efficiency. This is achieved by employing a low-complexity joint wideband muting and multi-point scheduling algorithm optimizing a novel energy-aware utility. The utility accounts for the power consumption models of different TPs, the current cost per unit energy and the TP's predicted 'Soft Loading Ratio'. Operator controls the energy savings-performance tradeoff in individual network regions regardless of the topology. Simulation results show significant energy efficiency and system capacity gains.

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 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 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: none
Teacher disagreement score0.916
Threshold uncertainty score0.593

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 teacher head, 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

Citations2
Published2015
Admission routes1
Has abstractyes

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