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Record W1579772159 · doi:10.1109/icc.2015.7248294

Impact of intra- and inter-RAT offloading on the spectrum/energy efficiency of HetNets

2015· article· en· W1579772159 on OpenAlexaff
Jaya Rao, Abraham O. Fapojuwo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHeterogeneous networkQuality of serviceComputer scienceMathematical optimizationSpectral efficiencyEfficient energy useCellular networkOptimization problemPareto principlePareto optimalMulti-objective optimizationComputer networkDistributed computingWireless networkWirelessMathematicsTelecommunicationsEngineeringAlgorithm

Abstract

fetched live from OpenAlex

This paper addresses the problem of simultaneously achieving high spectral efficiency (SE) and energy efficiency (EE) in the heterogeneous radio access technology (RAT) environment via biased intra- and inter-RAT offloading. An analytical framework is developed for investigating the SE and EE performance of a two-RAT heterogeneous network (HetNet), based on which it is shown that the feasibility of increasing the SE and EE via offloading is strongly dependent on the load level and the biased offloading factors. For jointly maximizing the SE and EE, a multi-objective optimization problem subject to quality of service (QoS) constraints is formulated and solved to give the Pareto optimal operational regime in terms of the network parameters. Following this, the constrained Pareto regime is used to quantify the tradeoff between SE and EE as an opportunity cost measure. By opportunistically adapting the small cell BS density and biased offloading factors to the load conditions, we numerically show the range of load values that achieves a good balance in the SE-EE tradeoff while satisfying the specified users' QoS requirements.

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: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.208

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 head0.012
GPT teacher head0.234
Teacher spread0.221 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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