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Record W2054335779 · doi:10.1109/pimrc.2013.6666678

Analysis of load dependent energy efficiency of two-tier heterogeneous cellular networks

2013· article· en· W2054335779 on OpenAlexaff
Jaya Rao, Abraham O. Fapojuwo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTelecommunications linkComputer scienceThroughputBase stationTransmission (telecommunications)Cellular networkEfficient energy usePower controlHeterogeneous networkMacroComputer networkPower (physics)Distributed computingLoad balancing (electrical power)Mathematical optimizationWireless networkWirelessEngineeringMathematicsTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a tractable analytical framework for investigating the energy efficiency (EE) of a 2-tier heterogeneous cellular network consisting of macro and picocell base stations (BS) is developed. The BSs, which are randomly distributed, serve a number of users in the downlink under shared spectrum operation. By incorporating a tunable downlink power control mechanism, the successful transmission probability and the average throughput achievable in both tiers are analytically derived. Since varying load conditions has an important implication on the network performance, the EE is investigated as a function of the load level, considering the ability to transfer the load between the 2 tiers. An optimization problem that maximizes the throughput constrained 2-tier EE is formulated and solved to determine the optimal network operational parameters. In addition to providing valuable design insights, the findings reveal that, for different load conditions, it is feasible to significantly enhance the EE performance by optimally adapting the network configuration.

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 score0.921
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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