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Record W2684278524 · doi:10.1109/ccece.2017.7946747

Energy and spectral efficiency in cellular networks considering fading, path loss, and interference

2017· article· en· W2684278524 on OpenAlexaff
Abdulbaset M. Hamed, Raveendra K. Rao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsPath lossSpectral efficiencyFadingEfficient energy useComputer scienceTelecommunications linkBase stationCellular networkNakagami distributionInterference (communication)Stochastic geometryEnergy consumptionElectronic engineeringComputer networkChannel (broadcasting)Topology (electrical circuits)TelecommunicationsWirelessEngineeringMathematicsElectrical engineeringStatistics

Abstract

fetched live from OpenAlex

Due to the increase of energy consumption in wireless systems, energy-efficient cellular planning concept has become an important concern in designing cellular networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) analysis for single and multi-cell cellular systems are presented and investigated. The efficiency analysis is studied under the influence of Nakagami-m multi-path fading superimposed on path loss, and co-channel interference for three base station (BS) antenna configurations which are omni, and 120o and 60o directive antennas. The downlink SE and EE are derived and simulated concerning the random users' location, normalized reuse distance, cell radius, and random channel gain. Theoretical and simulation results show that, as expected, single cell scenario provides higher efficiency than multi cell; however, antenna directivity improves both efficiency metrics. The analysis provides insight contribution for the SE-EE trade-off issue in cellular networks for which most of the network and propagation parameters are included in the analysis.

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 score0.835
Threshold uncertainty score0.401

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 head0.008
GPT teacher head0.201
Teacher spread0.192 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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