MétaCan
Menu
Back to cohort
Record W2086471688 · doi:10.1109/glocom.2014.7037205

A stochastic power control game for two-tier cellular networks with energy harvesting small cells

2014· article· en· W2086471688 on OpenAlexaff
Tran Kien Thuc, Hina Tabassum, Ekram Hossain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsMacrocellPower controlComputer scienceBase stationTransmission (telecommunications)Telecommunications linkStochastic geometryWirelessComputer networkSmall cellMathematical optimizationEnergy harvestingPower (physics)Energy (signal processing)TelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest their energy from environment sources whereas the macro base station (MBS) uses conventional power supply. Given a stochastic energy arrival process, this paper derives a power control policy for the downlink transmission of both MBS and SBSs such that they can obtain an equilibrium of their own objectives on a long-term basis (e.g., maximizing the transmission rate for SBSs while maintaining the target signal-to-interference-plus-noise ratio (SINR) at the macro users) on a given transmission channel. To this end, we propose a single controller stochastic game and develop a power control policy as a solution of a quadratic programming problem. Numerical results demonstrate the significance of the developed optimal power control policy over the conventional fixed and random power control policies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.163
Teacher spread0.158 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207