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Cognitive Base Stations in LTE/3GPP Femtocells: A Correlated Equilibrium Game-Theoretic Approach

2011· article· en· W2080920551 on OpenAlexaff
Jane W. Huang, Vikram Krishnamurthy

Bibliographic record

VenueIEEE Transactions on Communications · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMacrocellFemtocellBase stationTelecommunications linkCognitive radioComputer scienceResource allocationNode (physics)Game theoryPotential gameFrequency allocationComputer networkInterference (communication)Mathematical optimizationResource management (computing)LTE AdvancedNash equilibriumWirelessEngineeringTelecommunicationsMathematicsMathematical economics

Abstract

fetched live from OpenAlex

This paper considers downlink spectrum allocation in a long term evolution (LTE) system macrocell which contains multiple femtocells. By incorporating cognitive capabilities into femtocell base stations, the Home evolved Node Bs (HeNBs) can be formulated as secondary base stations seeking to maximize the spectrum utility while minimizing interference to primary base stations (evolved Node-Bs). The competition amongst cognitive HeNBs for spectrum resources is formulated as a non-cooperative game-theoretic learning problem where each agent (HeNB) seeks to adapt its strategy in real time. We formulate the resource block (RB) allocation among HeNBs in the downlink of a LTE system using a game-theoretic framework, where the correlated equilibrium solutions of the formulated game are being investigated. A distributed RB access algorithm is proposed to compute the correlated equilibrium RB allocation policy..

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.254
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations129
Published2011
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on CommunicationsSame topicAdvanced MIMO Systems OptimizationFrench-language works237,207