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Record W2169949302 · doi:10.1109/icc.2011.5962512

A Game Theory Approach for Inter-Cell Interference Management in OFDM Networks

2011· article· en· W2169949302 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceInterference (communication)Scheme (mathematics)Game theoryOrthogonal frequency-division multiplexingChannel (broadcasting)Power (physics)Transmitter power outputWirelessWireless networkReuseRadio resource managementCooperative game theoryCellular networkAdaptation (eye)Computer networkTransmitterTelecommunicationsMathematicsEngineering

Abstract

fetched live from OpenAlex

Inter-cell interference management is one of the most important issues in next generation wireless networks. In this paper, a transmit power adaptation method using a non cooperative game theory approach is developed in such a way to reduce the inter-cell interference in the whole network. The gaming problem is formulated by allowing that a subchannel could be shared by multiple neighboring cells (i.e. universal frequency reuse). The data rate is enhanced by finding the optimum transmit power for each co-channel user using game theory-based scheme. The performance of the proposed scheme is analyzed and compared with existing scheme. Simulation and analytical results are presented to show the effectiveness of the proposed scheme.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.519

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 head0.016
GPT teacher head0.197
Teacher spread0.181 · 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

Quick stats

Citations24
Published2011
Admission routes1
Has abstractyes

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