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Greedy spectrum sharing for cognitive MIMO networks

2012· article· en· W1993413558 on OpenAlexaff
Elmahdi Driouch, Wessam Ajib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCognitive radioGreedy algorithmComputer scienceMIMOBase stationComputational complexity theoryMathematical optimizationSpectral efficiencyCognitive networkAlgorithmComputer networkWirelessTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Cognitive radio is regarded as an emerging technique developed to enhance the efficiency of spectrum usage. In this paper, we consider a point-to-multipoint secondary network sharing frequency bands with several primary transmitters and receivers. Assuming a cognitive base station equipped with multiple transmit antennas and single antenna receivers, we propose a greedy spectrum sharing algorithm in order to approach the maximum achievable sum rate of the secondary network. First, we model the overall studied system as a weighted graph, and then we formulate the spectrum sharing as a vertex coloring problem. Second, we show that the two problems are similar and we design a greedy algorithm to solve both problems using two selection criteria. We show through computer simulations that the proposed algorithm is able to achieve near-optimal performances with very low computational complexity.

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 score0.987
Threshold uncertainty score0.404

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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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