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Proactive Pure Handoff Model with SAW-TOPSIS Selection and Time Series Predict
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/></div></noscript> <!-- /Yandex.Metrika counter --> <!-- Matomo --> <!-- End Matomo Code --> <title>Proactive Pure Handoff Model with SAW-TOPSIS Selection and Time Series Predict</title> <meta name="description" content="Proactive Pure Handoff Model with SAW-TOPSIS Selection and Time Series Predict"> <meta name="keywords" content="cognitive radio, spectrum handoff, decision making, time series, wireless networks"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav 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class="card"> <div class="card-body"><strong>Paper Count:</strong> 87408</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Proactive Pure Handoff Model with SAW-TOPSIS Selection and Time Series Predict</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harold%20V%C3%A1squez">Harold V谩squez</a>, <a href="https://publications.waset.org/abstracts/search?q=Cesar%20Hern%C3%A1ndez"> Cesar Hern谩ndez</a>, <a href="https://publications.waset.org/abstracts/search?q=Ingrid%20P%C3%A1ez"> Ingrid P谩ez </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper approach cognitive radio technic and applied pure proactive handoff Model to decrease interference between PU and SU and comparing it with reactive handoff model. Through the study and analysis of multivariate models SAW and TOPSIS join to 3 dynamic prediction techniques AR, MA ,and ARMA. To evaluate the best model is taken four metrics: number failed handoff, number handoff, number predictions, and number interference. The result presented the advantages using this type of pure proactive models to predict changes in the PU according to the selected channel and reduce interference. The model showed better performance was TOPSIS-MA, although TOPSIS-AR had a higher predictive ability this was not reflected in the interference reduction. <iframe src="https://publications.waset.org/abstracts/33216.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20handoff" title=" spectrum handoff"> spectrum handoff</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20networks" title=" wireless networks"> wireless networks</a> </p> <a 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