رویکرد یادگیری-Q فازی مبتنی بر فرمان ترافیک پویا در شبکه بی سیم چند لایه چند RAT
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|70387||2014||17 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computer Networks, Volume 71, 4 October 2014, Pages 100–116
The infrastructure of current cellular networks must evolve to cope with the increasing demand for mobile-broadband services. Heterogeneous networks are an attractive solution for operators to expand network capacity, based on deploying different Radio Access Technologies, cell sizes and carrier frequencies in the same environment. As a result, operators gain flexibility to distribute traffic across the different networks (or layers) in order to make a more efficient use of resources and enhance network performance. In this work, a dynamic traffic steering technique in multi-RAT multi-layer wireless networks is proposed. In particular, a fuzzy rule-based reinforcement learning algorithm modifies handover parameters according to a specific policy set by the operator, which typically searches for a trade-off between key performance indicators. Results show that the proposed optimization algorithm provides good flexibility to support different policies by simply adjusting some weighting factors. In addition, the Q-Learning algorithm is shown as an effective solution to adapt the network to context variations, such as those produced in the user spatial distribution.