شبکه های رادیویی شناختی مبتنی بر مجازی سازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|76096||2016||15 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Systems and Software, Volume 117, July 2016, Pages 15–29
The emerging network virtualization technique is considered as a promising technology that enables the deployment of multiple virtual networks over a single physical network. These virtual networks are allowed to share the set of available resources in order to provide different services to their intended users. While several previous studies have focused on wired network virtualization, the field of wireless network virtualization is not well investigated. One of the promising wireless technologies is the Cognitive Radio (CR) technology that aims to handle the spectrum scarcity problem through efficient Dynamic Spectrum Access (DSA). In this paper, we propose to incorporate virtualization concepts into CR Networks (CRNs) to improve their performance. We start by explaining how the concept of multilayer hypervisors can be used within a CRN cell to manage its resources more efficiently by allowing the CR Base Station (BS) to delegate some of its management responsibilities to the CR users. By reducing the CRN users’ reliance on the CRN BS, the amount of control messages can be decreased leading to reduced delay and improved throughput. Moreover, the proposed framework allows CRNs to better utilize its resources and support higher traffic loads which is in accordance with the recent technological advances that enable the Customer-Premises Equipments (CPEs) of potential CR users (such as smart phone users) to concurrently run multiple applications each generating its own traffic. We then show how our framework can be extended to handle multi-cell CRNs. Such an extension requires addressing the self-coexistence problem. To this end, we use a traffic load aware channel distribution algorithm. Through simulations, we show that our proposed framework can significantly enhance the CRN performance in terms of blocking probability and network throughput with different primary user level of activities.