Software Defined Cognitive Radio Network Framework: Design and Evaluation

Software Defined Cognitive Radio Network Framework: Design and Evaluation

Yaser Jararweh (Computer Science Department, Jordan University of Science and Technology, Irbid, Jordan), Mahmoud Al-Ayyoub (Computer Science Department, Jordan University of Science and Technology, Irbid, Jordan), Ahmad Doulat (Computer Science Department, Jordan University of Science and Technology, Irbid, Jordan), Ahmad Al Abed Al Aziz (Computer Science Department, Jordan University of Science and Technology, Irbid, Jordan), Haythem A. Bany Salameh (Department of Telecommunications Engineering, Yarmouk University, Irbid, Jordan) and Abdallah A. Khreishah (Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA)
Copyright: © 2015 |Pages: 17
DOI: 10.4018/ijghpc.2015010102
OnDemand PDF Download:
List Price: $37.50


Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.
Article Preview

1. Introduction

Due to the low cost and widespread acceptance of the wireless communication devices, the available radio spectrum is becoming insufficient to fulfill the needs of these large numbers of wireless devices. Users of wireless networks are generally viewed as either Primary Users (PUs) or Secondary Users (SUs). PUs are licensed to operate over a licensed spectrum that is reserved for their own services. These reserved spectrum bands are not fully utilized (Bany Salameh and Krunz 2009). So, to improve the spectrum utilization, SUs are allowed to opportunistically access the licensed bands without affecting the performance of the PUs.

Cognitive Radio (CR) is a technology that enables a cognitive radio node (SU) to sense its surrounding environment and change its transmission parameters according to the acquired information with the goal of increasing spectrum utilization. For example, the cognitive Radio Interface (RI) can sense its environment for the available spectrum (spectrum holes) and then divides it into a set of channels and select the best channel that does not cause interference with a PU according to a predefined policy (Bany Salameh 2010).

The IEEE 802.22 standard for Wireless Regional Area Network (WRAN) is the first effort to make commercial applications based on CR technology feasible (Cordeiro et al. 2005, Bany Salameh et al. 2014, Hani et al. 2013, Mhaidat et. al. 2014). According to IEEE 802.22, a network consists of a BS and a set of users. The available spectrum is divided into orthogonal channels. To provide better knowledge of the availability of channels, the users sense the spectrum availability in their vicinity and periodically send their sensing reports to the BS. The BS acts as a centralized broker governing the users’ use of the spectrum. Thus, whenever a user wants to transmit, the BS has to be consulted. Such high overhead of control communication between the users and the BS will surely have a negative impact on the overall performance of the network.

Simplifying network resource configuration and management is a very challenging and complex task. The newly emerged concept of software defined networking (SDN) provides a new paradigm shift in efficiently managing network resources (Kim and Feamster 2013). SDN is receiving an increasing attention from both academic and industrial communities for its promising features. SDN permits network managers to control the network resources with software tools without the need for tedious manual configuration. It promotes the separation between the data plane and the control plane of the network (Costanzo et al. 2012, Li et al. 2012, Hasan et al. 2013).

In this paper, we introduce Software Defined Cognitive Radio Network (SD-CRN) framework. SD-CRN provides a virtualization based resource allocation approach for cognitive radio networks. In our approach, we advocate delegating some of the management responsibilities of the BS to the users allowing them to make local decisions. By doing so, we aim to reduce the users’ reliance on the BS and improve network performance by reducing the unneeded control overhead. The resource management process is totally software based without the need for any network administrator interventions. The distinct features/advantages of our work are: (1) the cooperative resource management over wireless cognitive networks, (2) a centralized BS controls and manages the resource allocation using a centralized manager called the Global Hypervisor (GH) among the different cognitive users joining the network without affecting the PUs, and (3) virtualizing the physical radio nodes to have several instances for different virtual networks, each of which having an intermediate layer called the Local Hypervisor (LH). The LHs support the GH in distributing the resources to minimize the control overhead at the BS. These features/advantages play a significant role in improving the overall network throughput, as well as minimizing the management overhead at the BS (Jararweh et al. 2014, Doulat et al. 2014).

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing