SD-CRN: Software Defined Cognitive Radio Network Framework

Jararweh, Yaser; Ayyoub, Mahmoud Al; Doulat, Ahmad; Aziz, Ahmad Al Abed Al; Khreishah, Abdallah

doi:10.1109/ic2e.2014.88

Cited by 26 publications

(11 citation statements)

References 19 publications

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“…Consistent results can be observed in Figures 4B and 4C; the only difference is that, as the field area increases, the achieved average data rate decreases, and this is expected as the received power (and hence the SNR) at the receiver side decreases with distance as indicated by Equation (24), and hence, the detection accuracy goes down, as shown in Table 3.…”

Section: Insupporting

confidence: 82%

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A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Halloush

Almalkawi

et al. 2019

Trans Emerging Tel Tech

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Algorithms for spectrum sharing over resource‐limited cognitive radio networks are often designed to solve specific problems. This means that a certain algorithm deals specifically with a certain limited resource, and is not suitable for other resources. This limitation violates the software‐defined networking philosophy, where a scheme has to be reprogrammable to cope with different limited resources that can dynamically arise depending on network conditions. In this work, we investigate the problem of spectrum sharing in resource‐limited cognitive radio networks. Specifically, we introduce a novel spectrum sharing algorithm that is compatible with software‐defined networks, in the sense that it can be reprogrammed to support multiple constraints on resources of different types. A main feature of the proposed scheme is that computations could be distributed across multiple processing units to reduce computational complexity on each unit. In addition, the proposed algorithm is equipped with a fairness scheme. Simulation results demonstrate the efficacy of the proposed scheme.

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Section: Insupporting

confidence: 82%

“…Table 2 lists the average SNR value of the PU-SU sensing channels at each SU over the three channels and for all field areas. Note that, as the field area increases, the SNR values decrease since P r decreases as indicated by Equation (24).…”

Section: Simulation Resultsmentioning

confidence: 96%

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Halloush

Almalkawi

et al. 2019

Trans Emerging Tel Tech

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“…In doing so, cognitive radio users reduce reliance on the CRN BSs and the remaining physical network resources. However, SDN&R architecture differs from [18], since control is centralized and SDN realization is integrated to the BSs to understand the OpenFlow protocol and to perform radio resource management based on a SDN-controller application.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], a framework for a virtualization based SDN resource management platform for cognitive radio networks (CRNs) is presented. It also introduces a semidecentralized control scheme that allows the CRN BS to delegate some of the management responsibilities to the network users.…”

Section: Related Workmentioning

confidence: 99%

Implementation of OpenFlow based cognitive radio network architecture: SDN&R

et al. 2015

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The static conventional network architecture is ill-suited to the growing management complexity and highly dynamic wireless network topologies. Software Defined Radio systems and their extension to cognitive and smart radio are characterized by distinct control loops for management which constantly increase network complexity and management inefficiencies, due to clear-cut between radio and core network management. Adding numerous devices and networks together will constantly increase the management cost, thus hinders scalability. Therefore, a holistic solution to synchronize radio and networks status has an elevated demand. To interconnect these systems and devices together, there is a need for a common management interface. OpenFlow is the first standard interface that enables Software Defined Networking (SDN). It can be rolled out in a variety of networking devices to enable improved automation and management by using common Application Program Interfaces to abstract the underlying networking details. The Software Defined Networking & Radio (SDN&R) framework proposed here has a potential combination between SDN and Radio networks to discover the underlying dynamism in cognitive access networks with integrated radio management. By isolating the control plane from the data plane, SDN&R enables a flexible management framework empowered by end-to-end goals through OpenFlow. In this article, we propose, validate, and evaluate the SDN&R architecture. In doing so, first we implement the OpenFlow enabled cognitive basestations (BSs) on Wireless Open-Access Research Platform. Furthermore, we develop software agents on BSs to provide radio status information to the cognitive control application implemented on the SDN controller. The results verify that the proposed framework in-lines with layer-2 or layer-3 forwarding performance. We claim that this work represents the first successful implementation results which synergizes SDN with Cognitive networks that motivates researchers towards SDN based radio resource management.

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“…To simplify resource configuration and reduce overhead, an SDN-based CRN framework was pro-posed in Jararweh et al (2014), in which the resource management was totally software-based. The proposed framework consists of global hypervisors (GHs) and local hypervisors (LHs).…”

Section: Cognitive Radio Networkmentioning

confidence: 99%

Enhancing the performance of futurewireless networks with software-defined networking

Feng

Mao

Jiang

2016

Frontiers Inf Technol Electronic Eng

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To provide ubiquitous Internet access under the explosive increase of applications and data traffic, the current network architecture has become highly heterogeneous and complex, making network management a challenging task. To this end, software-defined networking (SDN) has been proposed as a promising solution. In the SDN architecture, the control plane and the data plane are decoupled, and the network infrastructures are abstracted and managed by a centralized controller. With SDN, efficient and flexible network control can be achieved, which potentially enhances network performance. To harvest the benefits of SDN in wireless networks, the software-defined wireless network (SDWN) architecture has been recently considered. In this paper, we first analyze the applications of SDN to different types of wireless networks. We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks. Finally, we present possible future research directions of SDWN.

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SD-CRN: Software Defined Cognitive Radio Network Framework

Cited by 26 publications

References 19 publications

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

A rate‐maximizing spectrum sharing algorithm for cognitive radio networks with generic resource constraints

Implementation of OpenFlow based cognitive radio network architecture: SDN&R

Enhancing the performance of futurewireless networks with software-defined networking

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