doi:

DOI: 10.3724/SP.J.1016.2012.00446

Chinese Journal of Computers (计算机学报) 2012/35:3 PP.446-453

Research on Dynamic Spectrum Allocation Using Cognitive Radio Technologies


Abstract:
With the fast development of wireless communication technologies and the amazing increasing of user numbers in Internet of Things, the limited spectrum resources have become more and more scarce. However, today’s spectrum resources are regulated by a fixed assignment policy and they are in inefficient usage. How to satisfy users’ high mobility and mass date transmission requirements are new challenges. Cognitive Radio is one of these technologies that can offer users a seamless accessing environment, and solves the current spectrum inefficiency problems. It represents a great potential for the development of Internet of Things. In this paper, using Cognitive Radio technologies, we propose a cognitive radio users and networks cooperative spectrum allocation framework, then propose a dynamic spectrum allocation solution. This solution consists of two algorithms: One is a Spectrum Ranking Selecting algorithm (SRS) implemented at cognitive radio users, to meet their QoS and mobility requirements; and the other is a Joint Optimization Matching algorithm (JOM) implemented at the networks, by achieving the co-optimization between spectrum utilization and handoff rate to satisfy the mass data transmission requirement. With the cooperation between cognitive radio users and networks, our solution can construct an efficient dynamic spectrum allocation. Simulation results show that, compared with the traditional mapping algorithm, our solution can significantly improve the performance of networks in terms of throughput by 70% and spectrum handoff rate by 56%.

Key words:Internet of Things (IoT),Cognitive Radio (CR),dynamic spectrum allocation,Quality of Service (QoS)

ReleaseDate:2014-07-21 16:14:56



[1] FCC. Notice of proposed rule making and order. ET Docket No.03-222, Dec, 2003

[2] Akyildiz I F, Lee W Y, Vuran M C, Mohanty S. NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks Journal, 2006, 50(13): 2127-2159

[3] Qin Y, Shen Q, Lin T, Zhao Z, Tang H, Ci S, Feng Z. Cognitive network management in Internet of things. China Communications, 2011, 8(1): 1-7

[4] Zhao Q, Sadler B M. A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 2007, 24(3): 79-89

[5] Zheng H, Cao L. Device-centric spectrum management//Proceedings of the IEEE DySPAN 2005. Baltimore, USA, 2005: 56-65

[6] Yuan Y, Bahl P, Chandra R, Moscibroda T, Wu Y. Alloca-ting dynamic time-spectrum blocks in cognitive radio networks//Proceedings of the MOBIHOC 2007. Montreal, Canada, 2007: 130-139

[7] An C, Ji H, Si P. Dynamic spectrum access with QoS provisioning in cognitive radio networks//Proceedings of the IEEE Globecom 2010. Miami, USA, 2010: 1-5

[8] Jo O, Park J H, Cho D-H. QoS provisioning spectrum management based on intelligent matching and reservation for cognitive radio system//Proceedings of the IEEE CrownCom 2007. Orando, USA, 2007: 530-534

[9] Jo O, Cho D-H. Efficient spectrum matching based on spectrum characteristics in cognitive radio systems//Proceedings of the IEEE WTS 2008. Pomona, Canada, 2008: 230-235

[10] Swami S,Ghosh C,Dhekne R P,Agrawal D P,Berman K A. Graph theoretic approach to QoS-Guaranteed spectrum allocation in cognitive radio networks//Proceedings of the IEEE IPCCC 2008. Austin, USA, 2008: 354-359

[11] Deng H, Yeh C H, Willis R J. Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 2000, 27(10): 963-973

[12] Kuhn H W. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 1955, 2(1-2): 83-97

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