Hybrid Spectrum Access in Cognitive-Radio-Based Smart-Grid Communications Systems

Cognitive-radio-based smart-grid networks have been studied recently as an efficient and reliable communications infrastructure for the future power grid. In this paper, we consider the spectrum resource management in cognitive-radio-based smart-grid networks. A new spectrum access paradigm called hybrid spectrum access (HSA) is proposed, in which both licensed and unlicensed spectrum bands are intelligently scheduled for the transmission of smart-grid services. The admission control problem under HSA is deliberately investigated. Furthermore, the impact of spectrum sensing error on the performance of HSA is analyzed by using a multidimensional Markov chain. Regarding the practical applications of the smart grid, two optimization problems, namely, cost-driven spectrum leasing and quality of service (QoS)-driven spectrum management, are formulated. Numeric results indicate that the HSA strategy is able to significantly improve the QoS of the smart-grid services, save the cost in spectrum leasing, and maintain the system interference at a sufficiently low range.

[1]  Ian F. Akyildiz,et al.  Optimal spectrum sensing framework for cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[2]  Shengli Xie,et al.  Cross-Layer Optimized Call Admission Control in Cognitive Radio Networks , 2010, Mob. Networks Appl..

[3]  Zhongding Lei,et al.  IEEE 802.22: The first cognitive radio wireless regional area network standard , 2009, IEEE Communications Magazine.

[4]  Gerald Thomas Heydt,et al.  The Next Generation of Power Distribution Systems , 2010, IEEE Transactions on Smart Grid.

[5]  Jiming Chen,et al.  Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid , 2013, IEEE Transactions on Smart Grid.

[6]  Mohsen Guizani,et al.  Home M2M networks: Architectures, standards, and QoS improvement , 2011, IEEE Communications Magazine.

[7]  Shengli Xie,et al.  Cognitive machine-to-machine communications: visions and potentials for the smart grid , 2012, IEEE Network.

[8]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[9]  Laurence T. Yang,et al.  Aggregated-Proofs Based Privacy-Preserving Authentication for V2G Networks in the Smart Grid , 2012, IEEE Transactions on Smart Grid.

[10]  Zhe Chen,et al.  Cognitive Radio for Smart Grid: Theory, Algorithms, and Security , 2011, Int. J. Digit. Multim. Broadcast..

[11]  Mohsen Guizani,et al.  Secure service provision in smart grid communications , 2012, IEEE Communications Magazine.

[12]  Randy L. Ekl,et al.  Security Technology for Smart Grid Networks , 2010, IEEE Transactions on Smart Grid.

[13]  Pramode K. Verma,et al.  A proposed communications infrastructure for the smart grid , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[14]  Husheng Li,et al.  QoS Routing in Smart Grid , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[15]  Mohsen Guizani,et al.  Cognitive radio based hierarchical communications infrastructure for smart grid , 2011, IEEE Network.

[16]  Mohsen Guizani,et al.  Secondary users cooperation in cognitive radio networks: balancing sensing accuracy and efficiency , 2012, IEEE Wireless Communications.

[17]  Mahesh Sooriyabandara,et al.  The new frontier of communications research: smart grid and smart metering , 2010, e-Energy.

[18]  Jose Medina,et al.  Demand Response and Distribution Grid Operations: Opportunities and Challenges , 2010, IEEE Transactions on Smart Grid.

[19]  Yan Zhang,et al.  A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks , 2010, IEEE Transactions on Vehicular Technology.

[20]  Xiaorong Zhu,et al.  Analysis of Cognitive Radio Spectrum Access with Optimal Channel Reservation , 2007, IEEE Communications Letters.

[21]  C. Bennett,et al.  Networking AMI Smart Meters , 2008, 2008 IEEE Energy 2030 Conference.