On Performance Analysis of IEEE 802.22 PHY for Cognitive Radio based Smart Grid Communications

Smart Grid (SG) communication architecture involves multiple network layers starting from control center to consumer areas, in a top-bottom hierarchy. At each network layer, several SG applications need to be supported with diverse Quality-of-Service (QoS) requirements. Cognitive Radio (CR) which provides efficient spectrum utilization through opportunistic spectrum access is a promising solution to meet these versatile requirements. The IEEE 802.22 Wireless Regional Area Network (WRAN) is aimed at the use of CR by allowing the unused spectrum of television broadcast service for providing broadband access to remote areas. This standard provide a variety of the modes of operation to support various QoS demands. In this paper, we provide the performance comparison of various modes of IEEE 802.22 standard when applied to different SG applications to show its compliance with the SG communication infrastructure. The frame sizes and other parameters are taken as per the SG application requirements. Furthermore, various bounds on channel quality are also provided to support the reliability requirements of different SG applications using this standard.

[1]  Xin Zhang,et al.  A new standard activity in IEEE 802.22 wireless regional area networks: Enhancement for broadband services and monitoring applications in TV whitespace , 2012, The 15th International Symposium on Wireless Personal Multimedia Communications.

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

[3]  Akhtar Kalam,et al.  Compliance of IEEE 802.22 WRAN for field area network in smart grid , 2016, 2016 IEEE International Conference on Power System Technology (POWERCON).

[4]  Mohd Wazir Mustafa,et al.  Smart grids security challenges: Classification by sources of threats , 2018, Journal of Electrical Systems and Information Technology.

[5]  Luigi Paura,et al.  Sensing-time optimization in cognitive radio enabling Smart Grid , 2014, 2014 Euro Med Telco Conference (EMTC).

[6]  V. C. Gungor,et al.  Cognitive Radio Networks for Smart Grid Applications: A Promising Technology to Overcome Spectrum Inefficiency , 2012, IEEE Vehicular Technology Magazine.

[7]  Martin Reisslein,et al.  Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols , 2016, IEEE Communications Surveys & Tutorials.

[8]  Kranthimanoj Nagothu,et al.  MIMO-interference aware scheduling enabling the allocation of unbounded co-channels in unplanned networks , 2013, 2013 IEEE International Systems Conference (SysCon).

[9]  Liangzhong YAO,et al.  Challenges and progresses of energy storage technology and its application in power systems , 2016 .

[10]  A. Ghassemi,et al.  Cognitive Radio for Smart Grid Communications , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[11]  Saifur Rahman,et al.  Communication network requirements for major smart grid applications in HAN, NAN and WAN , 2014, Comput. Networks.

[12]  Shengli Xie,et al.  QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Akhtar Kalam,et al.  Review of IEEE 802.22 and IEC 61850 for real-time communication in Smart Grid , 2015, 2015 International Conference on Computing and Network Communications (CoCoNet).

[14]  Marco Rivera,et al.  Communication systems and security issues in smart microgrids , 2017, 2017 IEEE Southern Power Electronics Conference (SPEC).

[15]  Huaizhou Shi,et al.  Fairness and network capacity trade-off in P2P IEEE 802.22 networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

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