Performance analysis of the dynamic switch system based on user activity in cognitive radio network

According to the peak hours of the day, the number of sensed primary users (PUs) and also requesting secondary users (RSUs) will be dramatically increased. Since the radio spectrum is a limited resource, efficient spectrum management techniques on cognitive radio is necessary to manage all PUs, active secondary users (ASUs) and RSUs where all three types of the users have unknown activity ratios. Instant Overbooking Framework for Cognitive Radio Networks (IOFCR) is used to maximize both spectrum utilization and the network revenue under several spectrum management policies. In this paper, dynamic switch system based on user activity is designed for IOFCR. Accordingly, IOFCR automatically switches to the convenient policy to minimize the elapsed time and to get high spectrum utilization with minimal policy complexity in peak hours. Simulation results show that a considerable amount of improvement is achieved through taking user activities into account.

[1]  V. Tuzlukov,et al.  Generalized detector as a spectrum sensor in cognitive radio networks , 2015 .

[2]  Ejaz Ahmed,et al.  Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges , 2016, IEEE Communications Surveys & Tutorials.

[3]  Ossama Younis,et al.  Distance- and Traffic-Aware Channel Assignment in Cognitive Radio Networks , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[4]  Wasim Arif,et al.  Analysis of Spectrum Handoff under Diverse Mobile Traffic Distribution Model in Cognitive Radio , 2014, 2014 International Conference on Devices, Circuits and Communications (ICDCCom).

[5]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

[6]  B. Abolhassani,et al.  A New Protocol for Cooperative Spectrum Sharing in Mobile Cognitive Radio Networks , 2015 .

[7]  Tugrul Cavdar,et al.  Instant overbooking framework for cognitive radio networks , 2015 .

[8]  Rajkumar Buyya,et al.  Managing Cancellations and No-Shows of Reservations with Overbooking to Increase Resource Revenue , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[9]  Dirk Dahlhaus,et al.  Statistical modeling of ISM data traffic in indoor environments for cognitive radio systems , 2015, 2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC).

[10]  Berk Canberk,et al.  Enhancing the performance of multiple IEEE 802.11 network environment by employing a cognitive dynamic fair channel assignment , 2010, 2010 The 9th IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[11]  Erkan Guler,et al.  PSO-optimized Instant Overbooking Framework for cognitive radio networks , 2015, 2015 38th International Conference on Telecommunications and Signal Processing (TSP).

[12]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[13]  R. Phillips,et al.  Pricing and Revenue Optimization , 2005 .

[14]  W. Lieberman.  The Theory and Practice of Revenue Management , 2005 .