PSO-optimized Instant Overbooking Framework for cognitive radio networks

Nowadays, the emerging wireless communication technologies cause rapid growth in radio spectrum usage. However, the radio spectrum is a limited resource and has almost been fully assigned by the current network operators (NOs). Although numerous studies have been accomplished on the spectrum management, there are rare evolutionary techniques dealing with booking spectrum on cognitive radio. Instant Overbooking Framework for Cognitive Radio networks (IOFCR) is used to maximize both spectrum utilization and the revenue of NOs under several over-booking and pricing policies. In such a network, when a primary user (PU) activity is sensed, the control of which requesting secondary users (SUs) can be denied and which Active SUs can be ejected is managed by IOFCR. In this paper, the Particle Swarm Optimization (PSO) algorithm is applied into the IOFCR to find optimal values of the over-booking price and the Compensation Cost (CC) ratio to optimize revenue and achieve high performance.

[1]  K. J. Ray Liu,et al.  Primary-prioritized Markov approach for dynamic spectrum allocation , 2009, IEEE Transactions on Wireless Communications.

[2]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

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

[6]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[7]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[8]  Lajos Hanzo,et al.  Over-Booking Approach for Dynamic Spectrum Management , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[9]  Dong In Kim,et al.  Joint rate and power allocation for cognitive radios in dynamic spectrum access environment , 2008, IEEE Transactions on Wireless Communications.

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

[11]  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).

[12]  Jonathan Rodriguez,et al.  Testbed for combination of local sensing with geolocation database in real environments , 2012, IEEE Wireless Communications.

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

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