A dynamic spectrum decision scheme for heterogeneous cognitive radio networks

Spectrum sensing and spectrum management are the main challenging functions that cognitive radio (CR) networks have to perform. In this paper, we focus specifically on the spectrum decision problem. This problem is worsened in the presence of users with different demands and spectrum channels with different properties in a heterogeneous network. For accurate and proper spectrum management, we propose a spectrum decision algorithm for spectrum brokers, which takes user type and spectrum channel properties into consideration. This approach increases both the number of users that can get proper spectrum bands and the throughput of the system. The algorithm also includes a "patience" option. Users that choose the "patience" option agree to wait for a predetermined amount of time until their connection is established. Meanwhile, a better spectrum may become available. This option increases the efficiency of spectrum usage too.

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

[2]  Wenhui Zhang,et al.  Handover decision using fuzzy MADM in heterogeneous networks , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[3]  Rahim Tafazolli,et al.  Auction driven dynamic spectrum allocation: optimal bidding, pricing and service priorities for multi-rate, multi-class CDMA , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[4]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[5]  László Kovács,et al.  Spatio-temporal spectrum management model for dynamic spectrum access networks , 2006, TAPAS '06.

[6]  M. Buddhikot,et al.  Spectrum management in coordinated dynamic spectrum access based cellular networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

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

[8]  E. Del Re,et al.  Power allocation strategy for Cognitive Radio terminals , 2008, 2008 First International Workshop on Cognitive Radio and Advanced Spectrum Management.

[9]  Panagiotis Demestichas,et al.  Neural network-based learning schemes for cognitive radio systems , 2008, Comput. Commun..

[10]  Enrico Del Re,et al.  Resource Allocation in Cognitive Radio Networks: A Comparison Between Game Theory Based and Heuristic Approaches , 2009, Wirel. Pers. Commun..

[11]  L. Shapley,et al.  Potential Games , 1994 .

[12]  Jun Zhao,et al.  Distributed coordination in dynamic spectrum allocation networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[13]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[14]  Milind M. Buddhikot,et al.  DIMSUMnet: new directions in wireless networking using coordinated dynamic spectrum , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

[15]  S. T. Chung,et al.  A game-theoretic approach to power allocation in frequency-selective gaussian interference channels , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..