CCRA: channel criticality based resource allocation in cognitive radio networks

Summary In cognitive radio networks, signal to interference plus noise ratio (SINR) is a quantity that is used to analyze the bounds of the capacity of a channel. This is the reason for SINR being one of the important parameters toward evaluating the performance of spectrum sharing in every network. To maximize the channel utilization in any network, the SINR of a channel should be considered to be within a threshold. This also leads to lesser power consumption, and the quality of service for the licensed users is maintained. Further, reduced SINR leads to an improvement in the quality of communication in the network. In this paper, a graph theoretic measure for the efficient utilization of channels in cognitive radio networks has been proposed and is named as channel criticality based resource allocation (CCRA). Using the SINR as the weight of the graph, a novel concept of channel criticality has also been introduced in this work. The proposed CCRA technique has also been compared with the existing interference aware channel assignment (IACA) technique in terms of the channel utilization. Through simulations, the CCRA has been observed to outperform the IACA scheme. The average channel utilization of the proposed CCRA was observed to have increased by 8%, when the secondary users were introduced in the network as compared with the IACA technique. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  Siavash Ghavami,et al.  A new practical interference mitigation scheme for interference cognitive radio networks , 2013, Int. J. Commun. Syst..

[2]  Kyunbyoung Ko,et al.  Maximized achievable rate of SINR‐measurement‐based spectrum sharing with binary feedback , 2012, Int. J. Commun. Syst..

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

[4]  M.M. Buddhikot,et al.  Understanding Dynamic Spectrum Access: Models,Taxonomy and Challenges , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[5]  N. M. Abdul Latiff,et al.  Interference aware channel assignment (IACA) for cognitive wireless mesh networks , 2013, 2013 IEEE 11th Malaysia International Conference on Communications (MICC).

[6]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[7]  Kejie Lu,et al.  MaxDiv: an optimal randomized spectrum access with maximum diversity scheme for cognitive radio networks , 2012, Int. J. Commun. Syst..

[8]  S. Borgatti,et al.  Betweenness centrality measures for directed graphs , 1994 .

[9]  Ali Tizghadam,et al.  On Robust Traffic Engineering in Transport Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[10]  Hyung-Kun Park,et al.  Channel prediction-based channel allocation scheme for multichannel cognitive radio networks , 2014, Journal of Communications and Networks.

[11]  Sidan Du,et al.  Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints , 2012, Int. J. Commun. Syst..

[12]  Abbas Mohammadi,et al.  Interference-constraint spectrum allocation model for cognitive radio networks , 2012, 2012 6th IEEE International Conference Intelligent Systems.

[13]  Ali Tizghadam,et al.  Robust QoS-guaranteed network engineering in interference-aware wireless networks , 2013, 2013 Proceedings IEEE INFOCOM.

[14]  Alberto Leon-Garcia,et al.  Survival Value of Communication Networks , 2009, IEEE INFOCOM Workshops 2009.

[15]  Mohamed Khedr,et al.  Opportunistic channel allocation decision making in cognitive radio communications , 2014, Int. J. Commun. Syst..

[16]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

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

[18]  Ali Tizghadam,et al.  Autonomic traffic engineering for network robustness , 2010, IEEE Journal on Selected Areas in Communications.

[19]  F. Richard Yu,et al.  Cognitive Radio Mobile Ad Hoc Networks , 2011 .

[20]  Ali Tizghadam,et al.  Betweenness centrality and resistance distance in communication networks , 2010, IEEE Network.

[21]  Geoffrey Ye Li,et al.  Graph-based robust resource allocation for cognitive radio networks , 2015, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Kathleen M. Carley,et al.  On the robustness of centrality measures under conditions of imperfect data , 2006, Soc. Networks.

[23]  Honggang Wang,et al.  Channel allocation and reallocation for cognitive radio networks , 2013, Wirel. Commun. Mob. Comput..

[24]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

[25]  Jianfeng Wang,et al.  Emerging cognitive radio applications: A survey , 2011, IEEE Communications Magazine.

[26]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[27]  Anthony H. Dekker,et al.  Network Robustness and Graph Topology , 2004, ACSC.

[28]  Sherali Zeadally,et al.  Next Generation Networks (NGNs) , 2010, Int. J. Commun. Syst..

[29]  Ying-Chang Liang,et al.  Maximizing Spectrum Utilization of Cognitive Radio Networks Using Channel Allocation and Power Control , 2006, IEEE Vehicular Technology Conference.