Immune system-inspired evolutionary opportunistic spectrum access in cognitive radio ad hoc networks

Underutilization of licensed spectrum stimulates the opportunistic spectrum access (OSA) paradigm that aims to enable unlicensed users to detect and access the temporarily unused spectrum bands so as to enhance overall spectrum utilization. However, the realization of these paradigms entails several difficulties such as self-organization of unlicensed users, self-regulation of communication parameters, and self-adaptation to time-varying radio environment. In nature, biological systems intrinsically have these great abilities that can be modeled and adopted to overcome the difficulties posed by opportunistic spectrum access. In this paper, a new Immune system-inspired Evolutionary Opportunistic Spectrum Access (ESA) protocol is introduced. Based on the self-nonself detection and clonal selection principles in immune system, ESA allows unlicensed users to separately detect, share, and access the available spectrum bands without interfering the licensed users. The overall ESA operations do not need for any priori information about the access statistics of licensed users and also do not need for any coordination and message exchanges among the network nodes. In addition, unlike the existing works, ESA does not require any dedicated control channel in the entire network. Furthermore, ESA also exploits the contention among the nodes and their mobility, if exists, towards accelerating the evolution in the system, and hence, yielding higher overall spectrum utilization. Performance evaluation results show that ESA achieves high throughput under various network conditions.

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

[2]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[3]  Kang G. Shin,et al.  OS-MAC: An Efficient MAC Protocol for Spectrum-Agile Wireless Networks , 2008, IEEE Transactions on Mobile Computing.

[4]  Jean-Christophe Dunat,et al.  Bio-Inspired Algorithms for Dynamic Resource Allocation in Cognitive Wireless Networks , 2007, CrownCom.

[5]  C. Cordeiro,et al.  C-MAC: A Cognitive MAC Protocol for Multi-Channel Wireless Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[6]  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..

[7]  Ian F. Akyildiz,et al.  Optimal spectrum sensing framework for cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[8]  Sofie Pollin,et al.  Performance Analysis of Multichannel Medium Access Control Algorithms for Opportunistic Spectrum Access , 2009, IEEE Transactions on Vehicular Technology.

[9]  Chien-Chung Shen,et al.  Single-Radio Adaptive Channel Algorithm for Spectrum Agile Wireless Ad Hoc Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[10]  Panagiotis Papadimitratos,et al.  A bandwidth sharing approach to improve licensed spectrum utilization , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[11]  Ian F. Akyildiz,et al.  CRAHNs: Cognitive radio ad hoc networks , 2009, Ad Hoc Networks.

[12]  Özgür B. Akan,et al.  BIOlogically-Inspired Spectrum Sharing in Cognitive Radio Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[13]  C.-C. Jay Kuo,et al.  A Cognitive MAC Protocol Using Statistical Channel Allocation for Wireless Ad-Hoc Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[14]  H. Haas,et al.  Interference Aware Medium Access for Dynamic Spectrum Sharing , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[15]  Peter J. Bentley,et al.  Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

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

[17]  L. Ma,et al.  Dynamic open spectrum sharing MAC protocol for wireless ad hoc networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[18]  Xuemin Shen,et al.  HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management , 2008, IEEE Journal on Selected Areas in Communications.

[19]  Özgür B. Akan,et al.  Immune System Based Distributed Node and Rate Selection in Wireless Sensor Networks , 2006, 2006 1st Bio-Inspired Models of Network, Information and Computing Systems.

[20]  Zhi Ding,et al.  ESCAPE: A Channel Evacuation Protocol for Spectrum-Agile Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[21]  Carlos A. Coello Coello,et al.  Multiobjective Optimization Using Ideas from the Clonal Selection Principle , 2003, GECCO.

[22]  Andrew M. Tyrrell,et al.  Hardware fault tolerance: an immunological solution , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[23]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[24]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[25]  Pietro Simone Oliveto,et al.  On the Convergence of Immune Algorithms , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.