ABSTRACT
Contents 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 16.2 Immune System and Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . 412
16.2.1 Biological Immune System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 16.2.2 Immune System-Inspired Cognitive Radio Networks . . . . . . . . . 414
16.3 Immune System-Inspired Spectrum Sensing and Management in Cognitive Radio Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 16.3.1 Immune System-Inspired Spectrum-Sensing Model
for Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 16.3.2 Immune System-Inspired Spectrum Management Model
for Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 16.4 Biological Task Allocation and Spectrum Sharing in Cognitive Radio
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 16.4.1 Biological Task Allocation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419
16.4.2 Biological Task Allocation-Inspired Spectrum-Sharing Model for Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 420
16.5 Biological Switching-Inspired Spectrum Mobility Management in Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422
16.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425
Cognitive radio (CR) is a promising wireless communication technology that aims to detect temporally unused spectrum bands by sensing its radio environment and communicate over these spectrum bands in order to enhance overall spectrum utilization. These objectives pose difficulties and have additional requirements such as self-organizing, self-adaptation, and scalability over conventional communication paradigms. Therefore, it is imperative to develop new communication models and algorithms. In nature, as a result of the natural evolution, biological systems have acquired great capabilities, which can be modeled and adopted for addressing the challenges in the CR domain. In this chapter, we explore the surprising similarities and mapping between cognitive radio network (CRN) architectures and natural biological systems. We introduce potential solution avenues from the biological systems toward addressing the challenges ofCRN such as spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility/handoff management. The objective of this chapter is to serve as a roadmap for the development of efficient scalable, adaptive, and self-organizing bio-inspired communication techniques for dynamic spectrum access in CRN architectures.
