A bio-inspired approach for cognitive radio networks

WenJun Xu,JiaRu Lin,Kai Niu,Tao Qiu,ZhiQiang He,Tao Song,Li Guo

doi:10.1007/s11434-012-5216-x

Abstract

The recent increasing interest in cognitive radio networks has motivated the study and development of new approaches capable of coping with the intrinsic challenges of this kind of network, such as dynamic spectrum availability, distributed and heterogeneous network architectures, and soaring complexity. The bio-inspired approaches, with appealing characteristics such as autonomy, adaptation and collective intelligence of collaborative individuals, have been extensively studied. This paper presents a comprehensive survey of bio-inspired approaches for cognitive radio networks, emphasizing their domains of application. Specifically, ant colony optimization and particle warm optimization are further investigated with examples and numerical simulation.

Highlights

The recent increasing interest in cognitive radio networks has motivated the study and development of new approaches capable of coping with the intrinsic challenges of this kind of network, such as dynamic spectrum availability, distributed and heterogeneous network architectures, and soaring complexity
For the sake of highly capable of autonomy and adaptation, bio-inspired approaches seem to be the promising methods to cope with the challenges in cognitive radio networks (CRN), they can be rather slow to adapt to environmental changes
Most of the related works have been focused on optimization problems in network management, especially for the field of swarm intelligence such as ant colony optimization (ACO), particle swarm optimization (PSO) and so on

Summary

Bio-inspired cognitive radio network

He Z Q, et al Chin Sci Bull October (2012) Vol. No.28-29 duction of new networking functions into CRN. As a result of this organization, ant colonies can accomplish complex tasks that in some cases far exceed the individual capabilities of a single insect Among those bio-inspired approaches, ACO and PSO are well-known for their wide applications as efficient evolutionary optimization algorithms. The cognitive devices can establish networks in a dynamic ad hoc way, without necessarily using a centralized infrastructure, so the networks must be capable of self-organized and self-healed to adapt to the heterogeneity and distributivity of CRN. Another major challenge is the soaring complexity cognitive nodes introduced in the conventional wireless networks. The detail description, recent researches and future discussions of these two approaches, especially for application to CRN, have been given in the following two subsections

Design and optimize cognitive radio network with ACO

Simulation settings

Performance evaluation

Findings

Conclusions