ABSTRACT
Contents 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 7.2 Problems Encountered by Cognitive Radio Networks . . . . . . . . . . . . . 185
7.2.1 Spectrum-Aware Channel Selection: A Global Coordination Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
7.3 Distributed Cognitive Radio Coordination through the Principles of Swarm Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 7.3.1 Requirements for Concept Transfer . . . . . . . . . . . . . . . . . . . . . . . . . 189
7.3.1.1 The Surrounding Environment Is Memoryless . . . . 190 7.3.1.2 State Information Can Be Directly Observed on
a Limited Horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 7.3.1.3 Individuals Can Communicate through
Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 7.3.2 Mechanisms of Swarm Behavior in Nature . . . . . . . . . . . . . . . . . 191
7.3.2.1 A Formalization of Swarm Behavior . . . . . . . . . . . . . . . 192
7.3.3 How Swarm Behavior Can Be Transferred to Cognitive Radio Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 7.3.3.1 Controlled Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 7.3.3.2 Dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 7.3.3.3 Variable Types and Dynamics . . . . . . . . . . . . . . . . . . . . . 197
7.3.4 Example Adaptations for a Cognitive Radio Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 7.3.4.1 Cohesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 7.3.4.2 Obstacle Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 7.3.4.3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
7.4 Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 7.4.1 A Cognitive Radio Swarm Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 7.4.2 Hardware Implementation Using Low-Cost Commodity
Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 7.4.2.1 Implementation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 7.4.2.2 Experimental Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 7.4.2.3 Convergence with Local Information . . . . . . . . . . . . . . 206 7.4.2.4 Interoperability with Different Control
Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 7.4.2.5 Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 7.4.2.6 Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
7.4.3 Technical Limitations and Their Implications to the Biological Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
This chapter explores the problem of configuration and coordination of cognitive radio networks through the use of local control algorithms. To address this problem, this research examines the issue of channel assignment for cognitive radio networks, wherein different nodes must agree on which channels they will use to communicate. Through a series of theoretical analysis and hardware implementation, this work demonstrates that biologically inspired local control algorithms are a feasible and worthwhile avenue for cognitive radio coordination and shows promising prospects for other areas in wireless systems such as sensor and ad hoc networks. This research also demonstrates that local control based on biologically inspired algorithms is well suited for the coordination of cognitive radio nodes in heterogeneous environments.
7.1 Introduction This chapter examines the application of efficient local control algorithms to the problem of managing and coordinating cognitive radio networks. These algorithms
must be able to configure the radio to allow satisfactory communicationwith external nodes because the cognitive radio’s objective is to exchange data. Correspondingly, this chapter explores whether local control approaches (specifically based on biologically inspired algorithms) can be used to efficiently coordinate a cognitive radio network without the use of global information, but by relying on information that can be collected independently by each station.
