ABSTRACT
Contents 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
13.1.1 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 13.2 Cognitive Radio-Tactical Layer 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
13.2.1 Cognitive Radio Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 13.2.2 Transmission Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 13.2.3 Environmental Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 13.2.4 Performance Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 13.2.5 Fitness Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 13.2.6 Cognitive Radio Engine Techniques . . . . . . . . . . . . . . . . . . . . . . 329 13.2.7 Cognitive Radio Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
13.3 Network Layer Cognition-Strategic Layer 3 . . . . . . . . . . . . . . . . . . . . . . 331 13.3.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 13.3.2 Cognition Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 13.3.3 Available Network Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 13.3.4 Host Identity Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 13.3.5 Policy Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 13.3.6 An Approach for Dynamic Network Layer Selection. . . . . 338
13.4 Cognitive Management and Control-Approaches at Higher Tiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 13.4.1 Application Topology Management Using Strategic
Cognition Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 13.4.2 Data Collection for Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 13.4.3 Network Topology Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 13.4.4 Distributed Cognitive Topology Inference. . . . . . . . . . . . . . . . 342 13.4.5 Application Topology Optimization . . . . . . . . . . . . . . . . . . . . . . 344
13.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
13.1 Introduction Future generations of networks [1,2] have a need for more distributed and comprehensive management and control functionality for both devices themselves and the network overall, as the number of devices and complexity of technology continues to expand. These problems are particularly acute in wireless networks, due to the challenges of the radio environment, mobility, and varied applications. A long-term vision of a solution for future network scenarios was presented in [3] to illustrate the issues and objectives for research. Figure 13.1 represents a proposed architecture [4] based on cognition that uses distributed sensing and control functionality, a collaborative control mechanism, and learning to support the very large networks of the future. This approach is applicable to networking in general; wireless networking in particular can benefit because of the wide range of situations and dynamic operational environments.
