Quantifying the relative merits of genetic and swarm algorithms for network optimization in cognitive radio networks

Cognitive engines have been under study and development for a number of years as a technique for addressing the needs of cognitive radios [1,2,3]. More recently there has been effort to expand the role of the cognitive engine to address the needs of a network of cognitive radios [4,5]. Haykin [6] has demonstrated that there is a significant difference between a network of cognitive radios and a cognitive radio network. This paper addresses three questions: 1. What are the significant functional and parametric differences between cognitive algorithms that deal with optimizing the operations of a cognitive radio and cognitive algorithms that optimize the operations of a cognitive radio network? 2. What are the trade-offs in applying the various algorithms to each task? 3. Which algorithms are optimal for the networking tasks? This paper identifies a set of parameters that characterize candidate algorithms and explores the benefits and drawbacks of each for cognitive network tasks. We propose a tiered architecture of cognitive engine algorithms that work in tandem to optimize the use of cognitive networked radios for the optimal success of the networked mission.