Bio-Inspired Dynamic Radio Access in Cognitive Networks based on Social Foraging Swarms

There is strong trend, in current research on communication and sensor networks, to study selforganizing, self-healing systems. This poses great challenges to the research on decentralized systems, but at the same offers great potentials for future developments, especially in view of the current trend towards miniaturized systems. Even if the development of self-organizing systems is probably at the beginning, biological systems offers many examples of self-organization and selfhealing. This is as testified, for example, by swarming behaviors, brain activity, and so on. It is then of great interest to derive mathematical models of biological systems and see how they can suggest novel design tools for engineers. Signal Processing can play a big role in this cross-fertilization, as it can help to find out manageable mathematical problems, study their behavior and test the performance in the presence of disturbances. The challenge is to establish a cross-fertilization of ideas from biological to artificial systems, as well as to help understanding biological systems as such. This dissertation considers the problem of dynamic radio access based on sensing in cognitive radio networks. In particular, we follow a rather alternative path with respect to more conventional approaches and, inspired by biological models, we formulate the search for radio resources, i.e. time and frequency slots, as the search for food by a flock of birds swarming in a cooperative manner, but without any centralized control. The interference distribution in the time-frequency plane takes the role of the food spatial distribution: The birds (radio nodes) fly