One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized. In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied. In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations. In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations. / <p>QC 20170908</p>

Complex networks have been used to represent the fundamental structure of a multitude of complex systems from various fields. In the network representation, the system is reduced to a set of nodes and links that denote the elements of the system and the connections between them respectively. Complex networks are commonly adaptive such that the structure of the network and the states of nodes evolve dynamically in a coupled fashion. Adaptive networks lead to peculiar complex dynamics and network topologies, which can be investigated by moment-closure approximations, a coarse-graining approach that enables the use of the dynamical systems theory. In this thesis, I study several contact processes in adaptive networks that are defined by the transmission of node states. Employing moment-closure approximations, I establish analytical insights into complex phenomena emerging in these systems. I provide a detailed analysis of existing alternative moment-closure approximation schemes and extend them in several directions. Most importantly, I consider developing analytical approaches for models with complex update rules and networks with complex topologies. I discuss four different contact processes in adaptive networks. First, I explore the effect of cyclic dominance in opinion formation. For this, I propose an adaptive network model: the adaptive rock-paper-scissors game. The model displays four different dynamical phases (stationary, oscillatory, consensus, and fragmented) with distinct topological and dynamical properties. I use a simple moment-closure approximation to explain the transitions between these phases. Second, I use the adaptive voter model of opinion formation as a benchmark model to test and compare the performances of major moment-closure approximation schemes in the literature. I provide an in-depth analysis that leads to a heightened understanding of the capabilities of alternative approaches. I demonstrate that, even for the simple adaptive voter model, highly sophisticated approximations can fail due to special dynamic correlations. As a general strategy for targeting such problematic cases, I identify and illustrate the design of new approximation schemes specific to the complex phenomena under investigation. Third, I study the collective motion in mobile animal groups, using the conceptual framework of adaptive networks of opinion formation. I focus on the role of information in consensus decision-making in populations consisting of individuals that have conflicting interests. Employing a moment-closure approximation, I predict that uninformed individuals promote democratic consensus in the population, i.e. the collective decision is made according to plurality. This prediction is confirmed in a fish school experiment, constituting the first example of direct verification for the predictions of adaptive network models. Fourth, I consider a challenging problem for moment-closure approximations: growing adaptive networks with strongly heterogeneous degree distributions. In order to capture the dynamics of such networks, I develop a new approximation scheme, from which analytical results can be obtained by a special coarse-graining procedure. I apply this analytical approach to an epidemics problem, the spreading of a fatal disease on a growing population. I show that, although the degree distribution has a finite variance at any finite infectiousness, the model lacks an epidemic threshold, which is a genuine adaptive network effect. Diseases with very low infectiousness can thus persist and prevail in growing populations.

Complex networks have been used to represent the fundamental structure of a multitude of complex systems from various fields. In the network representation, the system is reduced to a set of nodes and links that denote the elements of the system and the connections between them respectively. Complex networks are commonly adaptive such that the structure of the network and the states of nodes evolve dynamically in a coupled fashion. Adaptive networks lead to peculiar complex dynamics and network topologies, which can be investigated by moment-closure approximations, a coarse-graining approach that enables the use of the dynamical systems theory. In this thesis, I study several contact processes in adaptive networks that are defined by the transmission of node states. Employing moment-closure approximations, I establish analytical insights into complex phenomena emerging in these systems. I provide a detailed analysis of existing alternative moment-closure approximation schemes and extend them in several directions. Most importantly, I consider developing analytical approaches for models with complex update rules and networks with complex topologies. I discuss four different contact processes in adaptive networks. First, I explore the effect of cyclic dominance in opinion formation. For this, I propose an adaptive network model: the adaptive rock-paper-scissors game. The model displays four different dynamical phases (stationary, oscillatory, consensus, and fragmented) with distinct topological and dynamical properties. I use a simple moment-closure approximation to explain the transitions between these phases. Second, I use the adaptive voter model of opinion formation as a benchmark model to test and compare the performances of major moment-closure approximation schemes in the literature. I provide an in-depth analysis that leads to a heightened understanding of the capabilities of alternative approaches. I demonstrate that, even for the simple adaptive voter model, highly sophisticated approximations can fail due to special dynamic correlations. As a general strategy for targeting such problematic cases, I identify and illustrate the design of new approximation schemes specific to the complex phenomena under investigation. Third, I study the collective motion in mobile animal groups, using the conceptual framework of adaptive networks of opinion formation. I focus on the role of information in consensus decision-making in populations consisting of individuals that have conflicting interests. Employing a moment-closure approximation, I predict that uninformed individuals promote democratic consensus in the population, i.e. the collective decision is made according to plurality. This prediction is confirmed in a fish school experiment, constituting the first example of direct verification for the predictions of adaptive network models. Fourth, I consider a challenging problem for moment-closure approximations: growing adaptive networks with strongly heterogeneous degree distributions. In order to capture the dynamics of such networks, I develop a new approximation scheme, from which analytical results can be obtained by a special coarse-graining procedure. I apply this analytical approach to an epidemics problem, the spreading of a fatal disease on a growing population. I show that, although the degree distribution has a finite variance at any finite infectiousness, the model lacks an epidemic threshold, which is a genuine adaptive network effect. Diseases with very low infectiousness can thus persist and prevail in growing populations.:1. Introduction .................................................................................. 1 2. Moment-closure approximations of complex networks ................. 5 3. Cyclic dominance in adaptive network models of opinion formation .......... 25 4. Performance of moment-closure approximations of adaptive networks .... 35 5. Information and consensus in a fish school ................................. 65 6. Epidemic spreading on growing heterogeneous adaptive networks ......... 83 7. Conclusions ................................................................................. 101 Appendix A: Moment expansion for node update rules ................... 107