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In this paper, a multi-channel cognitive radio network is considered in which both primary users (PUs) and secondary users (SUs) can sense and make use of idle channels, i.e. both kinds of users are cognitive. We study a situation where a PU, while transmitting, can sense idle channels with a better transmission rate than the one currently occupied and can switch to such a better channel. This switching behaviour affects the PU activity profile on the channels and hence also the SU throughput, since SU packet transmissions are confined to the gaps left by the PUs. Different PU switching strategies are studied by developing a continuous-time Markov chain (CTMC) for each strategy. Performance measures include the mean sojourn time of PUs as well as the saturation throughput and interruption probability of SU packets. Some specific considered scenarios quantitatively demonstrate the influence of the PU switching speed. An increased PU switching rate considerably reduces PU sojourn time, at the cost of reducing the SU throughput. [ABSTRACT FROM PUBLISHER]

We are interested in investigating the spread of contagion in a network, G, which describes the interactions between the agents in the system. The topology of this network is often neglected due to the assumption that each agent is connected with every other agents; this means that the network topology is a complete graph. While this allows for certain simplifications in the analysis, we fail to gain insight on the diffusion process for non-complete network topology. In this paper, we offer a continuous-time Markov chain infection model that explicitly accounts for the network topology, be it complete or non-complete. Although we characterize our process using parameters from epidemiology, our approach can be applied to many application domains. We will show how to generate the infinitesimal matrix that describes the evolution of this process for any topology. We also develop a general methodology to solve for the equilibrium distribution by considering symmetries in G. Our results show that network topologies have dramatic effect on the spread of infections. [ABSTRACT FROM PUBLISHER]