PERFORMANCE ANALYSES OF OPPORTUNISTIC SPECTRUM ACCESS IN COGNITIVE RADIO NETWORK

[[abstract]]This thesis studies the performance analysis of opportunistic spectrum access (OSA) with imperfect sensing, spectrum backup channels (BCs), and prediction scheme for cognitive radio networks (CRN). The studies have organized in to three works. At first work we study the performance of an opportunistic spectrum access (OSA) system with a general number of channels and imperfect spectrum sensing. The system is modeled as a two-dimensional continuous-time Markov chain. We specify the state-dependent transition rates due to imperfect sensing for the general channel number case by using simple recursive functions. For performance metrics, we consider blocking probability, termination probability and success probability for each type of users. On second work, we study the performance of cognitive radio networks with imperfect spectrum sensing and backup channels (BCs). In case an SU is blocked from PCs, it then checks the BCs to find a free one. The system is modeled as a three-dimensional continuous-time Markov chain. We specify the state-dependent transition rates due to imperfect sensing by simple recursive functions. On the last work we propose a hidden Markov model (HMM) with state prediction for opportunistic spectrum access (OSA) in cognitive radio (CR) networks. In contrast to the traditional scheme relying only on channel sensing for exploring spectrum opportunities, the proposed prediction scheme takes advantage of state prediction, channel sensing, and acknowledgments (ACKs) from the receiver in an attempt to maximize the utility. We impose some constraints on the system parameters and derive thresholds by which we can specify the optimal action. We then conduct simulations to compare the performance of the prediction scheme to that of the traditional scheme. Keyword: Opportunistic Spectrum Access, cognitive radio network, imperfect sensing, backup channel, prediction scheme, recursive function, two dimensional Markov, Three dimensional Markov, hidden Markov model