Modeling and Optimization of Dynamic Spectrum Access

Dynamic spectrum access (DSA) is a promising approach for the more effective use of existing spectrum. Of fundamental importance to current research is the need for modeling and optimization of DSA.;Firstly, the conventional urn model for channel availability (the random (i.i.d) model) is revisited and a correlated Markov model which is apropos for scenarios with memory is introduced. An n-step serial search strategy is proposed and the performance of random and serial search schemes is investigated. We then highlight a key trade-off underlying the overall mean time to detect a free channel: it is a function of both the mean number of steps and the sensing time per step. The minimized overall mean detection time is analytically investigated for low SNR and verified by simulation results under various SNR environments.;Secondly, a model for two-stage sensing technique is described based on an analysis of the average detection time. Simulation results show that it provides significantly faster idle channel detection than conventional single-stage searching schemes. Several system-level issues are also investigated including the settling time of the phase-locked loop (PLL) in the frequency synthesizer, which determines the channel switching time. Effects of the bandwidth of the coarse sensing block and the integration duration of the energy detector are also presented in this research.;Other than conventional spectrum sensing research in homogeneous network model, we propose a novel queuing model for sensing-based dynamic spectrum access (DSA) in two heterogeneous scenarios: (a) between a primary and a secondary system and (b) coexisting secondary systems. To date, there has been little research conducted on queueing theoretic modeling of such heterogeneous systems that provide a quantitative estimate of the benefits from DSA, which has the potential of vastly improving the spectrum utilization. Our model incorporates service rate heterogeneity and bandwidth heterogeneity while also considering preemptive priority for the primary system. We present a continuous time Markov chain (CTMC) model for performance analysis of DSA between a primary and a secondary system with various priority classes and bandwidth requirements in single and multiple channel scenarios. Closed form results for spectrum utilization, blocking probability, and optimal traffic intensities are then derived. Further, we propose a channel packing scheme (CPS), which packs smaller bandwidth users into clusters of adjacent channels to alleviate blockage of users with larger bandwidth requirements. Comparative studies are performed between CPS and conventional schemes with respect to spectrum utilization for (multiple) heterogeneous secondary systems. Numerical results show that the co-located heterogeneous systems call benefit from CPS in terms of blocking probability and spectrum utilization.;Unlike traditional optimization research on spectrum sensing by considering single parameter, we investigate the impact of true joint minimization under two performance criteria: (a) minimization of the average time to detection of a spectrum hole and (b) joint maximization of the aggregate opportunistic throughput. We show that the resulting non-convex problem is actually biconvex under practical conditions for which effective algorithms can be developed that yields reliable numerical procedures to solve the optimization problem. The results show that the proposed approach can considerably improve system performance (in terms of the mean time to detect a spectrum hole and the aggregate opportunistic throughput of both primary and secondary users), relative to the scenarios with only a single sensing variable or a sub-optimal optimization approach used for two variable case.