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Computationally efficient and scalable models that describe droop-controlled inverter dynamics are key to modeling, analysis, and control in islanded microgrids. Typical models developed from first principles in this domain describe detailed dynamics of the power electronics inverters, as well as the network interactions. Consequently, these models are very involved; they offer limited analytical insights and are computationally expensive when applied to investigate the dynamics of large microgrids with many inverters. This calls for the development of reduced-order models that capture the relevant dynamics of higher order models with a lower dimensional state space while not compromising modeling fidelity. To this end, this paper proposes model-reduction methods based on singular perturbation and Kron reduction to reduce large-signal dynamic models of inverter-based islanded microgrids in temporal and spatial aspects, respectively. The reduced-order models are tested in a modified IEEE 37-bus system and verified to accurately describe the original dynamics with lower computational burden. In addition, we demonstrate that Kron reduction isolates the mutual inverter interactions and the equivalent loads that the inverters have to support in the microgrid-this aspect is leveraged in the systematic selection of droop coefficients to minimize power losses and voltage deviations.

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Dynamic spectrum access (DSA) is a promising approach for the more effective use of existing spectrum. Of fundamental importance to DSA is the need for fast and reliable spectrum sensing over a wide bandwidth. A model for two-stage sensing is described based on an analysis of the mean time to detect an idle channel. Simulation results show that it provides significantly faster idle channel detection than conventional single-stage random searching. 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.

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Abstract-Cognitive radio networks require fast and reliable spectrum sensing to achieve high network utilization by secondary users. Optimization approaches to spectrum sensing todate have largely focused on maximizing throughput for secondary users while considering only a single parameter variable pertinent to sensing -notably the threshold or duration, but not both. In this work, 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 resulting 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 also the aggregate opportunistic throughput of both primary and secondary users), relative to the scenarios with only a single sensing variable or a sub-optimal ad-hoc optimization approach used for two variable case.

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Abstract-Cognitive Radio systems face an important challenge -fast and reliable channel searching to enable secondary users to optimize available spectral resources. We revisit conventional urn models for channel occupancy and analyze the performance of several search schemes in terms of the mean time to detection. In particular, we focus on correlated Markov models for bin occupancy and highlight the performance of n-step serial search (nSS) algorithm Index Terms-Cognitive Radio, channel model, search scheme