Sepideh Zarrin

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In this paper, cooperative quickest spectrum sensing for cognitive radios is studied. Various cooperative schemes are considered based on the cumulative sum (CUSUM) algorithm, for different memory and communication constraint scenarios. The optimal CUSUM statistics are derived for each of these cooperative sensing schemes in the noisy channel scenario. In(More)
This paper investigates the problem of cooperative spectrum sensing in cognitive radios with unknown parameters in the likelihood function. We first derive the optimal likelihood ratio test (LRT) statistic based on the Neyman-Pearson (NP) criterion at the fusion center for hard (one-bit), soft (infinite precision) and quantized (multi-bit) local decisions.(More)
In this paper, we present a probabilistic inference approach for cooperative spectrum sensing. We probabilistically model the cooperative sensing system on a representative factor graph, and approach the decision fusion problem as one of probabilistic inference on a factor graph that can be tackled by message passing algorithms like belief propagation. This(More)
In this paper, we maximize the throughput of a cognitive radio (CR) network with respect to the frame length when quickest sensing is used. The amount of interference to the primary network, measured by the probability of collision with primary users (PUs), is constrained. The corresponding problem when CRs use block sensing is also solved, assuming that(More)
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