Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case.
Abstract. In this paper, a multivariate Markovian traffic model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios.
Abstract-We establish a statistical framework for investigating the influence of correlated random displacements of antenna elements in a uniform circular antenna array (UCA) on the distribution of direction-of-arrival (DOA) estimates. More specifically, we apply a stochastic collocation method for modeling the sparse UCA root-MUSIC-DOA estimates as polynomial expansions of the random displacements. Compared to Monte-Carlo simulations, this approach yields a speedup of about 40 for the case of a displacement of two antenna elements.
In this paper, we study the loss and delay of data bursts in an optical buffer. We assume that this buffer consists of a number of fiber delay lines (FDLs). In order to guarantee quality of service (QoS) differentiation in such a buffer, we investigate analytically an offset-time based scheduling mechanism. We consider a system with C QoS classes, where the high-priority QoS classes have a larger offset time than the low-priority QoS classes. For this system, we calculate the total burst loss probability and the burst loss probability within each QoS class. Furthermore, we study the delay of an arbitrary arriving data burst, as well as the delay of an arriving data burst of a certain QoS class.
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