Spatial Process Simulation

Kroese, Dirk P.; Botev, Zdravko I.

doi:10.1007/978-3-319-10064-7_12

Cited by 89 publications

(78 citation statements)

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“…The speed of our algorithm in practice heavily depends on how quickly the underlying Gaussian random fields can be generated. In our one-dimensional case, we generate the Gaussian processes with the recent Matlab implementation by Kroese and Botev [13]. Theoretically, the computational effort needed to generate a sample from the underlying Gaussian process is independent of α in this implementation.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Exact simulation of Brown-Resnick random fields at a finite number of locations

2015

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Section: Numerical Experimentsmentioning

confidence: 99%

Exact simulation of Brown-Resnick random fields at a finite number of locations

2015

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“…As mentioned in Section II, the link shadowing losses are calculated deterministically from the underlying shadowing map. We first generate shadowing maps with the covariance given in (3) using the Circulant Embedding method [15]. Link shadowing losses are then calculated from the pre-generated maps using (4).…”

Section: Implementation In Ns-3mentioning

confidence: 99%

Effects of Correlated Shadowing Modeling on Performance Evaluation of Wireless Sensor Networks

Lu¹,

May

Haines³

2015

2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall)

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Abstract-Wireless links are typically modeled in isolation as independent and parallel links. However, in reality, there is often a correlation in defects and impairments between collocated links. The spatial correlation of shadowing among proximate wireless links has been frequently observed. Although different types of correlated shadowing models have been proposed for wireless channels, these models are not present in popular network simulators. Ignoring such correlations results in the diversity of adjacent links being over-estimated. Our hypothesis is that this could lead to incorrect results when evaluating the performance of wireless systems and network protocols. In this paper, the ns-3 simulation environment has been enhanced with a prototype correlated shadowing model developed in our previously published work. We study the end-to-end packet delivery ratio and delay in wireless sensor networks using both correlated and independent propagation models. Simulation results show that correlated and independent shadowing models produce significant differences in network performance. Moreover, we compare the complexity of different modeling options. It is shown that this correlated propagation model in ns-3 can improve simulation realism without inducing any considerable degradation of scalability.

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“…However, for more realizations of the Gaussian random field, we can store the results of the square root of the BCM and repeat the final step only. For further details on how to implement the Circulant Embedding method, the reader is referred to [14].…”

Section: A Circulant Embedding Methodsmentioning

confidence: 99%