An extension of clarke's model with stochastic amplitude flip processes
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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AbstractStochastic modeling is an essential tool for studying statistical properties of wireless channels. In multipath fading channel (MFC) models, the signal reception is modeled by a sum of wave path contributions, and Clarke's model is an important example of such which has been widely accepted in many wireless applications. However, since Clarke's model is temporally deterministic, Feng and Field noted that it does not model real wireless channels with time-varying randomness well. Here, we extend Clarke's model to a novel time-varying stochastic MFC model with scatterers randomly flipping on and off. Statistical properties of the MFC model are analyzed and shown to fit well with real signal measurements, and a limit Gaussian process is derived from the model when the number of active wave paths tends to infinity. A second focus of this work is a comparison study of the error and computational cost of generating signal realizations from the MFC model and from its limit Gaussian process. By rigorous analysis and numerical studies, we show that in many settings, signal realizations are generated more efficiently by Gaussian process algorithms than by the MFC model's algorithm. Numerical examples that strengthen these observations are also presented. © 2014 IEEE.
SponsorsThis work was supported in part by the Center for Industrial and Applied Mathematics at the Royal Institute of Technology (KTH) and in part by the King Abdullah University of Science and Technology Strategic Research Initiative Center for Uncertainty Quantification in Computational Science. The associate editor coordinating the review of this paper and approving it for publication was O. Oyman.