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Wireless communication networks are characterized by nodes and scatters mobility which make the propagation environment time-varying and subject to fading. These variations are captured by random time-varying impulse responses. The latter are fairly general finite energy functions of both time and space that cannot be specified by a finite number of parameters. In this letter, we show that the impulse responses can be approximated in a mean square sense as close as desired by impulse responses that can be realized by stochastic differential equations (SDEs). The behaviors of the SDEs are characterized by small finite dimensional parameter sets. [ABSTRACT FROM AUTHOR]

This paper is concerned with modeling of time-varying wireless long-term fading channels, parameter estimation, and identification from received signal strength data. Wireless channels are represented by stochastic differential equations, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal strength data. These algorithms estimate the channel path loss and identify the channel parameters recursively. Numerical results showing the viability of the proposed channel estimation and identification algorithms are presented. [ABSTRACT FROM AUTHOR]