00000cab a22000003a 4500 UP-99796217609333257 Buklod 20231007234443.0 m |o d | ta 100616s xx d | ||r ||||| || DENGII eng Turin, W. Hidden Markov modeling of flat fading channels. pp. 1809-1817 Hidden Markov models (HMMs) are a powerful tool for modeling stochastic random processes. They are general enough to model with high accuracy a large variety of processes and are relatively simple allowing us to compute analytically many important parameters of the process which are very difficult to calculate for other models (such as complex Gaussian processes). Another advantage of using HMMs is the existence of powerful algorithms for fitting them to experimental data and approximating other processes. In this paper, we demonstrate that communication channel fading can be accurately modeled by HMMs, and we find closed-form solutions for the probability distribution of fade duration and the number of level crossings HMM. Rayleigh fading. Algorithms. Closed-form solutions. Communication channel fading. Complex Gaussian processes. Experimental data. Fade duration. Flat fading channels. Hidden Markov modeling. High accuracy. Level crossings. Probability distribution. Stochastic random processes. IEEE Journal on selected areas in communications 16, 9 (1998). FO Article