Estimation and identification of time-varying long-term fading channels via the particle filter and the EM algorithm

Xiao Ma, M. Olama, S. Djouadi, C. Charalambous
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引用次数: 2

Abstract

In this paper, we are concerned with the estimation and identification of time-varying wireless long-term fading channels. The dynamics of the fading channels are captured using a mean-reverting linear stochastic differential equation driven by a Brownian motion. Recursive estimation and identification algorithms solely from received signal strength data are developed. These algorithms are based on combining the particle filter (PF) with the expectation maximization (EM) algorithm that estimate and identify the power path-loss of the channel and its parameters, respectively. Numerical results are provided to evaluate the accuracy of the proposed algorithms.
基于粒子滤波和EM算法的时变长期衰落信道估计与识别
本文主要研究时变无线长期衰落信道的估计与识别问题。衰落信道的动态被捕获使用均值回归线性随机微分方程由布朗运动驱动。开发了仅根据接收信号强度数据的递归估计和识别算法。这些算法基于粒子滤波(PF)和期望最大化(EM)算法的结合,分别估计和识别信道的功率路径损耗及其参数。数值结果验证了所提算法的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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