Recursive Estimation and Identification of Time-Varying Long-Term Fading Channels

Mohammed M Olama,Seddik M Djouadi,Kiran K Jaladhi,Charalambos D Charalambous

doi:10.1155/2007/17206

Abstract

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.

Highlights

This paper is concerned with the development of timevarying (TV) long-term fading (LTF) wireless channel models based on system identification and estimation algorithms to extract various parameters of the LTF channel using received signal measurements
In the TV models, the statistics of channel are time-varying. This contrasts with the majority of published work that mainly deals with timeinvariant random models or simple free space model, where the channel statistics do not depend on time [4,5,6]
We propose to estimate the TV power path loss of the LTF channel and its parameters from received signal strength data, which are usually available or easy to obtain in any wireless network

Summary

INTRODUCTION

This paper is concerned with the development of timevarying (TV) long-term fading (LTF) wireless channel models based on system identification and estimation algorithms to extract various parameters of the LTF channel using received signal measurements. In time-invariant models, channel parameters are random but do not depend on time, and they remain constant throughout the observation and estimation phase. We propose to estimate the TV power path loss of the LTF channel and its parameters from received signal strength data, which are usually available or easy to obtain in any wireless network.

TV LTF WIRELESS CHANNEL MATHEMATICAL MODEL

WIRELESS CHANNEL ESTIMATION VIA THE EM ALGORITHM AND KALMAN FILTERING

Channel state estimation—The EKF

Channel parameter estimation—The EM algorithm

NUMERICAL RESULTS

CONCLUSION