Deterministic Channel‐parameter Estimation

This chapter introduces some widely adopted spectral‐based and parametric algorithms used for estimating channel parameters from measurement data. It describes the Bartlett beamforming method, which is a common spectral‐based method for estimating channel power spectra. The chapter presents a subspace‐based spectral method, and the multiple signal identification classification (MUSIC) algorithm. It describes the principle of two subspace‐based parametric methods: the estimation of signal parameters via rotational invariance technique (ESPRIT) and the propagator method. The chapter elaborates the maximum‐likelihood estimation method and describes the expectation‐maximization (EM) and the space‐alternating generalized EM (SAGE) algorithms used for channel parameter estimation. It reviews the Richter's maximum‐likelihood estimation method (RiMAX) and presents techniques relying on the multi‐layer evidence framework using Bayes' theorem. The chapter describes the implementation of the extended Kalman filter (EKF) and modified particle‐filter‐based technique. This technique is more readily applicable than the EKF for tracking channel variations with low complexity.