Statistical Channel‐parameter Estimation
Xuefeng Yin, Xiang Cheng · 2016
Stochastic channel models are usually obtained by first estimating the channel's parameters, such as the composite spreading parameters-the delay spread, Doppler frequency spread, and the parameters of individual propagation paths from individual channel snapshots-and then secondly, extracting the statistics of the channel parameters for modeling. This chapter describes some recently developed parameter estimation algorithms, which can be used to estimate directly the statistical channel parameters from the measurement data. The estimators based on a linear approximation model include those derived using the two-specular-scatterer model the two-SDSs model, and the GAM model. The chapter discusses the first-order GAM model and derive estimators of its parameters using standard deterministic and stochastic maximum-likelihood (ML) methods, as well as a novel MUSIC algorithm. It presents generic power spectral density (PSD) model was proposed for characterization of the six-dimensional shape of the bidirection delay and Doppler frequency PSD of individual components in the response of a propagation channel.