Reduced-rank least squares channel estimation

A reduced-rank least squares (RRLS) algorithm based on oversampling the channel output by an integer factor and singular value decomposition (SVD) of a data matrix is shown to have certain advantages over the nonparametric least-squares (LS) and the parametric Evans and Fischl (EF) alternative algorithm for estimating the pulse response of a truncated equivalent baseband communication channel. By sampling the channel output faster than the training symbol rate and applying SVD to the data matrix formed from the observed data, the method is shown to exhibit improved-error performance over existing nonparametric LS methods and the parametric EF iterative algorithm. The RRLS algorithm´s performance has been shown to be somewhat sensitive to model order selection and observation noise statistics. The normalized mean squared error (MSE) performance of the RRLS algorithm is shown to be essentially independent of oversampling factors that are not much greater than the span of the truncated channel. It performs well even in severe noise environments