Performance comparison of least squares and least mean squares algorithms as HF channel estimators

In this paper a comparison is made between the convergence and tracking properties of Least Squares (LS) and Least Mean Squares (LMS) algorithms as high frequency (HF) channel estimators. Theoretical results are derived for the asymptotic error achieved by the LS algorithms under white-input conditions in the HF channel. This result is more accurate than previous analyses of LS algorithms in a nonstationary enviroment [5,8,9]. Utilising a state space definition of the channel model a minimum variance Kalman estimator is derived using the a-priori knowledge of the parameters which define the Markov process.