Title :
Stochastic Maximum-Likelihood Method for MIMO Propagation Parameter Estimation
Author :
Ribeiro, áCássio B. ; Ollila, Esa ; Koivunen, Visa
Author_Institution :
Signal Process. Lab., Fed. Univ. of Rio de Janeiro
Abstract :
In this paper, we derive a stochastic maximum-likelihood (ML) method for estimating spatio-temporal parameters for multiple-input multiple-output (MIMO) channels. Such estimators are needed in propagation studies where extensive channel measurements and sounding are required. These are seminal tasks in the process of developing advanced channel models. The proposed method employs an angular von Mises distribution model which is appropriate for angular data observed in channel measurement campaigns. The signal model is stochastic, and consequentially the method is particularly useful for estimation of the diffuse scattering component. This approach leads to lower complexity and faster convergence in comparison to deterministic models. These benefits are due to lower dimensionality of the model, leading to a simpler optimization problem. The statistical performance of the estimator is studied by establishing the Crameacuter-Rao lower bound (CRLB) and comparing the variances. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size. The estimator is robust in the sense that meaningful results are obtained when applied to data generated by channel models other than the one used in its derivation
Keywords :
MIMO communication; channel estimation; maximum likelihood estimation; radiowave propagation; stochastic processes; Cramer-Rao lower bound; MIMO propagation; angular von Mises distribution; channel measurements; diffuse scattering component; multiple-input multiple-output channels; parameter estimation; stochastic maximum-likelihood method; Acoustic propagation; Acoustic scattering; Convergence; Laboratories; MIMO; Maximum likelihood estimation; Multidimensional signal processing; Parameter estimation; Power system modeling; Stochastic processes; Channel sounding; parameter estimation;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2006.882057