Subspace-based blind channel estimation : generalization, performance analysis and adaptive implementation
Wei Kang · eScholarship@McGill (McGill) · 2003
In this thesis, we present a systematic study of the subspace-based blind channel estimation method. We first formulate a general signal model of multiple simultaneous signals transmitted through vector channels, which can be applied to a multitude of modern digital communication systems. Based on this model, we then propose a generalized subspace-based channel estimator by minimizing a novel cost function, which incorporates the set of kernel matrices of the signals sharing the target channel via a weighted sum of projection errors. We investigate the asymptotic performance1 of the proposed estimator, i.e. bias, covariance, mean square error (MSE) and Cramer-Rao bound, for large numbers of independent observations. We show that the performance of the estimator can be optimized by increasing the number of kernel matrices and by using a special set of weights in the cost function. We also propose a novel adaptive implementation of the generalized subspace channel estimator. The low-complexity and numerical robustness of this adaptive implementation make it suitable for online estimation of time1-varying channels over long observation periods. Finally, we consider the application of the proposed estimator to a down-link CDMA system operating in frequency selective fading channel with negligible ISI. The results of the computer simulations fully support our analytical developments.