A subspace blind identification algorithm based on CGM with reduced computational complexity

We propose a subspace blind channel identification algorithm based on CGM (conjugate gradient method). The algorithm estimates (1) the channel order, (2) the noise variance, (3) the noise subspace, and then identifies (4) channel impulse response without using the eigenvalue decomposition. The special features of the proposed algorithm are (1) accurate channel order estimation and (2) the reduction of computational complexity using CGM. Numerical examples show the effectiveness of the proposed algorithm.