Adaptive least squares smoothing for blind channel estimation and tracking

A least squares smoothing (LSS) approach is presented for the blind estimation of single-input multiple-output finite impulse response systems. By exploiting the isomorphic relation between the input and output subspaces, this geometrical approach identifies the channel from the least squares smoothing error of the channel output. Based on this approach, an adaptive least squares smoothing (A-LSS) algorithm is proposed. Compared with subspace and linear prediction-based algorithms, A-LSS gains an advantage for its high convergence rate, adaptivity to both channel order and channel parameter variation, low complexity with no matrix operations, and modular structure suitable for VLSI implementation.