Sparse approximations of the LMMSE channel estimation in OFDM with transmitter diversity

The linear minimum mean-squared error (LMMSE) channel estimation for orthogonal frequency-division multiplexing (OFDM) systems requires a large number of complex multiplications. We evaluate a simplified LMMSE channel estimation algorithm in a transmit diversity environment by applying a significant weight catching (SWC) technique to the LMMSE fixed weighting matrix. The SWC technique itself is based on modifying the smoothing matrix by leaving the Gamma largest values in each row and turning the rest to zeros. This allows the computational complexity of the full LMMSE processor to be reduced by more than 50%. In the well known LMMSE by singular value decomposition (SVD) technique the sparse approximation is accomplished by zeroing out all but the r largest singular values. LMMSE by SVD is the preferred approximation technique for low delay spread channels. However, in channels with large delay spreads, LMMSE by SWC is a better choice in terms of computational complexity and estimation accuracy.