Selective detection with adaptive channel estimation for MIMO OFDM

In this paper, we investigate the performance of a new selective detection algorithm that is a modification of that proposed in [1]. The channel estimation used is based on adaptive model-based regularization in Multi Input Multi Output (MIMO) OFDM systems. The Basis Expansion Model (BEM) approach is employed for channel estimation. For the adaptive regularization, regularization matrices are computed for a set of uniform power delay profiles. The generalized cross-validation method is then used to select a best matrix from the precomputed set. We compare the performance of the detector implementing the channel estimation with adaptive regularization with the performance of the detector using the Linear Minimum Mean Square Error (LMMSE) channel estimation.