Performance of MIMO Schemes with Channel Estimation Errors

In this paper, we investigate the optimum detection of MIMO signals in the presence of channel estimation errors. A maximum likelihood (ML)-based detection algorithm is proposed for optimal MIMO signal detection based on the modified ML criterion which takes into account channel estimation errors. Moreover, we evaluate for both the uncoded and coded case, the bit-error-rate (BER) performance of the hard decoded MIMO systems. The new algorithm, taking into account channel estimation errors, achieves improvement in terms of BER gain over the conventional MIMO detector algorithm ignoring channel estimation errors. Moreover, the optimal designed constellations taking channel estimation errors into account, show an increase in the capacity over conventional schemes.