SGA based symbol detection and EM channel estimation for MIMO systems

This paper investigates iterative channel estimation and symbol detection for spatial multiplexing multiple input multiple output (MIMO) systems with frequency flat block fading channels using the expectation-maximization (EM) algorithm. The maximum likelihood (ML) estimation of the MIMO channels via the EM algorithm requires the computation of the posterior mean and covariance of transmit symbol vectors which involve an exhaustive search of all possible symbol combinations and are computationally prohibitive for large systems. However, most of the symbol combinations contribute very little to the estimation. Therefore, we suggest that sequential Gaussian approximation (SGA) algorithm can be used to identify the M most significant symbol combinations and we can approximate the mean and covariance based on those symbol combinations. Simulation results are provided to illustrate the proposed algorithm.