Suboptimal Maximum Likelihood Detection Using Gradient-based Algorithm for MIMO Channels

This paper proposes a suboptimal maximum likelihood detection (MLD) algorithm for multiple-input multiple-output (MIMO) communications. The proposed algorithm regards transmitted signals as continuous variables in the same way as a common method for the discrete optimization problem, and then searches candidates of the transmitted signals in the direction of a modified gradient vector of the metric. The vector enhances components in the gradient that are likely to cause the noise enhancement from which the zero-forcing (ZF) or minimum mean square error (MMSE) algorithms suffer. This method sets the initial guess to the solution by the ZF or MMSE algorithms, which can be recursively calculated. Also, the proposed algorithm requires the same complexity order as that of the ZF algorithm. Computer simulations demonstrate that it is superior in BER performance to conventional suboptimal algorithms of which complexity order is equal to that of ZF