Abstract
This paper proposes a suboptimal algorithm for the maximum likelihood detection (MLD) in 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 for candidates of the transmitted signals in the direction of a modified gradient vector of the metric. The vector is almost proportional to the direction of the noise enhancement, from which zero-forcing (ZF) or minimum mean square error (MMSE) algorithms suffer. This method sets the initial guess to the solution by ZF or MMSE algorithms, which can be recursively calculated. Also, the proposed algorithm has the same complexity order as that of conventional suboptimal algorithms. Computer simulations demonstrate that it is much superior in BER performance to the conventional ones.
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