Maximum-Likelihood Precoder Selection for ML Detector in MIMO-OFDM Systems

Abstract

Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.