MLD with QR Decomposition and M-algorithm (QRM-MLD) [8]-[16]. To looking for the suboptimal detection algorithm with the near optimal performance and the affordable complexity

 Abstract—This paper proposes new adaptive tree search detection with variable path expansion based on Gram-Schmidt (GS) orthogonalization (GSO) in MIMO systems. We adopt the GSO procedure to reduce the channel matrix instead of the QR-decomposition in the conventional QRM-MLD. This detection scheme combined the GSO reduction with the M-algorithm, what we call GSM-MLD, can achieve near-ML performance as the conventional QRM-MLD. The proposed detection method is a breadth-first algorithm and performs the adaptive tree search with variable path expansion in the GSM-MLD. In this paper, we introduce a path metric ratio function to evaluate the reliability for all the survived branches. The survived but lower reliable branches adopt parts of the constellation points as the candidates into the next detection layer. The proposed detection algorithm reduces the complexity by adaptively decreasing the computation of the path metric for the low reliable candidates. The numerical results exhibit that the proposed scheme achieves near-ML performance with relatively lower complexity compared to the conventional QRM-MLD.