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The maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD) has been proposed as a near-optimal multiple-input multiple-output (MIMO) detection scheme which can achieve near maximum likelihood (ML) performance with complexity greatly reduced. However, the performance of the QRM-MLD algorithm will deteriorate with the increasing of the number of transmit antennas, as well as the decreasing of the number of the selected nodes in each stage due to the abandoning of some hypotheses in each step of the tree search. To solve this problem, we modify the QR decomposition method through rearranging the channel columns based on the post-detection signal-to-noise ratio (SNR), which makes the process of the abandoning more reliable. Simulation results show that the new proposed algorithms achieve better performance compared with the conventional QRM-MLD algorithm, especially when the number of transmit antennas is large. Additionally, the increase of the complexity is negligible compared with that of the conventional QRM-MLD algorithm.