In this paper, we propose a minimum-meansquared-error (MMSE)-based lattice-reduction (LR)-Aided fixedcomplexity sphere decoder (FSD) for low-complexity nearmaximum-likelihood (near-ML) multiple-input multiple-output (MIMO) detection. In order for the FSD to achieve optimal performance, the number of full expansion (FE) stages should be sufficient, which is the major cause of the increase in the computational complexity when either a large signal constellation or a large number of antennas are adopted. However, the proposed algorithm maintains the near-ML performance with the aid of the MMSE-based LR algorithm while reducing the number of FE stages. Although there exists the increase in the computational complexity for the application of the additional processing elements, the decrease in the number of FE stages results in the lower computational complexity of the overall algorithm. The numerical analysis demonstrates that there is a considerable decrease in the computational complexity while the performance degradation is negligible, compared to the optimal FSD.