Abstract

In this paper, we present an efficient quantization scheme for lattice-reduction (LR) aided (LRA) MIMO detection using Gram-Schmidt orthogonalization. For the LRA detection, the quantization step applies the simple rounding operation, which often leads to the quantization errors. Meanwhile, these errors may result in the detection errors. Hence, the motivation of the proposed detection is to further solve the problem of degrading the performance due to the quantization errors in the signal estimation. In this paper, the proposed quantization scheme decreases the quantization errors using a simple tree search with a threshold function. Through the analysis and the simulation results, the proposed detection can achieve the near-ML performance with only a little additional complexity.

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