Abstract
In uplink massive multiple-input multiple-output (MIMO) systems, the conventional minimum mean square error-interference rejection combining (MMSE-IRC) signal detection algorithm needs to compute the inverse of the interference and noise covariance matrix, which incurs high computational complexity, especially when the number of antennas is large. A low-complexity MMSE-IRC signal detection algorithm based on the eigenvalue decomposition of the interference and noise covariance matrix is proposed. The proposed algorithm exploits a dimension-reduction technique to reduce the computation-intensive of the matrix inversion compared with the conventional algorithm. Meanwhile, the proposed algorithm is shown to be equivalent to the conventional MMSE-IRC algorithm under the assumption of uncorrelated interference and noise. Analysis and simulation results show the effectiveness of the proposed algorithm.
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