Abstract
In this paper, we propose a three-dimensional (3D) beamforming scheme for the massive multiple-input multiple-output (MIMO) system where the base station (BS) employs a uniform rectangular array (URA). In order to avoid the high computational complexity involving large-dimensional channel matrices, a two-stage beamforming method is applied where the second-stage beamforming is a Kronecker product of azimuth and elevation discrete Fourier transform (DFT) beamforming. These DFT prebeamformers are used for cell splitting and form effective channels with lower dimension for first-stage precoding. We develop a low-complexity user grouping algorithm based on the statistical channel state information at the transmitter (CSIT) to partition users. Each group of users is served by the signal-to-leakage-and-noise ratio (SLNR) precoding aiming at suppressing the intra-group and adjacent-group interferences, which is a good balance between performance and complexity. We derive the approximate signal-to-interference-plus-noise ratio (SINR) of our proposed scheme. Numerical results validate that the SINR approximations are tight and indicate the significance of the proposed 3D beamforming scheme.
Highlights
In order to meet the demand of explosively increasing data services, the massive multiple-input multiple-output (MIMO) system has emerged as a promising technology for the generation of cellular systems [1,2,3]
We apply the discrete Fourier transform (DFT) beamforming as the azimuth and elevation prebeamformers, and their Kronecker product constructs the 3D prebeamformer. These DFT prebeamformers are used for cell splitting, and all groups are all working in the same time-frequency resource
The proposed scheme is a good balance between performance and complexity
Summary
In order to meet the demand of explosively increasing data services, the massive multiple-input multiple-output (MIMO) system has emerged as a promising technology for the generation of cellular systems [1,2,3]. Considering the one-ring scattering model, the azimuth and elevation correlations are characterized by Toeplitz matrices, and the eigenvector matrices of these Toeplitz matrices are approximated by submatrices of DFT matrix when the number of antennas is large [19, 20]. Numerical results show that the SINR approximations are tight when the number of BS antennas is large and validate the effectiveness of the proposed lowcomplexity user grouping algorithm. Since the eigenvalue distributions and the eigenvectors of 3D channel correlation and the Kronecker product of azimuth and elevation correlations are close, we can model the channel correlation for the kth user as [17, 18].
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