Abstract
We introduce a joint weighted Neumann series (WNS) and Gauss–Seidel (GS) approach to implement an approximated linear minimum mean-squared error (LMMSE) detector for uplink massive multiple-input multiple-output (M-MIMO) systems. We first propose to initialize the GS iteration by a WNS method, which produces a closer-to-LMMSE initial solution than the conventional zero vector and diagonal-matrix based scheme. Then the GS algorithm is applied to implement an approximated LMMSE detection iteratively. Furthermore, based on the WNS, we devise a low-complexity approximate log-likelihood ratios (LLRs) computation method whose performance loss is negligible compared with the exact method. Numerical results illustrate that the proposed joint WNS-GS approach outperforms the conventional method and achieves near-LMMSE performance with significantly lower computational complexity.
Highlights
Massive MIMO (M-MIMO) systems can significantly improve the link reliability and spectral efficiency compared to the small-scale MIMO systems
Since W and the matched-filter output yMF are all required by the conventional linear minimum meansquared error (LMMSE) algorithm and the proposed joint weighted Neumann series (WNS)-GS method, we focus on the complexity of likelihood ratios (LLRs) computation
We proposed to compute approximate LLR based on the WNS approach with negligible performance degradation
Summary
Massive MIMO (M-MIMO) systems can significantly improve the link reliability and spectral efficiency compared to the small-scale MIMO systems. It is highly desirable to design low-complexity high-performance detectors for practical “not-so-massive systems” with a low base station (BS)-to-user-antenna ratio (BUAR). The maximum-likelihood (ML) detector can achieve optimal performance, its complexity increases exponentially with the number of users, which makes it unaffordable for M-MIMO systems with large number of users. The LMMSE detector is shown to achieve near-optimal error-rate performance for M-MIMO systems with a large BUAR [1]. The associated matrix-inversion entails high computational complexity for practical implementation with a large number of users. The performance of the NS method approaches that of the LMMSE for a M-MIMO system with a large BUAR. We propose a joint weighted Neumann series (WNS) and Gauss-Seidel approach to implement the LMMSE detection without matrix inversion.
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