A Low-Complexity Precoding Method Based on the Steepest Descent Algorithm for Downlink Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the key technologies for the fifth generation (5G) due to its high throughput and spectral efficiency. However, the large-size antenna configurations in massive MIMO systems incur significantly high complexity for the conventional linear precoding schemes like minimum mean square error (MMSE) due to the associated high-dimensional matrix inversion operation. To solve the issue, we propose to utilize the steepest descent (SD) algorithm to realize the MMSE precoding operation deprived of the complex matrix inversion. Furthermore, we introduce a weighted-step approach, named weighted SD (WSD), to speed up the convergence process. The convergence of the proposed WSD-based approach is analyzed in this work. Numerical results illustrate that the WSD-based approach outperforms the Neumann-series (NS) based one in terms of the convergence speed and obtains nearly the same performance of the classical MMSE based one with significantly reduced computational complexity.