A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems

In this paper, the random iterative method is introduced to massive multiple-input multiple-output (MIMO) systems for the efficient downlink linear precoding. By adopting the random sampling into the traditional iterative methods, the matrix inversion within the linear precoding schemes can be approximated statistically, which not only achieves a faster exponential convergence with low complexity but also experiences a global convergence without suffering from the various convergence requirements. Specifically, based on the random iterative method, the randomized iterative precoding algorithm (RIPA) is firstly proposed and we show its approximation error decays exponentially and globally along with the number of iterations. Then, with respect to the derived convergence rate, the concept of conditional sampling is introduced, so that further optimization and enhancement are carried out to improve both the convergence and the efficiency of the randomized iterations. After that, based on the equivalent iteration transformation, the modified randomized iterative precoding algorithm (MRIPA) is presented, which achieves a better precoding performance with low-complexity for various scenarios of massive MIMO. Finally, simulation results based on downlink precoding in massive MIMO systems are given to show the system gains of RIPA and MRIPA in terms of performance and complexity.

Automated summary

This paper introduces the random iterative method to massive MIMO systems for efficient downlink linear precoding. By incorporating random sampling into traditional iterative methods, the matrix inversion in linear precoding schemes can be statistically approximated, resulting in faster convergence with low complexity and global convergence without convergence requirements. The paper proposes the randomized iterative precoding algorithm (RIPA) and shows that its approximation error decays exponentially and globally with the number of iterations. Furthermore, the concept of conditional sampling is introduced to optimize and enhance the convergence and efficiency of the randomized iterations. The modified randomized iterative precoding algorithm (MRIPA) is then presented based on the equivalent iteration transformation, achieving better precoding performance with low complexity for various scenarios of massive MIMO. Simulation results demonstrate the system gains of RIPA and MRIPA in terms of performance and complexity in downlink precoding for massive MIMO systems.