A Near-Optimal Detection Scheme Based on Joint Steepest Descent and Jacobi Method for Uplink Massive MIMO Systems

TLDR: Simulation results show that the proposed method outperforms Neumann Series, Richardson method, and conjugate gradient based methods, while achieving the near-optimal performance of linear detectors with a small number of iterations.

Abstract: A new approach based on joint steepest descent algorithm and Jacobi iteration is proposed to iteratively realize linear detections for uplink massive multiple-input multiple-output (MIMO) systems. Steepest descent algorithm is employed to obtain an efficient searching direction for the following Jacobi iteration to speed up convergence. Moreover, promising initial estimation and hybrid iteration are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results show that the proposed method outperforms Neumann Series, Richardson method, and conjugate gradient based methods, while achieving the near-optimal performance of linear detectors with a small number of iterations. Meanwhile, the FPGA implementation results demonstrate that our proposed method can achieve high throughput owing to its high parallelism.