Low-Complexity PAPR-Aware Precoding for Massive MIMO-OFDM Downlink Systems
Lei Hua, Yajun Wang, Zhuxian Lian, Yinjie Su, Zhibin Xie
Abstract
We address the issue of reducing the peak to average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM)-based massive multi-user (MU) multiple-input multiple-output (MIMO) downlink systems. Taking advantage of the massive degrees-of-freedom available in large-scale MIMO antenna arrays, we tackle the PAPR-aware precoding, which formulates MU precoding, OFDM modulation, and PAPR reduction into a convex optimization problem. Then the accelerated proximal gradient algorithm (APGM) is developed to solve the above optimization problems. The numerical results indicate that the proposed APGM algorithm has comparable advantages over the existing method in terms of PAPR reduction, symbol error rate (SER), and computational complexity.