LADMM-Based PAPR-Aware Precoding for Massive MIMO-OFDM Downlink Systems
Lei Hua, Yajun Wang, Zhuxian Lian, Yinjie Su, Zhibin Xie
Abstract
We tackle the peak to average power ratio (PAPR) reduction of an orthogonal frequency division multiplexing (OFDM)-based massive multi-user (MU) multiple-input multiple-output (MIMO) downlink systems in this paper. Utilizing the redundant degree of freedom (DOF) provided by a large number of antennas equipped at the base station, we first formulate OFDM modulation, MU precoding, and PAPR reduction into a non-convex optimization problem. By using the linearized alternating direction method of multipliers (LADMM), we directly address the non-convex PAPR-aware precoding problem without any relaxation in terms of PAPR and multi-user interference (MUI), which is completely different from the existing convex optimization methods based on relaxations. At the same time, the paper is also the first work to directly solve the non-convex PAPR-aware precoding optimization. The experimental results demonstrate that the LADMM method gets the comparable PAPR reduction and symbol error rate (SER), faster convergence, and lower complexity than the existing relaxed methods.