论文标题
大规模MU-MIMO-OFDM下行链路的频域数字预期
Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
论文作者
论文摘要
数字预性（DPD）是一种用于补偿功率放大器（PAS）非线性效应的方法。但是，大多数DPD算法的计算复杂性成为大型多用户（MU）多输入多输入（MIMO）正交频施加多路复用（OFDM）的下行链路中的一个问题，在基本站（BS）中最多需要几百个PAS（BS）需要线性化。在本文中，我们提出了频域中基于卷积的神经网络（CNN）的DPD，发生在预编码之前，其中信号空间的维度取决于用户的数量，而不是BS天线的数量。基于广义的内存多项式（GMP）PAS的仿真结果表明，随着BS天线的数量增加，拟议的基于CNN的DPD会导致非常大的复杂性节省，而牺牲了实现相同符号误差率（SER）的功率较小。
Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).
