论文标题
在非线性PA失真下，对节能大规模MIMO系统的无线性PA失真中的线性预系会进行自我监督的学习
Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems
论文作者
论文摘要
大量多重输入多重输出（MIMO）系统通常是在线性功率放大器（PAS）的假设下设计的。但是，在接近其饱和点时，PAS通常是最节能的，在该点它们会引起非线性失真。此外，当使用常规的预码器时，这种失真在用户位置相干结合，从而限制了性能。因此，在设计能节能的大型MIMO系统时，必须对此失真进行管理。在这项工作中，我们建议使用神经网络（NN）学习通道矩阵和预编码矩阵之间的映射，该矩阵在存在这种非线性失真的情况下最大化了总和率。这是针对单个和多用户案例的三阶多项式PA模型完成的。通过学习此映射，与常规的预编码器相比，在饱和度方案中，与传统的预编码器（DPD）相比，能源效率的显着提高可以实现。
Massive multiple input multiple output (MIMO) systems are typically designed under the assumption of linear power amplifiers (PAs). However, PAs are typically most energy-efficient when operating close to their saturation point, where they cause non-linear distortion. Moreover, when using conventional precoders, this distortion coherently combines at the user locations, limiting performance. As such, when designing an energy-efficient massive MIMO system, this distortion has to be managed. In this work, we propose the use of a neural network (NN) to learn the mapping between the channel matrix and the precoding matrix, which maximizes the sum rate in the presence of this non-linear distortion. This is done for a third-order polynomial PA model for both the single and multi-user case. By learning this mapping a significant increase in energy efficiency is achieved as compared to conventional precoders and even as compared to perfect digital pre-distortion (DPD), in the saturation regime.
