论文标题
使用NLOS频道深入学习基于位置的波束形成
Deep learning for location based beamforming with NLOS channels
论文作者
论文摘要
大量的MIMO系统高效，但严格依赖基站的准确通道状态信息（CSI），以确定适当的预制器。 CSI获取需要发送引起重要开销的飞行员符号。在本文中，仅提出了仅提出从用户位置的知识中确定适当的预编码器的方法。这种确定预编码器的方法称为基于位置的光束形成。它允许减少甚至消除对飞行员符号的需求，具体取决于如何获得位置。所提出的方法以监督方式学习了从位置到预编码器的直接映射。它涉及具有基于随机傅立叶特征的特定结构的神经网络，允许学习包含高空间频率的功能。通过经验评估它，并在现实的合成通道上产生有希望的结果。与先前提出的方法相反，它允许处理视线（LOS）和非线（NLOS）通道。
Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in order to determine appropriate precoders. CSI acquisition requires sending pilot symbols which induce an important overhead. In this paper, a method whose objective is to determine an appropriate precoder from the knowledge of the user's location only is proposed. Such a way to determine precoders is known as location based beamforming. It allows to reduce or even eliminate the need for pilot symbols, depending on how the location is obtained. the proposed method learns a direct mapping from location to precoder in a supervised way. It involves a neural network with a specific structure based on random Fourier features allowing to learn functions containing high spatial frequencies. It is assessed empirically and yields promising results on realistic synthetic channels. As opposed to previously proposed methods, it allows to handle both line-of-sight (LOS) and non-line-of-sight (NLOS) channels.
