论文标题
基于频道图表
Channel charting based beamforming
论文作者
论文摘要
渠道图表（CC）是一种无监督的学习方法，允许在没有参考的情况下相互定位用户。从更广泛的角度来看，可以将其视为发现低维的潜在空间图表频道的一种方式。在本文中，这种潜在的建模视觉与最近提出的基于位置的光束成型（LBB）方法一起使用，以表明通道图可用于绘制空间或频率中的映射通道。结合CC和LBB会产生类似于自动编码器的神经网络。在通道映射任务上对所提出的方法进行了经验评估，该任务的目的是预测上行链路通道的下行链路通道。
Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.
