论文标题
利用三胞胎损失和非线性尺寸降低在线频道图表
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
论文作者
论文摘要
渠道图表是一种无监督的学习方法，旨在将无线通道映射到所谓的图表，并尽可能保留尽可能多的空间邻域。在本文中，提出了基于模型的深度学习方法。它建立在一个有力动机的距离测量的基础上，以结构并初始化随后使用三胞胎损耗函数训练的神经网络。所提出的结构表现出较少的参数和巧妙的初始化会导致快速训练。这两个功能使提出的方法适合在频道的频道图表上。该方法在现实的合成通道上进行了经验评估，从而产生了令人鼓舞的结果。
Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learning approach to this problem is proposed. It builds on a physically motivated distance measure to structure and initialize a neural network that is subsequently trained using a triplet loss function. The proposed structure exhibits a low number of parameters and clever initialization leads to fast training. These two features make the proposed approach amenable to on-the-fly channel charting. The method is empirically assessed on realistic synthetic channels, yielding encouraging results.
