Channel charting is an emerging self-supervised method that maps channel-state information (CSI) to a low-dimensional latent space (the channel chart) that represents pseudo-positions of user equipments (UEs). While channel charts preserve local geometry, i.e., nearby UEs are nearby in the channel chart (and vice versa), the pseudo-positions are in arbitrary coordinates and global geometry is typically not preserved. In order to embed channel charts in real-world coordinates, we first propose a bilateration loss for distributed multiple-input multiple-output (D-MIMO) wireless systems in which only the access point (AP) positions are known. The idea behind this loss is to compare the received power at pairs of APs to determine whether a UE should be placed closer to one AP or the other in the channel chart. Second, we propose a line-of-sight (LoS) bounding-box loss that places the UE in a predefined LoS area of each AP that is estimated to have a LoS path to the UE. We demonstrate the efficacy of combining both of these loss functions with neural-network-based channel charting using ray-tracing-based and measurement-based channel vectors. Our approach outperforms several baselines and maintains the self-supervised nature of channel charting as it does not rely on geometrical propagation models or require ground-truth UE position information.

翻译：信道图绘制是一种新兴的自监督方法，它将信道状态信息映射到一个低维潜在空间（即信道图），该空间表示用户设备的伪位置。虽然信道图能够保持局部几何关系（即邻近的用户设备在信道图中也彼此靠近，反之亦然），但伪位置处于任意坐标系中，且通常无法保持全局几何结构。为了将信道图嵌入到真实世界坐标中，我们首先针对分布式多输入多输出无线系统提出了一种双边定位损失函数，该系统中仅已知接入点的位置。该损失函数的核心思想是通过比较用户设备在成对接入点处的接收功率，以确定在信道图中应将用户设备置于更接近哪个接入点的位置。其次，我们提出了一种视距边界框损失函数，该函数将用户设备置于每个估计存在视距路径的接入点的预定义视距区域内。我们通过基于射线追踪和实际测量的信道向量，验证了将这两种损失函数与基于神经网络的信道图绘制方法相结合的有效性。我们的方法在多个基线模型上表现出优越性，并保持了信道图绘制的自监督特性，因为它既不依赖于几何传播模型，也不需要用户设备的真实位置信息。