用视觉变压器聚焦信道矩阵角度延迟特征，提升设备定位精度。
Vision Transformer Based User Equipment Positioning
- 基于视觉变压器提取信道状态的角延迟轮廓特征
- 室内定位误差0.55米，室外最差场景误差3.45米
- 比现有方法精度提升约38%，适合非序列信道数据
近年来，深度学习技术被用于用户设备（UE）定位。然而，这些模型存在两大缺陷：一是对输入整体给予同等关注；二是不适用于非序列数据，例如仅能获取瞬时信道状态信息（CSI）的情况。为此，本文提出一种基于注意力机制的视觉变压器（ViT）架构，专注于从CSI矩阵中提取角延迟轮廓（ADP）特征。该方法在DeepMIMO和ViWi射线追踪数据集上进行了验证，实现室内RMSE为0.55米，DeepMIMO室外场景为13.59米，ViWi室外遮挡场景为3.45米。所提方案相较当前最优方法精度提升约38%，且在误差分布上也显著优于其他对比方法。
原文摘要 · Abstract (English)
Recently, Deep Learning (DL) techniques have been used for User Equipment (UE) positioning. However, the key shortcomings of such models is that: i) they weigh the same attention to the entire input; ii) they are not well suited for the non-sequential data e.g., when only instantaneous Channel State Information (CSI) is available. In this context, we propose an attention-based Vision Transformer (ViT) architecture that focuses on the Angle Delay Profile (ADP) from CSI matrix. Our approach, validated on the `DeepMIMO' and `ViWi' ray-tracing datasets, achieves an Root Mean Squared Error (RMSE) of 0.55m indoors, 13.59m outdoors in DeepMIMO, and 3.45m in ViWi's outdoor blockage scenario. The proposed scheme outperforms state-of-the-art schemes by $\sim$ 38\%. It also performs substantially better than other approaches that we have considered in terms of the distribution of error distance.
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