标题： 无线基础模型：针对基于5G的室内定位预训练Transformer模型 显示英文标题

标题： Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization

摘要： 基于人工智能（AI）的射频指纹识别（FP）方法在存在强烈多径效应的传播环境中优于经典的定位方法。然而，FP模型和数据的编排耗时且成本高昂，因为它需要每个环境下的许多参考位置和广泛的测量活动。相反，现代无监督和自监督学习方案对定位所需的参考数据更少，但要么其精度较低，要么需要额外的传感器信息，这使得它们在实践中难以应用。本文提出了一种自监督学习框架，该框架利用我们实时收集的5G信道测量数据对通用变换器（TF）神经网络进行预训练，而无需昂贵的设备。我们的新颖预训练任务通过随机屏蔽和删除输入信息来学习重建这些信息，从而隐式地学习到支持基于FP的定位的空间-时间模式和信息。最有趣的是，当我们优化这个预训练模型以在给定环境中进行定位时，它达到了最先进的方法的精度，但所需的参考数据量减少了十倍，并显著缩短了从训练到运行的时间。 显示英文摘要

摘要： Artificial Intelligence (AI)-based radio fingerprinting (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model and data orchestration of FP are time-consuming and costly, as it requires many reference positions and extensive measurement campaigns for each environment. Instead, modern unsupervised and self-supervised learning schemes require less reference data for localization, but either their accuracy is low or they require additional sensor information, rendering them impractical. In this paper we propose a self-supervised learning framework that pre-trains a general transformer (TF) neural network on 5G channel measurements that we collect on-the-fly without expensive equipment. Our novel pretext task randomly masks and drops input information to learn to reconstruct it. So, it implicitly learns the spatiotemporal patterns and information of the propagation environment that enable FP-based localization. Most interestingly, when we optimize this pre-trained model for localization in a given environment, it achieves the accuracy of state-of-the-art methods but requires ten times less reference data and significantly reduces the time from training to operation.