用自监督学习预训练5G信号模型，大幅减少定位所需数据量。
Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization
- 通过随机掩码输入信号，让Transformer模型自重建，学习空间时间特征。
- 在新环境中定位精度达顶尖水平，仅需十倍少的参考数据。
- 适合需要快速部署、数据采集成本高的室内定位场景。
基于人工智能的无线电指纹（FP）在强多径传播环境下优于传统定位方法。然而，传统方法需大量参考位置和广泛测量，成本高且耗时。现有无监督与自监督学习虽减少数据需求，但精度不足或需额外传感器信息，实用性受限。本文提出一种自监督学习框架，利用实时采集的5G信道数据预训练通用Transformer神经网络，无需昂贵设备。其创新的预训练任务通过随机掩码和丢弃输入信息，使模型隐式学习传播环境的空间-时间模式，从而支持指纹定位。最令人关注的是，在特定环境微调后，该模型达到当前最优精度，却仅需十分之一的参考数据，并显著缩短从训练到部署的时间。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。