集成感知与通信系统中用于室内定位的CSI掩码潜在预测
Masked Latent Prediction of CSI for Indoor Localization in Integrated Sensing and Communication Systems
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- Nokia Bell Labs（诺基亚贝尔实验室）
- Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
提出基于JEPA的自监督CSI定位框架，通过掩码潜在预测预训练编码器，在DICHASUS-005x数据集上以50%掩码率将定位误差从0.90米降至0.42米，实现标签高效且低开销的室内定位。
中文摘要 AI 辅助
基于信道状态信息（CSI）的指纹定位能够实现精确的室内定位，但面临域偏移、标记数据有限以及多径丰富环境中性能下降等问题。为应对这些挑战，我们提出了一种基于联合嵌入预测架构（JEPA）的自监督定位框架。每个CSI时间快照被视为一个令牌，编码器通过从可见快照预测掩码快照的潜在嵌入进行预训练，无需任何标签即可学习鲁棒的通道表示。随后编码器被冻结，仅训练一个轻量级回归头，利用少量标记位置估计用户位置。我们在实测的DICHASUS-005x数据集上评估该框架，该数据集由单天线发射器在富含多径的室内环境中由32天线阵列接收。在50%掩码率下，与基于原始CSI训练的有监督卷积神经网络（CNN）基线相比，所提方法将平均定位误差从0.90米降至0.42米（降低53%，即0.48米）。由于标签高效且编码器冻结的设计，该框架与集成感知与通信（ISAC）目标一致，能以最少的标记和计算开销实现可靠感知。
英文摘要
Channel State Information (CSI)-based fingerprinting can enable accurate indoor localization but suffers from domain shift, limited labeled data, and degraded performance in multipath-rich environments. To address these challenges, we propose a self-supervised localization framework built on a Joint Embedding Predictive Architecture (JEPA). Each CSI time snapshot is treated as a token, and the encoder is pre-trained by predicting the latent embeddings of masked snapshots from the visible ones, learning robust channel representations without any labels. The encoder is then frozen, and only a lightweight regression head is trained on a small set of labeled positions to estimate the user location. We evaluate the framework on the measured DICHASUS-005x dataset, a single-antenna transmitter received by a 32-antenna array in a multipath-rich indoor environment. With a 50% masking ratio, the proposed method reduces the mean localization error from 0.90 m to 0.42 m (a 53% reduction, or 0.48 m) relative to a supervised Convolutional Neural Network (CNN) baseline trained on raw CSI. Owing to its label-efficient, frozen-encoder design, the framework aligns with integrated sensing and communication (ISAC) objectives, enabling reliable sensing with minimal labeling and compute overhead.
