标题： CSI-BERT2：一种用于无线通信和感知中高效信道状态信息预测和分类的BERT启发框架 显示英文标题

标题： CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing

摘要： 信道状态信息（CSI）是无线通信和感知系统中的基本组成部分，它能够实现诸如无线电资源优化和环境感知等关键功能。 在无线感知中，数据稀缺性和数据包丢失阻碍了高效模型训练，而在无线通信中，由于高移动性导致的高维CSI矩阵和短相干时间给CSI估计带来了挑战。为了解决这些问题，我们提出了一种名为CSI-BERT2的统一框架，用于CSI预测和分类任务。 基于CSI-BERT，我们引入了一种两阶段训练方法，首先使用掩码语言模型（MLM）使模型能够在无监督的情况下从稀缺数据集中学习通用特征提取，然后进行微调以适应特定的下游任务。 具体而言，我们将MLM扩展为掩码预测模型（MPM），该模型能够有效地解决CSI预测任务。 我们还引入了一个自适应重加权层（ARL）来增强子载波表示，并引入了一个基于多层感知机（MLP）的时间嵌入模块，以缓解时间序列CSI数据中的排列不变性问题。 这显著提高了原始CSI-BERT模型的CSI分类性能。 在真实世界收集的和模拟的数据集上的大量实验表明，CSI-BERT2在所有任务中都达到了最先进的性能。 我们的结果进一步表明，CSI-BERT2在不同采样率上具有有效的泛化能力，并能稳健地处理由数据包丢失引起的不连续CSI序列——这是传统方法无法解决的挑战。 显示英文摘要

摘要： Channel state information (CSI) is a fundamental component in both wireless communication and sensing systems, enabling critical functions such as radio resource optimization and environmental perception. In wireless sensing, data scarcity and packet loss hinder efficient model training, while in wireless communication, high-dimensional CSI matrices and short coherent times caused by high mobility present challenges in CSI estimation.To address these issues, we propose a unified framework named CSI-BERT2 for CSI prediction and classification tasks. Building on CSI-BERT, we introduce a two-stage training method that first uses a mask language model (MLM) to enable the model to learn general feature extraction from scarce datasets in an unsupervised manner, followed by fine-tuning for specific downstream tasks. Specifically, we extend MLM into a mask prediction model (MPM), which efficiently addresses the CSI prediction task. We also introduce an adaptive re-weighting layer (ARL) to enhance subcarrier representation and a multi-layer perceptron (MLP) based temporal embedding module to mitigate permutation invariance issues in time-series CSI data. This significantly improves the CSI classification performance of the original CSI-BERT model. Extensive experiments on both real-world collected and simulated datasets demonstrate that CSI-BERT2 achieves state-of-the-art performance across all tasks. Our results further show that CSI-BERT2 generalizes effectively across varying sampling rates and robustly handles discontinuous CSI sequences caused by packet loss-challenges that conventional methods fail to address.