用无线信道数据预测未来信号变化，准确率提升一倍。
Learning Latent Wireless Dynamics from Channel State Information
- 将信道状态信息映射到隐空间，再预测其动态演化。
- 长时预测任务下准确率比基准方法高两倍。
- 适合研究无线通信系统建模与预测的工程师。
本文提出一种新型数据驱动机器学习方法，用于在隐空间中建模与预测无线传播环境的动态特性。基于信道制图思想，该方法学习高维信道状态信息（CSI）的压缩表示，并引入预测模块捕捉无线系统的动态变化。我们联合训练一个信道编码器，将估计的CSI映射至合适的隐空间，以及一个预测器，建模这些表示之间的关系。因此，问题转化为训练一个联合嵌入预测架构（JEPA），从CSI模拟无线网络的隐动态。我们在实测数据上进行了数值评估，结果表明，在长时前瞻预测任务中，所提JEPA相比基准方法准确率提升两倍。
原文摘要 · Abstract (English)
In this work, we propose a novel data-driven machine learning (ML) technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations of high-dimensional channel state information (CSI), we incorporate a predictive component to capture the dynamics of the wireless system. Hence, we jointly learn a channel encoder that maps the estimated CSI to an appropriate latent space, and a predictor that models the relationships between such representations. Accordingly, our problem boils down to training a joint-embedding predictive architecture (JEPA) that simulates the latent dynamics of a wireless network from CSI. We present numerical evaluations on measured data and show that the proposed JEPA displays a two-fold increase in accuracy over benchmarks, for longer look-ahead prediction tasks.
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