论文标题
用于“大型CSI数据”的分布式大型MIMO频道声音 - 机器学习
A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning
论文作者
论文摘要
提出了一种分布式大量的MIMO通道声音，用于获取大型CSI数据集（称为Dichasus）。测量的数据在研究用户本地化，JCAS，频道图表的各种机器学习算法的研究中具有潜在的应用，在FDD操作中启用了大量的MIMO等。所提出的通道音调架构与以前的类似设计不同，因为每个单独的单个Antenna接收器都是完全自主的，可以实现任意，空间分布的天线部署，并在天线数量中提供几乎无限的可伸缩性。可选地，可以通过GNSS接收器（用于室外操作）或通过各种室内定位技术获得的地面真相位置数据标记提取的通道系数向量。
A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of various machine learning algorithms for user localization, JCAS, channel charting, enabling massive MIMO in FDD operation, and many others. The proposed channel sounder architecture is distinct from similar previous designs in that each individual single-antenna receiver is completely autonomous, enabling arbitrary, spatially distributed antenna deployments, and offering virtually unlimited scalability in the number of antennas. Optionally, extracted channel coefficient vectors can be tagged with ground truth position data, obtained either through a GNSS receiver (for outdoor operation) or through various indoor positioning techniques.
