Channel Charting for Streaming CSI Data
CoRR(2023)
摘要
Channel charting (CC) applies dimensionality reduction to channel state
information (CSI) data at the infrastructure basestation side with the goal of
extracting pseudo-position information for each user. The self-supervised
nature of CC enables predictive tasks that depend on user position without
requiring any ground-truth position information. In this work, we focus on the
practically relevant streaming CSI data scenario, in which CSI is constantly
estimated. To deal with storage limitations, we develop a novel streaming CC
architecture that maintains a small core CSI dataset from which the channel
charts are learned. Curation of the core CSI dataset is achieved using a
min-max-similarity criterion. Numerical validation with measured CSI data
demonstrates that our method approaches the accuracy obtained from the complete
CSI dataset while using only a fraction of CSI storage and avoiding
catastrophic forgetting of old CSI data.
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关键词
Channel State Information Data,Dimensionality Reduction,Small Core,Catastrophic Forgetting,Neural Network,Current Strategies,Performance Metrics,Shortest Path,Base Station,Latent Space,Data Streams,Euclidean Norm,Memory Capacity,Wireless Communication Systems,Orthogonal Frequency Division Multiplexing,User Equipment,Maximum Similarity,Siamese Network,Small Memory,Core Memory,Channel State Information Estimation,Multiple-input Single-output
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