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Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
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AI
DOI：10.1109/TWC.2024.3417892.png)
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摘要
En 中文
Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations due to ambiguities is provided. Consequently, a new channel distance especially designed to remove ambiguities is proposed. Guidelines for designing systems capable of learning quality charts with the proposed distance are also derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.
Keyword:
Vectors
Antenna measurements
Antennas
Wireless communication
Euclidean distance
Bandwidth
Task analysis
Channel charting
dimensionality reduction
MIMO signal processing
machine learning
physical model
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
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