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CSI-Based MIMO Indoor Positioning Using Attention-Aided Deep Learning
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DOI：10.1109/LCOMM.2023.3335408.png)
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摘要
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Location-based services have become an indispensable component of wireless networks, but high-precision positioning is challenging. With the application of multiple-input multiple-output (MIMO) in 5G, accurate channel state information (CSI) can be obtained and leveraged for high-precision positioning. Solving the MIMO positioning problem by deep learning has demonstrated better accuracy than traditional methods. To further improve the positioning accuracy, we propose a novel deep learning model named ACPNet, which incorporates two types of attention mechanisms and an improved training scheme. Experiment results show that compared to the state-of-the-art work, ACPNet exhibits more than 20% positioning accuracy improvement, and also maintains a relatively low computation complexity.
Keyword:
Training
MIMO communication
Deep learning
Task analysis
Convolution
Neural networks
Kernel
Positioning
MIMO
CSI
deep learning
attention mechanism
training scheme
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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