Attention Aided CSI Wireless Localization
Deep neural networks (DNNs) have become a popular approach for wireless localization based on channel state information (CSI). A common practice is to use the raw CSI in the input and allow the network to learn relevant channel representations for mapping to location information. However, various wo...
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| creator | Salihu, Artan Schwarz, Stefan Rupp, Markus |
| description | Deep neural networks (DNNs) have become a popular approach for wireless localization based on channel state information (CSI). A common practice is to use the raw CSI in the input and allow the network to learn relevant channel representations for mapping to location information. However, various works show that raw CSI can be very sensitive to system impairments and small changes in the environment. On the contrary, hand-designing features may hinder the limits of channel representation learning of the DNN. In this work, we propose attention-based CSI for robust feature learning. We evaluate the performance of attended features in centralized and distributed massive MIMO systems for ray-tracing channels in two non-stationary railway track environments. By comparison to a base DNN, our approach provides exceptional performance. |
| doi_str_mv | 10.48550/arxiv.2203.10506 |
| format | Article |
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| subjects | Artificial neural networks Computer Science - Artificial Intelligence Localization Machine learning Performance evaluation Railway tracks Ray tracing Representations Wireless networks |
| title | Attention Aided CSI Wireless Localization |
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