Attention Aided CSI Wireless Localization
This work addresses robust localization for wireless systems in dynamic environments, but it appears incremental as it builds on existing deep learning methods with attention mechanisms.
The paper tackled the problem of wireless localization using channel state information (CSI) by proposing attention-based features to improve robustness against system impairments and environmental changes, achieving exceptional performance compared to a base deep neural network in non-stationary railway environments.
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.