Abstract
Source localization in wireless networks is essential for spectrum utilization optimization. Traditional methods often require extensive transmitter information while existing deep learning approaches perform poorly in new and low sampling rate environments. We introduce LocNet, a deep learning approach that overcomes these limitations using a compact UNet-like architecture incorporating environmental maps. Unlike other deep learning strategies, LocNet adopts loss functions designed explicitly for imbalanced data, moving beyond the conventional mean-square error loss. Our comparative analysis reveals that LocNet outperforms other deep learning models by more than a factor of two. This advancement underscores LocNet’s suitability for real-world deployment across diverse operational contexts.
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Acknowledgement
The research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-23-2-0014. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, not withstanding any copyright notation herein.
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Le, T.D., Huang, Y. (2024). Localization Through Deep Learning in New and Low Sampling Rate Environments. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_24
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