Localization Through Deep Learning in New and Low Sampling Rate Environments

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.