VAE models quantized for Edge TPUs deliver over 42x compression of GNSS signals with F2-score 0.915 for classifying 72 interference types, nearly matching uncompressed performance of 0.923.
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification,
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GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU
VAE models quantized for Edge TPUs deliver over 42x compression of GNSS signals with F2-score 0.915 for classifying 72 interference types, nearly matching uncompressed performance of 0.923.