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We study efficient deep learning training algorithms that process receivedwireless signals, if a test Signal to Noise Ratio (SNR) estimate is available.We focus on two tasks that facilitate source identification: 1- Identifying themodulation type, 2- Identifying the wireless technology and channel in the 2.4GHz ISM band. For benchmarking, we rely on recent literature on testing deeplearning algorithms against two well-known datasets. We first demonstrate thatusing training data corresponding only to the test SNR value leads to dramaticreductions in training time while incurring a small loss in average testaccuracy, as it improves the accuracy for low SNR values. Further, we show thatan erroneous test SNR estimate with a small positive offset is better fortraining than another having the same error magnitude with a negative offset.Secondly, we introduce a greedy training SNR Boosting algorithm that leads touniform improvement in accuracy across all tested SNR values, while using asmall subset of training SNR values at each test SNR. Finally, we demonstratethe potential of bootstrap aggregating (Bagging) based on training SNR valuesto improve generalization at low test SNR values with scarcity of trainingdata.

Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates