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