  Convolutional Neural Networks (CNNs) are one of the most studied family of
deep learning models for signal classification, including modulation,
technology, detection, and identification. In this work, we focus on technology
classification based on raw I/Q samples collected from multiple synchronized
receivers. As an example use case, we study protocol identification of Wi-Fi,
LTE-LAA, and 5G NR-U technologies that coexist over the 5 GHz Unlicensed
National Information Infrastructure (U-NII) bands. Designing and training
accurate CNN classifiers involve significant time and effort that goes into
fine-tuning a model's architectural settings and determining the appropriate
hyperparameter configurations, such as learning rate and batch size. We tackle
the former by defining architectural settings themselves as hyperparameters. We
attempt to automatically optimize these architectural parameters, along with
other preprocessing (e.g., number of I/Q samples within each classifier input)
and learning hyperparameters, by forming a Hyperparameter Optimization
(HyperOpt) problem, which we solve in a near-optimal fashion using the
Hyperband algorithm. The resulting near-optimal CNN (OCNN) classifier is then
used to study classification accuracy for OTA as well as simulations datasets,
considering various SNR values. We show that the number of receivers to
construct multi-channel inputs for CNNs should be defined as a preprocessing
hyperparameter to be optimized via Hyperband. OTA results reveal that our OCNN
classifiers improve classification accuracy by 24.58% compared to manually
tuned CNNs. We also study the effect of min-max normalization of I/Q samples
within each classifier's input on generalization accuracy over simulated
datasets with SNRs other than training set's SNR and show an average of 108.05%
improvement when I/Q samples are normalized.
