Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers

Amir Hossein Yazdani-Abyaneh, Marwan Krunz

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 GHzUnlicensed National Information Infrastructure (U-NII) bands. Designing and training accurate CNN classifiers involve significant time and effort that goes to fine-tuning a model's architectural settings (e.g., number of convolutional layers and their filter size) 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 aHyperparameter 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 using a larger number of receivers to construct multi-channel inputs for CNNs does not necessarily improve classification accuracy. Instead, this number 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 SNRs other than training set's SNR, and show an average of $108.05%$ improvement when I/Q samples are normalized.

Original languageEnglish (US)
Title of host publicationWiseML 2022 - Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450392778
StatePublished - May 19 2022
Event4th ACM Workshop on Wireless Security and Machine Learning, WiseML 2022 - San Antonio, United States
Duration: May 19 2022 → …

Publication series

NameWiseML 2022 - Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning


Conference4th ACM Workshop on Wireless Security and Machine Learning, WiseML 2022
Country/TerritoryUnited States
CitySan Antonio
Period5/19/22 → …


  • 5g nr-u
  • automl
  • cnn
  • hyperband
  • hyperopt
  • lte-laa
  • multi-receiver
  • sdr
  • signal classification
  • wi-fi

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software


Dive into the research topics of 'Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers'. Together they form a unique fingerprint.

Cite this