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Wideband spectral monitoring using deep learning

Published: 16 July 2020 Publication History

Abstract

We present a system to perform spectral monitoring of a wide band of 666.5 MHz, located within a range of 6 GHz of Radio Frequency (RF) bandwidth, using state-of-the-art deep learning approaches. The system detects, labels, and localizes in time and frequency signals of interest (SOIs) against a background of wideband RF activity. We apply a hierarchical approach. At the lower level we use a sweeping window to analyze a wideband spectrogram, which is input to a deep convolutional network that estimates local probabilities for the presence of SOIs for each position of the window. In a subsequent, higher-level processing step, these local frame probability estimates are integrated over larger two-dimensional regions that are hypothesized by a second neural network, a region proposal network, adapted from object localization in image processing. The integrated segmental probability scores are used to detect SOIs in the hypothesized spectro-temporal regions.

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  • (2024)Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep Learning FrameworksWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71464-1_32(387-401)Online publication date: 13-Nov-2024
  • (2023)WRIST: Wideband, Real-Time, Spectro-Temporal RF Identification System Using Deep LearningIEEE Transactions on Mobile Computing10.1109/TMC.2023.324097123:2(1550-1567)Online publication date: 31-Jan-2023
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cover image ACM Conferences
WiseML '20: Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning
July 2020
91 pages
ISBN:9781450380072
DOI:10.1145/3395352
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 July 2020

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Author Tags

  1. cognitive radio
  2. deep learning
  3. wideband spectral monitoring

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  • (2025)VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and RecognitionIEEE Transactions on Wireless Communications10.1109/TWC.2024.349681324:2(909-925)Online publication date: Feb-2025
  • (2024)Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep Learning FrameworksWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71464-1_32(387-401)Online publication date: 13-Nov-2024
  • (2023)WRIST: Wideband, Real-Time, Spectro-Temporal RF Identification System Using Deep LearningIEEE Transactions on Mobile Computing10.1109/TMC.2023.324097123:2(1550-1567)Online publication date: 31-Jan-2023
  • (2023)Advances in Machine Learning-Driven Cognitive Radio for Wireless Networks: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334579626:2(1201-1237)Online publication date: 21-Dec-2023
  • (2023)An End-to-End Deep Learning Framework for Wideband Signal RecognitionIEEE Access10.1109/ACCESS.2023.3280454(1-1)Online publication date: 2023
  • (2023)Adversarial defense method based on ensemble learning for modulation signal intelligent recognitionWireless Networks10.1007/s11276-023-03299-429:7(2967-2980)Online publication date: 22-Mar-2023
  • (2022)Application of Object Detection Approaches on the Wideband Sensing Problem2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)10.1109/BlackSeaCom54372.2022.9858132(341-346)Online publication date: 6-Jun-2022
  • (2022)Spectrum Monitoring Based on End-to-End Learning by Deep LearningInternational Journal of Wireless Information Networks10.1007/s10776-021-00548-129:2(180-192)Online publication date: 12-Jan-2022
  • (2022)Location-Independent Human Activity Recognition Using WiFi SignalSignal and Information Processing, Networking and Computers10.1007/978-981-19-3387-5_158(1319-1329)Online publication date: 2-Jul-2022

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