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DetecSHUN: Detection of Signals Hidden Under the Noise

Published: 27 May 2024 Publication History

Abstract

Precise detection of the presence of a signal in low signal-to-noise ratio (SNR) environments has significance in many wireless applications, such as primary user detection in a dynamic spectrum access (DSA) model, localization of signals, or operating in long range scenarios. Existing methods rely largely on traditional signal processing-based techniques for detection, and fail to perform reliably under high noise. To alleviate this problem, this paper develops a new convolutional neural network-based model to detect signals, specifically for high noise environments. Our proposed method outperforms existing deep-learning techniques by ~5 dB at accuracy levels above 90% for signals down to -18 dB SNR, utilizing network sizes within 10% of existing techniques and without any signal pre-processing. The model is generalized for varying sequence lengths and timing offsets, making it ready for adoption in realistic scenarios.

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      cover image ACM Conferences
      WiseML '24: Proceedings of the 2024 ACM Workshop on Wireless Security and Machine Learning
      May 2024
      49 pages
      ISBN:9798400706028
      DOI:10.1145/3649403
      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Published: 27 May 2024

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      1. deep learning
      2. dynamic spectrum access
      3. spread spectrum

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