Detection of Frequency-Hopping Signals With Deep Learning

Lee, Kyung-Gyu; Oh, Seong-Jun

doi:10.1109/lcomm.2020.2971216

Cited by 26 publications

(11 citation statements)

References 12 publications

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“…Like time-domain representations, frequencydomain representations can also be kept as a vector of values (such as DFT coefficients) used in [4] or further transforms can be applied to make a PSD image, or STFT image (also known as a spectrogram), used in [9], [11] and others. A benefit of using STFT images is that a time dependency can be captured which allows such a system to be used to detect frequency hopping signals such as in [23]. In Fig.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Radio Frequency Signal Classification with Hybrid Images

Elyousseph¹,

Altamimi²

2021

Preprint

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In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. In this work, we focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, we propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. Our initial results point out limitations to classical pre-processing approaches while also showing that it's possible to build a classifier that can leverage the strengths of multiple signal representations.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Radio Frequency Signal Classification with Hybrid Images

Elyousseph¹,

Altamimi²

2021

Preprint

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“…However, as captured by the uncertainty principle, it is not possible to reach good time and frequency resolutions simultaneously [ 24 ]. TFA methods also suffer from cross-term interference and spectral leakage, resulting in high SNR requirements [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels

Ghosh,

Dong,

Haimovich

et al. 2023

Entropy

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This paper studies blind source separation (BSS) for frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over (i) line-of-sight (LOS), (ii) single-cluster, and (iii) multiple-cluster Spatial Channel Model (SCM) settings. The sources are stationary, spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by leveraging direction of arrival (DOA) information through an FH estimation stage, a DOA estimation stage, and a pairing stage with the latter associating FH patterns with physical sources via their estimated DOAs. Current methods in the literature do not perform the association of multiple frequency hops to the sources they are transmitted from. We bridge this gap by pairing the FH estimates with DOA estimates and labeling signals to their sources, irrespective of their hopped frequencies. A state filtering technique, referred to as hidden state filtering (HSF), is developed to refine DOA estimates for sources that follow a HMM. Numerical results demonstrate that the proposed approach is capable of separating multiple intermittent FH sources.

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“…Efforts were also devoted on the study of the robust DNN training [46]. Recently, the DNNs were also proposed to estimate the parameters of the FHSS signals [47,48]. However, as a common problem with these methods, there is a lack of adaptivity in their implementations.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

Liu

Jiang

2022

Entropy

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The frequency-hopping spread spectrum (FHSS) technique is widely used in secure communications. In this technique, the signal carrier frequency hops over a large band. The conventional non-compressed receiver must sample the signal at high rates to catch the entire frequency-hopping range, which is unfeasible for wide frequency-hopping ranges. In this paper, we propose an efficient adaptive compressed method to measure and detect the FHSS signals non-cooperatively. In contrast to the literature, the FHSS signal-detection method proposed in this paper is achieved directly with compressed sampling rates. The measurement kernels (the non-zero coefficients in the measurement matrix) are designed adaptively, using continuously updated knowledge from the compressed measurement. More importantly, in contrast to the iterative optimizations of the measurement matrices in the literature, the deep neural networks are trained once using task-specific information optimization and repeatedly implemented for measurement kernel design, enabling efficient adaptive detection of the FHSS signals. Simulations show that the proposed method provides stably low missing detection rates, compared to the compressed detection with random measurement kernels and the recently proposed method. Meanwhile, the measurement design in the proposed method is shown to provide improved efficiency, compared to the commonly used recursive method.

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Detection of Frequency-Hopping Signals With Deep Learning

Cited by 26 publications

References 12 publications

Deep Learning Radio Frequency Signal Classification with Hybrid Images

Deep Learning Radio Frequency Signal Classification with Hybrid Images

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

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