FHSS Signal Separation using Constrained Clustering

White, Parker D.; Buehrer, R. Michael; Headley, William C.

doi:10.1109/milcom47813.2019.9021087

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…Another area of spectrum situational awareness seeing a particular increase in the RFML literature is signal detection [31], [32], [34]. Most often, signal detection is discussed in the context of spectrum sensing as a step in identifying a specific or primary user of the spectrum [33], [35]- [38], and is traditionally performed using various energy detection methods and/or matched filtering.…”

Section: B Signal Detectionmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

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Section: B Signal Detectionmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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“…Spectrum sensing is the process of gaining knowledge of a given spectral environment with little, to no, a priori knowledge of the environment. Spectrum sensing is primarily made up of the following Digital Signal Processing (DSP) tasks: signal detection [23]- [41], [87]- [89], [118], signal parameter estimation [13], [92]- [97], signal classification [18], [42]- [81], [83]- [88], [97], [119], emitter identification/fingerprinting [98]- [103], and anomaly detection [104], [105]. These spectrum sensing tasks are of fundamental importance in both military and commercial applications.…”

Section: A Spectrum Sensingmentioning

confidence: 99%

“…2) Signal Detection: Another spectrum sensing area seeing a particular increase in the RFML literature is signal detection [25], [36], [89]. This is one key example in which image processing techniques have been directly applied to solve RFML problems.…”

Section: A Spectrum Sensingmentioning

confidence: 99%

“…This is one key example in which image processing techniques have been directly applied to solve RFML problems. For example, in [25], [36], [89], the raw In-Phase and Quadrature (IQ) samples were converted into spectrum waterfall plots, where the spectrum information in the time-frequency plane is viewed as an image. This has allowed a rich class of image processing techniques to be applied directly to signal detection applications for positive Signal-to-Noise Ratio (SNR) environments.…”

Section: A Spectrum Sensingmentioning

confidence: 99%

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The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Wong,

Clark,

Flowers

et al. 2020

Preprint

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While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications. In particular, recent research has shown deep machine learning to be an enabling technology for cognitive radio applications as well as a useful tool for supplementing expertly defined algorithms for spectrum sensing applications such as signal detection, estimation, and classification (termed here as Radio Frequency Machine Learning, or RFML). A major driver for the usage of deep machine learning in the context of wireless communications is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware/software issues, that must be taken into account before deploying deep machine learning systems in real-world wireless communication applications. This paper provides an overview and survey of prior work related to these major research considerations. In particular, we present their unique considerations in the RFML application space, which are not generally present in the image, audio, and/or text application spaces.

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“…In SDR collection procedure, the transmitter must transmit long enough for all channels that will hopping in it, then we can collect all the frequencies to program into our receivers which is will be of course another smart SDR. there for, from above the FHSS become predictable system for its sequence order time in all next transmissions and traffic data link messaging (Mototolea, et al, 2020) (White, et al, 2019).…”

Section: Introduction For Fhss Chss and Pshssmentioning

confidence: 99%

Securing Physical Layer for FHSS Communication System Using Code andPhase Hopping Techniques in CDMA, System Design and Implementation

Mohammed

2020

jcoeng

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The Frequency-hopping Spread Spectrum (FHSS) systems and techniques are using in military and civilianradar recently and in the communication system for securing the information on wireless communications link channels, for example in the Wi-Fi 8.02.X IEEE using multiple number bandwidth and frequencies in the wireless channel in order to hopping on them for increasing the security level during the broadcast, but nowadays FHSS problem, which is, any Smart Software Defined Radio (S-SDR) can easily detect a wireless signal at the transmitter and the receiver for the hopping sequence in both of these, then duplicate this sequence in order to hack the signal on both transmitter and receiver messages using the order of the sequences that will be recognized for next transmissions. In 2017 Code and Phase Hopping Techniques are both proposed to resolve the most recent problem in security-related, but not with FHSS, therefore in this paper presents a new composed proposed system will progress Phase Shift and Code Hopping in Code Division Multiplexing Access (CDMA) to complement with FHSS; because the wireless Communications systems security in the last years became a nightmare for some individuals, also companies, and even countries, also join them needing for higher bits rate in the wireless channels; because of next-generation communication system 5G-6G and the evolution in social networking, IoT, stream media, IoE, visualizations, and cloud computing. The new ideas can be applicable for a large number of users, also fast implementation without synchronization, as well as without any focus on encryption or keys-exchanges, at completion all results are simulation in MATLAB R2017b, the results have been tested by using 8 users in the same time, also our results showing promises effects on security for both applied systems Phases Shift and Codes Hopping especially the Code Hopping, therefor; our results encouraged us to complete research and compare our system with other.

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FHSS Signal Separation using Constrained Clustering

Cited by 3 publications

References 17 publications

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

Securing Physical Layer for FHSS Communication System Using Code andPhase Hopping Techniques in CDMA, System Design and Implementation

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