Automatic Modulation Recognition for MFSK Using Modified Covariance Method

M.Hamee, Hanan; Wadi, Jafer

doi:10.11591/ijece.v5i3.pp429-435

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…In a BFSK modulation, the carrier signal's frequency is altered in consistent with the message bit's values without changing the amplitude and phase [15,16]. In BFSK, two carrier signals are used in modulation.…”

Section: Binary Frequency Shift Keying (Bfsk)mentioning

confidence: 99%

Implementation of digital and analog modulation systems using FPGA

Mohammed

Abdullah

2020

IJEECS

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FPGA (Field Programmable Gate Array) based implementations of digital and analog modulation techniques play a vital rule in the design of signal processing system. The performance and flexibility provided by reconfigurable computing speeds up the development process in signal processing implementations using FPGA. Different methods for digital and analog modulation are designed in this paper by usinSg System Generator tools & Vivado. Then all designed systems are implemented successfully in an FPGA hardware via the NEXYS 4 DDR with ARTIX 7 XC7A100T. A comparison between five types of digital modulation techniques is discussed in terms of resources utilization in FPGA hardware. And also, the implementation of analog modulations in FPGA is contributed in this work. The hardware implementation shows that the number of slice LUTs in ASK modulation is 0.07% while in FSK modulation is 0.13% of the total number of slice LUTs. And also, the number of bounded IO that used for PSK modulation is 4.8% while in PM modulation is 61.4% of the total number bounded IO.

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Section: Binary Frequency Shift Keying (Bfsk)mentioning

confidence: 99%

Implementation of digital and analog modulation systems using FPGA

Mohammed

Abdullah

2020

IJEECS

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“…In [23], problem of future wireless network has been examined and new learning scheme using artificial neural network is proposed. The author of [24] has discussed a modulation classification method capable of classifying MFSK digital signals without a priori information using modified covariance method. In reference [25] cyclostationary based spectrum sensing method and a modified intrusion elimination (AIC) algorithm to mitigate the interference between the primary and cognitive users had been proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Borde

Joshi

2019

IJECE

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Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications

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“…[21] proposes an optimization algorithm based on instantaneous statistical characteristics of modulated signals and an SVM classifier. In [22], a modulation classification method capable of classifying multiple FSK digital signals without a priori information using modified covariance method was developed. However, as far as the authors know, there does not exist a similar approach to the one presented in this work, in which the signal modulation/demodulation is made by the combination of SVM, CWT, and FSK methods and implemented in an FPGA-based architecture.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an SVM-Powered Underwater Acoustic Modem

Guerrero-Chilabert,

Moreno-Salinas,

Sánchez-Moreno

2024

JMSE

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Underwater acoustic communication is fraught with challenges, including signal distortion, noise, and interferences unique to aquatic environments. This study aimed to advance the field by developing a novel underwater modem system that utilizes machine learning for signal classification, enhancing the reliability and clarity of underwater transmissions. This research introduced a system architecture incorporating a Lattice Semiconductors FPGA for signal modulation and a half-pipe waveguide to emulate the underwater environment. For signal classification, support vector machines (SVMs) were leveraged with the continuous wavelet transform (CWT) employed for feature extraction from acoustic signals. Comparative analysis with traditional signal processing techniques highlighted the efficacy of the CWT in this context. The experiments and tests carried out with the system demonstrated superior performance in classifying modulated signals under simulated underwater conditions, with the SVM providing a robust classification despite the presence of noise. The use of the CWT for feature extraction significantly enhanced the model’s accuracy, eliminating the need for further dimensionality reduction. Therefore, the integration of machine learning with advanced signal processing techniques presents a promising research line for overcoming the complexities of underwater acoustic communication. The findings underscore the potential of data mining methodologies to improve signal clarity and transmission reliability in aquatic environments.

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Automatic Modulation Recognition for MFSK Using Modified Covariance Method

Cited by 3 publications

References 9 publications

Implementation of digital and analog modulation systems using FPGA

Implementation of digital and analog modulation systems using FPGA

Enhanced signal detection slgorithm using trained neural network for cognitive radio receiver

Design and Development of an SVM-Powered Underwater Acoustic Modem

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