Dynamic Partial Reconfigurable Demodulators In Automatic Modulation Recognition Systems for Satellite Receivers

Keshk, Mohamed; Asami, K.

doi:10.2514/6.2017-5186

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…However, the integration of these techniques in underwater communication systems, particularly in conjunction with FPGA-based architectures for real-time processing, remains underexplored. Some exceptions that employ this FPGA-based architecture are found in [14,15]. The research described in [14] implemented and tested an SVM classifier on an FPGA, where a discrete wavelet transform (DWT) was used to extract the features of received modulated signal, and depending on these features, an SVM can classify binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulation techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Some exceptions that employ this FPGA-based architecture are found in [14,15]. The research described in [14] implemented and tested an SVM classifier on an FPGA, where a discrete wavelet transform (DWT) was used to extract the features of received modulated signal, and depending on these features, an SVM can classify binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulation techniques. The authors in [15] proposed an FPGA-based achitecture to embody several modulation techniques at once, making it more versatile, cost-effective, and easy to use and test.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%