Design and implementation of reconfigurable ASK and FSK modulation and demodulation algorithm on FPGA (Field Programmable Gate Array)

Sadiq, Md Wahid; Kabir, Md. Ahasan

doi:10.1016/j.sintl.2021.100155

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Currently, more and more research efforts are focused on modulation format conversion, and there is still room for further exploration in the research of advanced modulation formats with high nonlinear resistance. FSK modulates signals by different frequencies with constant optical power, inherently exhibiting high nonlinear resistance, and has received more attention from researchers in recent years [7][8][9] . By integrating technologies such as FSK, alternating polarization, and CRZ, solutions can be provided for the development of high-speed computing optical networks.…”

Section: Prior Work For Optical Modulation Technologiesmentioning

confidence: 99%

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

He,

Huang,

et al. 2024

Preprint

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In the era of the digital economy, enabling users to utilize computing power as conveniently as water and electricity is inseparable from the development of computing power networks. The carrier network of future 6G also needs to achieve deep collaboration between computing and networking. In high-speed computing power optical networks above 400G, nonlinear effects became one of the main factors limiting system transmission performance. Traditional coherent optical communication solutions are complex to implement, require high coherence and stability of light sources, which is difficult, and costly. This paper proposes a low-cost and low-complexity non-coherent solution based on the advanced modulation format Apol-CRZ-FSK to achieve 4 x 100Gbps computing power optical networks. Simulation results show that it exhibits better resistance to non-linear effects compared to traditional modulation formats such as CRZ-FSK and DQPSK, enabling longer single-span and multi-span transmission distances and superior transmission performance. Furthermore, in response to the transmission requirements and signal perception and recognition requirements in future computing optical networks, this study identifies three different modulation formats through the Inception-ResNet-v2 convolutional neural network model. When compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, the Inception-ResNet-v2 model achieved the highest accuracy rate of 99.51%, which is a 1.66% improvement over the ResNet50 model. It can provide an effective solution for low-cost, low-complexity, and high-performance signal transmission and signal recognition in the 6G era of high-speed computing power optical networks.

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Section: Prior Work For Optical Modulation Technologiesmentioning

confidence: 99%

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

He,

Huang,

et al. 2024

Preprint

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“…(3) In the main control unit, the STM32F407 has three 12-bit precision fast ADCs, each of which meets multiple channels [10]; the F407 also has two 12-bit DACs; the F407 also has two DMA controllers, each of which has its channel, and each of which has a different stream, so that various peripheral-to-memory, memory-to-peripheral, and other different needs can be realized, which is more conducive to the implementation of the circuit.…”

Section: Design Solutions For Regulating System Measurementsmentioning

confidence: 99%

Research on STM32-Based Signal Conditioning System Measurement System

Wei¹

2023

HSET

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The measurement of the signal modulation system is a vital part of channel transmission signal technology, which has a very important position in the signal communication quality assurance system. With the development of radio communications in modern radio technology, the research of AM and FM signal modulation measurement methods has become an important element in this field of research. This research is a signal modulation system using an STM32F407 microcontroller as the control core. The hardware of the system is mainly composed of a coherent demodulation circuit system and a comparator judgment circuit system, and the coherent demodulation circuit system is divided into AM demodulation circuit system and an FM demodulation circuit system. The frequency of the input signal was measured by a comparator, and the frequency change was used to initially determine the signal type. In which the modulating signal of AM signal was obtained by DDS generating separate orthogonal sine and cosine signals with the input signal through a series of operations, while the FM signal was operated with the sine signal. Finally, the amplitude and frequency information of the FM signal was obtained through the comparator, and finally reduced to the modulating signal of the FM signal. Output demodulated signal, its signal waveform without obvious distortion apparent phenomenon. Promotes the prospect of using the calibration of AM and FM signal generators in the field of communication.

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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%

“…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%

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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Design and implementation of reconfigurable ASK and FSK modulation and demodulation algorithm on FPGA (Field Programmable Gate Array)

Cited by 7 publications

References 4 publications

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

Research on STM32-Based Signal Conditioning System Measurement System

Design and Development of an SVM-Powered Underwater Acoustic Modem

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