Implementation of Digital Modulation Techniques in High-Speed FPGA Board

Srinivasan, Sekar; Kavitha, M.; Rani, G. Vimala; Manoharan, L.; Terence, E.; Siva, Alam Venkata

doi:10.1109/icears56392.2023.10085543

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The goal of the modulation classifier is to automatically recognize the modulation type from the received signal S(t), which is an essential step for signal demodulation in non-cooperative communication systems. Amplitude-shift keying (ASK), phase-shift keying (PSK), frequency shift keying (FSK), pulse amplitude modulation (PAM), and quadrature amplitude modulation (QAM) are commonly used modulation schemes [29]. These modulation types are generally generated by modifying one of the carrier signal properties, such as frequency, amplitude, or phase, based on the message signal.…”

Section: Signal Modelmentioning

confidence: 99%

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Abd-Elaziz,

Abdalla,

Elsayed

2023

Sensors

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Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

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Section: Signal Modelmentioning

confidence: 99%

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Abd-Elaziz,

Abdalla,

Elsayed

2023

Sensors

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Veri İletişimi Kodlama Tekniklerinin FPGA Üzerinde Gerçekleştirilmesi

Kumral,

Ersoy

2024

Yalvaç Akademi Dergisi

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Özet 1 : Teknoloji altyapısı bulunan her geliştirmede veri iletişimi gerçekleşmektedir. Veri iletişiminin doğru ve uygun altyapılar kullanılarak sağlanması önem taşımaktadır. Hat kodlaması; fiziksel kanalın ve alıcı ekipmanın belirli özellikleri için optimal olarak ayarlanmış olan genlik ve zaman-ayrık sinyali tarafından taşınacak olan dijital sinyali temsil etmekten ibarettir. İletilecek olan sinyali iletim ortamının özelliklerine uyumlu olacak biçimlere çevirip, sinyal bozucu etkenlerden daha az etkilenebilecek biçimde ve en az bant genişliği kullanımını sağlayacak şekilde değiştiren sinyal işleme uygulamasıdır. Uygulamalarda kullanmak adına çeşitli hat kodlama tekniği mevcuttur. Hangi kodlama tekniğinin kullanılacağı, DC seviyesinin, PSD (Güç Spektral Yoğunluğu) durumunun, bant genişliği gerekliliklerinin, bit hata oranı performansının, saat sinyalinin geri kazanım kolaylığının veya içsel algılama özelliğinin varlığına veya yokluğuna bağlıdır. Bu çalışmada, Manchester, Differential Manchester, NRZ (Non-Return Zero, Sıfıra Dönmeyen), NRZ-Level ve NRZ-Invert hat kodlama tekniklerinin en yaygın donanım tanımlama dili olan Very High Speed İntegrated Circuit Hardware Description Language (VHDL) kullanılarak FPGA kartı üzerinde gerçekleştirilmesi anlatılmıştır. Gerçekleştirilen uygulamanın doğru sonuçlar verip vermediğini kontrol etmek adına, elde edilen tümleşik devreye 8 bitlik veri girişi üzerinden çalışma sağlayacak bir simülasyon kodu yazılmıştır. Simülasyonun çalıştırılması sonucunda her bir kodlama tekniği için oluşan çıkış sinyalleri incelenerek sistemin doğruluğu sağlanmıştır. Özet 2 : Data communication takes place in every development that has a technology infrastructure. It is important to ensure data communication using correct and appropriate infrastructures. Line coding; It consists in representing the digital signal to be carried by the amplitude and time-discrete signal optimally adjusted for the particular characteristics of the physical channel and the receiving equipment. It is a signal processing application that converts the signal to be transmitted into forms that are compatible with the characteristics of the transmission medium and changes it in a way that is less affected by signal disturbances and ensures minimum bandwidth usage. Various line coding techniques are available for use in applications. Which coding technique to use depends on the DC level, PSD (Power Spectral Density) condition, bandwidth requirements, bit error rate performance, ease of recovery of the clock signal, or the presence or absence of intrinsic sensing capability. This paper describes the implementation of Manchester, Differential Manchester, NRZ (Non-Return Zero), NRZ-Level and NRZ-Invert line coding techniques on an FPGA board using Very High Speed Integrated Circuit Hardware Description Language (VHDL), the most common hardware description language In order to check whether the implemented application gives correct results, a simulation code was written to provide operation over 8-bit data input to the integrated circuit. As a result of running the simulation, the accuracy of the system was ensured by analysing the output signals generated for each coding technique.

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Implementation of Digital Modulation Techniques in High-Speed FPGA Board

Cited by 2 publications

References 12 publications

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Veri İletişimi Kodlama Tekniklerinin FPGA Üzerinde Gerçekleştirilmesi

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