Demonstrating Deep Learning driven BPSK Demodulation using Software-Defined Radios

Ahmad, Arhum; Agarwal, Suneeta

doi:10.1109/comsnets56262.2023.10041280

Cited by 2 publications

(1 citation statement)

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“…In [20], a signal recognition and demodulation method based on DBN was proposed to solve the problem of signal demodulation in noisy channels. In [21], the authors proposed and demonstrated a deep neural network-based BPSK demodulator that can detect bits even at low signal-to-noise ratio (SNR). In [22], Ahmad et al proposed a demodulation method based on deep feedforward neural network to improve the bit error rate (BER) performance.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based FM Demodulation in Complex Electromagnetic Environment

Zheng,

Pei,

Chen

et al. 2024

IEEE Open J. Commun. Soc.

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In recent years, deep learning has been applied widely in the field of communication. In this paper, a deep learning-based frequency modulation (FM) demodulation method is proposed for FM signal demodulation in complex electromagnetic environment. The received in-phase and quadrature (IQ) signals are fed into a designed model to recover the source signal. We perform experiments with random, voice and broadcasting signals under noise, multipath fading and single tone jamming scenarios. The results demonstrate that the proposed method outperforms the traditional method in terms of demodulation performance across all scenarios. Especially in the case of multipath fading and single tone jamming, our proposed method exhibits significant performance improvements over traditional demodulation. We further propose to use downsampling to reduce the computational complexity of DeepDeFM. The results indicate that by selecting a proper downsampling factor, it is possible to significantly reduce time complexity while maintaining high sound quality with minimal attenuation.

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Section: Introductionmentioning

confidence: 99%