| Autor: |
Shilian Zheng, Zhangbin Pei, Tao Chen, Jiepeng Chen, Weidang Lu, Xiaoniu Yang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Open Journal of the Communications Society, Vol 5, Pp 1579-1593 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2644-125X |
| DOI: |
10.1109/OJCOMS.2024.3373711 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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