• Swati Patel
• Mr. Devendra Singh Mandloi
• Mr. Ankit Jain

The ability to classify various types of signal modulations in radio transmissions is an important task for applications in defense, networking and communication areas. This process was traditionally performed manually by human analysis. Recent advances have shown that deep learning methods apply to this feasible task. But existing recognition networks are complex, with high computational requirements and low precision for some types of modulation and in environments not included. We have developed a robust high-frequency signal classifier with a hybrid approach that uses images derived from the signal constellation and spectrogram data in combination with an efficient neural folding network. Compared to the latest generation deep learning classifier, our system achieves better accuracy with lower computational requirements. We conduct an in-depth study on the performance of deep learning-based radio signal classification for radio communications signals.

signal modulation, radio transmission, deep learning, automatic modulation classification

"Deep learning based modulation classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.a308-a336, September-2021, Available :http://www.jetir.org/papers/JETIR2109046.pdf

"Deep learning based modulation classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppa308-a336, September-2021, Available at : http://www.jetir.org/papers/JETIR2109046.pdf