kumoh national institute of technology
Networked Systems Lab.

Godwin Tunze, Jae-Min Lee, and Dong-Seong Learning Spatiotemporal Features by using CNN for efficient Modulation Recognition, 2020 Korean Institute of Communication and Sciences (KICS) Summer Conference, August 12-14, 2020, Yong Pyong Resort, Pyeongchang, Gangwon Province, South Korea (N8)
By : Godwin
Date : 2020-06-08
Views : 120

This paper proposes a deep learning framework based on convolutional neural networks aims at extracting and processing spatialtemporal features for an efficient modulation recognition. In this architecture we integrate the strength of grouped and dilated convolutional layers to achieve the efficient recognition in terms of recognition accuracy and less complexity. To allow multilevel feature learning and model generalization we deployed skip connections. Furthermore, to verify the performance of our architecture we performed experimental analysis on RadioML 2018.01A open-source datasets. According to the results, our model outperforms ResNet based model with regards to recognition accuracy and parameter utilization accuracy.