An Evaluation of CNN Using Deep Residual Learning for Modulation, 5G, LTE, and WLAN System Classification.

In this study, we investigate and present a deep residual learning for modulation and system classification. The simulation results show the degradation problem that was exposed due to an increase in network depth and the saturation of accuracy in the conventional CNN; however, the proposed CNN has no such degradation. Therefore, the processing burden of the conventional CNN is much larger than the proposed CNN. In the simulation results, the proposed CNN framework achieves better system (5G, LTE, and WLAN) classification accuracy as the conventional CNN framework when reducing the processing burden in the proposed one. The better simulation results are shown by adjustment of the parameters using the proposed method in the case of 5G, LTE, and WLAN systems.