Work

Analysis on dendritic deep learning model for AMR task

Year: 2024

Type: article

Abstract: This study introduces a novel hybrid deep learning model featuring a dendritic layer for enhancing the performance of automatic modulation recognition (AMR). By replacing the fully connected layer, the proposed model demonstrates superior classification accuracy in AMR tasks. Comparative experiments with nine state-of-the-art deep learning models on the RadioML2016.10a dataset reveal its consistent superiority. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, confirm the significant advantage of the HDM-D model.

Source: Cybersecurity

DOI: 10.1186/s42400-024-00306-9

Authors: Peng Yin, Sanli Zhu, Yang Yu, Ziqian Wang, Zhuangzhi Chen

Institutions: University of Chinese Academy of Sciences, Nanjing University of Posts and Telecommunications, Zhejiang University of Technology

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Awards: G3853717702, G7605978393, G7894066602