TY - GEN
T1 - Blind Source Separation for Wireless Signals Based on GCU-Enhanced Dual-Path Model
AU - Lu, Yaojun
AU - Yang, Ziyi
AU - Pan, Gaofeng
AU - Wang, Shuai
AU - An, Jianping
N1 - Publisher Copyright:
©2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Blind source separation (BSS) has emerged as a significant research direction in deep learning, with widespread applications across various domains. With the advancement of wireless communication technology, some studies have explored its application in spectrum sensing to enhance detection accuracy and identify unauthorized signals. However, research on wireless signal separation based on deep learning remains relatively limited. In this paper, we propose a dual-path deep learning model enhanced by a Gated Convolutional Unit (GCU), termed DPGCU, for BSS of complex mixed signals comprising ten classes across five modulation types. The proposed model integrates a GCU into the dual-path architecture to extract global signal features and enhance the feature extraction capability by effectively combining intra-block, inter-block, and global contextual information. Experimental results demonstrate that the proposed model reduces the separation error probability by 1.5 × 10−3 under noise-free conditions.
AB - Blind source separation (BSS) has emerged as a significant research direction in deep learning, with widespread applications across various domains. With the advancement of wireless communication technology, some studies have explored its application in spectrum sensing to enhance detection accuracy and identify unauthorized signals. However, research on wireless signal separation based on deep learning remains relatively limited. In this paper, we propose a dual-path deep learning model enhanced by a Gated Convolutional Unit (GCU), termed DPGCU, for BSS of complex mixed signals comprising ten classes across five modulation types. The proposed model integrates a GCU into the dual-path architecture to extract global signal features and enhance the feature extraction capability by effectively combining intra-block, inter-block, and global contextual information. Experimental results demonstrate that the proposed model reduces the separation error probability by 1.5 × 10−3 under noise-free conditions.
KW - blind source separation
KW - gated conv unit
KW - spectrum sensing
UR - https://www.scopus.com/pages/publications/105013621086
U2 - 10.1109/ECIS65594.2025.11086926
DO - 10.1109/ECIS65594.2025.11086926
M3 - Conference contribution
AN - SCOPUS:105013621086
T3 - 2025 IEEE 2nd International Conference on Electronics, Communications and Intelligent Science, ECIS 2025 - Proceeding
BT - 2025 IEEE 2nd International Conference on Electronics, Communications and Intelligent Science, ECIS 2025 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Electronics, Communications and Intelligent Science, ECIS 2025
Y2 - 23 May 2025 through 25 May 2025
ER -