S4-UNET: A Long-Sequence Modeling Blind Source Separation Method for Single-Channel Co-Channel Overlapped Communication Signals

GAO Shaoyuan; GUO Wenpu; SHI Hao; PENG Ruiyan

doi:10.11999/JEIT251144

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

GAO Shaoyuan, GUO Wenpu, SHI Hao, PENG Ruiyan. S4-UNET: A Long-Sequence Modeling Blind Source Separation Method for Single-Channel Co-Channel Overlapped Communication Signals[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251144

Citation:

GAO Shaoyuan, GUO Wenpu, SHI Hao, PENG Ruiyan. S4-UNET: A Long-Sequence Modeling Blind Source Separation Method for Single-Channel Co-Channel Overlapped Communication Signals[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251144

Citation:

GAO Shaoyuan, GUO Wenpu, SHI Hao, PENG Ruiyan. S4-UNET: A Long-Sequence Modeling Blind Source Separation Method for Single-Channel Co-Channel Overlapped Communication Signals[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251144

PDF( 3882 KB)

S4-UNET: A Long-Sequence Modeling Blind Source Separation Method for Single-Channel Co-Channel Overlapped Communication Signals

doi: 10.11999/JEIT251144 cstr: 32379.14.JEIT251144

College of Combat Support, Rocket Force University of Engineering, Xi’an 710025, China

Received Date: 2025-11-01
Accepted Date: 2026-04-12
Rev Recd Date: 2026-04-12

Available Online: 2026-05-23

Abstract

Abstract

Objective Blind Source Separation (BSS) of single-channel co-channel overlapped communication signals remains challenging in non-cooperative reception. Conventional multi-channel methods are not applicable because of antenna limitations. Existing deep learning methods also show limited long-sequence modeling ability, high computational cost, and reduced performance for signals with small carrier frequency offsets. These limitations restrict the practical use of BSS techniques in dense electromagnetic environments. An efficient and robust framework is therefore needed to capture long-range temporal dependencies while maintaining computational feasibility. Methods S4-UNET integrates the U-NET encoder-decoder framework with the Structured State Space sequence model (S4). A Temporal State Enhancement Module (TSEM) is designed as the backbone block of both the encoder and decoder. It extracts local temporal features through residual learning. To model long-range dependencies, S4 is embedded in the odd-numbered stages of the encoder. This design captures global temporal correlations with near-linear computational complexity. S4 converts sequence modeling into a state-space evolution process and uses the Fast Fourier Transform (FFT) for efficient convolution. Skip connections and the Gated Linear Unit (GLU) are used to preserve fine-grained local details. Multi-scale feature fusion is achieved through skip connections between corresponding encoder and decoder stages. Signal resolution is then progressively restored by interpolation-based upsampling. The model also adaptively tokenizes feature maps in the temporal or channel dimension according to feature scale, which improves sequence representation. Results and Discussions Experiments are conducted on simulated datasets with small carrier frequency offsets, including same-modulation mixtures, mixed-modulation mixtures, and different-bandwidth mixtures. Public benchmark datasets and a measured dataset collected using hardware are also used. Quantitative results and visualizations (Fig. 3, Fig. 5, Table 5) show that S4-UNET consistently outperforms representative deep learning baselines, including ConvTasNet and CTDCRN, and the classical Time-Delay Embedding Independent Component Analysis (TDE-ICA) algorithm across different signal lengths and modulation schemes. The model maintains robust separation fidelity under randomly distributed carrier frequency offsets and initial phase differences (Table 3), confirming its strong generalization ability. Ablation and sensitivity analyses (Table 6, Table 7, Table 8) show that placing S4 in the odd-numbered encoder stages, using suitable convolutional stride settings, and adopting GLU jointly support a favorable balance between separation accuracy and computational efficiency. The model also maintains competitive inference latency while processing both long and short sequences, indicating its practical value. Conclusions S4-UNET addresses the main challenges of single-channel co-channel BSS by combining multi-scale convolutional feature extraction with efficient state-space long-sequence modeling. It achieves superior separation performance, strong robustness to small carrier frequency offsets, and good generalization across different data domains. The present work focuses on dual-source mixtures. Its modular architecture provides a basis for future extensions to mixtures with an unknown number of sources by integrating source number estimation and iterative cancellation strategies.
- Underdetermined blind source separation,
- single-channel,
- structured state space model,
- deep learning

FullText(HTML)

References(18)

References

[1]	ZHANG Weipeng, TAIT A, HUANG Chaoran, et al. Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation[J]. Nature Communications, 2023, 14(1): 1107. doi: 10.1038/s41467-023-36814-4.
[2]	ANSARI S, ALATRANY A S, ALNAJJAR K A, et al. A survey of artificial intelligence approaches in blind source separation[J]. Neurocomputing, 2023, 561: 126895. doi: 10.1016/j.neucom.2023.126895.
[3]	邓文, 黄知涛, 王翔. 单通道通信信号盲分离方法的研究进展综述[J]. 通信学报, 2023, 44(8): 179–194. doi: 10.11959/j.issn.1000-436x.2023138. DENG Wen, HUANG Zhitao, and WANG Xiang. Overview of research progress on blind separation methods for single channel communication signal[J]. Journal on Communications, 2023, 44(8): 179–194. doi: 10.11959/j.issn.1000-436x.2023138.
[4]	SCHWIEGELSHOHN F, OSSOVSKI E, and HÜBNER M. A resampling method for parallel particle filter architectures[J]. Microprocessors and Microsystems, 2016, 47: 314–320. doi: 10.1016/j.micpro.2016.07.017.
[5]	LIU Xiaobei and GUAN Yongliang. Single-channel blind separation of unsynchronized multiuser PSK signals with non-identical sampling frequency offsets[J]. IEEE Communications Letters, 2022, 26(11): 2774–2778. doi: 10.1109/LCOMM.2022.3202538.
[6]	LUO Yi and MESGARANI N. Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 27(8): 1256–1266. doi: 10.1109/TASLP.2019.2915167.
[7]	兰朝凤, 杨国涛, 陈英淇, 等. 时频域多尺度信息交互策略的单声道语音分离方法研究[J]. 电子与信息学报, 2025, 预发表. doi: 10.11999/JEIT251340. LAN Chaofeng, YANG Guotao, CHEN Yingqi, et al. Research on monophonic speech separation method using time-frequency domain multi-scale information interaction strategy[J]. Journal of Electronics & Information Technology, 2025, in press. doi: 10.11999/JEIT251340.
[8]	HOU Xiaoqi and GAO Yong. Single-channel blind separation of co-frequency signals based on convolutional network[J]. Digital Signal Processing, 2022, 129: 103654. doi: 10.1016/j.dsp.2022.103654.
[9]	MA Hao, ZHENG Xiang, YU Lu, et al. A novel end‐to‐end deep separation network based on attention mechanism for single channel blind separation in wireless communication[J]. IET Signal Processing, 2023, 17(2): e12173. doi: 10.1049/sil2.12173.
[10]	YANG Boyi, CHEN Tao, and LEI Yu. Single-channel radar signal separation based on instance segmentation with mask optimization[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, 71(5): 2879–2883. doi: 10.1109/TCSII.2024.3350662.
[11]	GUO Pengcheng, YU Miao, SHEN Lei, et al. Single-channel blind source separation in wireless communications: A complex-domain deep learning approach[J]. IEEE Wireless Communications Letters, 2024, 13(6): 1645–1649. doi: 10.1109/LWC.2024.3384813.
[12]	DENG Wen, WANG Xiang, and HUANG Zhitao. Co-channel multiuser modulation classification using data-driven blind signal separation[J]. IEEE Internet of Things Journal, 2024, 11(8): 14829–14843. doi: 10.1109/JIOT.2023.3345023.
[13]	LUO Jian, QIU Zhaoyang, XIAO Jian, et al. Single-channel blind source separation of co-channel communication signals: A hybrid knowledge-data driven approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2026, 12: 5704–5717. doi: 10.1109/TCCN.2026.3658769.
[14]	LU Weitsung. WANG Juchiang, KONG Qiuqiang, et al. Music source separation with band-split rope transformer[C]. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Republic of Korea, 2024: 481–485. doi: 10.1109/ICASSP48485.2024.10446843.
[15]	付卫红, 张鑫钰, 刘乃安. 基于多尺度融合神经网络的同频同调制单通道盲源分离算法[J]. 系统工程与电子技术, 2025, 47(2): 641–649. doi: 10.12305/j.issn.1001-506X.2025.02.30. FU Weihong, ZHANG Xinyu, and LIU Naian. Single-channel blind source separation algorithm for co-frequency and co-modulation based on multi-scale fusion neural network[J]. Systems Engineering and Electronics, 2025, 47(2): 641–649. doi: 10.12305/j.issn.1001-506X.2025.02.30.
[16]	GU A, GOEL K, and RE C. GU A, GOEL K, and RE C. Efficiently modeling long sequences with structured state spaces[C]. Proceedings of the 10th International Conference on Learning Representations (ICLR), 2022.
[17]	KALMAN R. On the general theory of control systems[J]. IRE Transactions on Automatic Control, 1959, 4(3): 110. doi: 10.1109/TAC.1959.1104873.
[18]	ROUX J L, WISDOM S, ERDOGAN H, et al. SDR – half-baked or well done?[C]. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019: 626–630. doi: 10.1109/ICASSP.2019.8683855.