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

GAO Shaoyuan; GUO Wenpu; SHI Hao; PENG Ruiyan

doi:10.11999/JEIT251144

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

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S4-UNET: A Long-Sequence Modeling Blind Source Separation Method for Single-Channel Co-Frequency 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 of single-channel co-frequency overlapped communication signals remains a formidable challenge in non-cooperative reception scenarios. Conventional multi-channel methods are inapplicable due to antenna limitations, while existing deep learning approaches suffer from inadequate long-sequence modeling capability, prohibitive computational complexity, and unsatisfactory performance when signals exhibit small carrier frequency offsets. These limitations severely hinder the practical deployment of blind separation techniques in dense electromagnetic environments. There is therefore a critical need for an efficient and robust framework that can effectively capture long-range temporal dependencies while maintaining computational tractability. Methods The proposed S4-UNET deeply 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 building block for both the encoder and decoder to extract local time-frequency features through residual learning. To address the long-range dependency modeling problem, the S4 is strategically embedded in the odd-numbered stages of the encoder, leveraging its inherent capacity to capture global temporal correlations with near-linear computational complexity. The S4 transforms sequence modeling into a state-space evolution process and employs Fast Fourier Transform (FFT) for efficient convolution, complemented by skip connections and Gated Linear Units (GLU) to preserve fine-grained local details. Multi-scale feature fusion is achieved through skip connections between corresponding encoder and decoder stages, and signal resolution is progressively restored via interpolation-based upsampling. The model adaptively tokenizes feature maps either temporally or channel-wise depending on the feature scale, ensuring optimal sequence representation. Results and Discussions Experimental evaluations were conducted on extensive simulation datasets covering identical modulation mixtures, different modulation mixtures, and different bandwidth mixtures with micro frequency offsets, as well as on publicly available benchmarks and hardware-collected measured datasets. Quantitative metrics and visualizations (Fig. 3, Fig. 5, Table 5) demonstrate that S4-UNET consistently outperforms representative deep learning baselines such as ConvTasNet and CTDCRN, as well as the classical TDE-ICA algorithm, across various signal lengths and modulation schemes. The model exhibits robust separation fidelity even under randomly distributed frequency offsets and phase mismatches (Table 3), confirming its strong generalization capacity. Ablation studies and sensitivity analyses (Table 6, Table 7, Table 8) reveal that the selective placement of S4 in odd encoder stages, appropriate convolutional stride configurations, and the adoption of GLU activation collectively contribute to an optimal trade-off between separation accuracy and computational efficiency. Importantly, the model maintains competitive inference latency while effectively handling both long and short sequences, underscoring its practical viability. Conclusions The proposed S4-UNET successfully addresses the core challenges of single-channel co-frequency blind source separation by synergistically combining multi-scale convolutional feature extraction with efficient state-space long-sequence modeling. It demonstrates superior separation performance, robustness against frequency offsets, and favorable generalization across diverse data domains. While the current work focuses on dual-source mixtures, the modular architecture provides a solid foundation for future extensions toward handling an unknown number of sources through integration with source enumeration and iterative cancellation strategies.
- Underdetermined blind source separation,
- single-channel,
- structured state space model,
- deep learning

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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. (查阅网上资料,未找到本条文献卷期、页码信息,请确认). 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. 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. (查阅网上资料, 未找到本条文献出版地信息, 请确认)
[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.