It has been challenging to separate the time-frequency (TF) overlapped wireless communication signals with unknown number of sources in underdetermined cases. To address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterwards, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. In this paper, the simulations are performed with digital signals of the same modulation and different modulation respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the benchmark algorithms.

The traditional methods exhibit unstable performance and high complexity for separating co‐frequency modulated wireless communication signals under single‐channel conditions. In this study, an end‐to‐end deep separation network based on the attention mechanism is proposed, which employs the encoder‐separator‐decoder architecture to implement the separation. The encoder can convert the signal into a high‐dimensional feature representation for the separator to achieve separation of the signal in a high‐dimensional space. The core of the proposed deep separation network is the innovative separator, which is mainly composed of attention‐based convolution units with residual connection. The attention‐based convolution unit integrates the large kernel convolution and modified global context (GC) block to simultaneously capture the local and global information of the signal. Furthermore, we improve the GC block to implement channel weighting and location weighting for the given feature map, thus further enhancing the adaptability of the model. The experimental results show that the proposed method not only outperforms the traditional methods in separating mixed signals with the same modulation, but also enables the separation of mixed signals with different modulations.

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It has been challenging to separate the time–frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms.