Single-Channel Blind Source Separation in Wireless Communications: A Complex-Domain Deep Learning Approach

The escalating crosstalk and interference in wireless services necessitate effective techniques like single-channel blind source separation (SCBSS). Deep Learning (DL) has shown promise in enhancing spectrum efficiency in wireless communications. Existing DL-SCBSS methods primarily focus on speech separation and speech enhancement, limiting their direct application to communication signals due to waveform and time scale differences. To overcome this, we introduce a complex time-domain dilated convolutional recurrent network (CTDCRN) featuring a complex hierarchical encoder (CHE) for complex signal representation. CTDCRN employs a convolutional recurrent network with complex dilated convolution module (CDCM) and complex LSTM (CLSTM) to extract precise features from communication signals. CDCM models short and long-term dependencies using depth-wise dilated convolutions, enhancing individual signal separation. Unlike speech separation networks, CTDCRN processes in-phase and quadrature components (I/Q) of complex signals. Simulation results showcase CTDCRN's superiority over traditional methods and real-valued networks in Pearson's correlation coefficient and bit error rate (BER).