Signal Separation in Radio Spectrum Using Self-Attention Mechanism

For a radio frequency (RF) signal separation task, we propose two models operating directly on the time-domain waveform: a Transformer U-Net, a convolution-attention based model with an encoder-decoder architecture where self-attention blocks are inserted in the bottleneck to refine its representations, and a finetuned discriminative WaveNet model. The mixture of signal to separate is based on the ICASSP 2024 Signal Processing Grand Challenge on Data-Driven Signal Separation in Radio Spectrum. Compared to the baseline WaveNet architecture, we observed competitive performance with the Transformer U-Net and performance gains when finetuning the WaveNet model. The submissions achieved the 2nd rank in BER score and 3rd rank in MSE score.