DL-based Joint Waveform and Beamforming Design for Integrated Sensing and Communication

As one of candidate technologies for the sixth-generation (6G) wireless network, integrated sensing and communication (ISAC) is expected to support a new use case. Due to the sharing of spectrum and hardware resources, mutual interference between sensing and communication is inevitable, leading to serious degradation on the overall performance. Under complicated channel and system conditions, it is challenging to mitigate the mutual interference effectively. To this end, we provide a low-complexity deep learning (DL)-based solution for ISAC systems by jointly optimizing the sensing signal waveform and the communication receive beamforming from the perspective of the weighted sum of sensing rate and communication rate maximization. Both theoretical analysis and numerical results verify the effectiveness of the proposed DL scheme over baseline ones.