Integrated Sensing and Communication (ISAC) systems provide considerable benefits over traditional communication and sensing networks. However, their design and implementation pose significant challenges, especially in terms of signal processing and computational demands. Combining these functionalities necessitates solving complex optimization problems to create optimal waveforms and process signals for both communication and sensing. These tasks are further complicated by constraints such as limited computational resources, tight timing requirements, and rapidly changing channel conditions. Traditional signal processing methods often fall short in achieving the desired outcomes under these circumstances.
Given the complexity of ISAC systems, alternative strategies are essential, as conventional mathematical and iterative optimization approaches may not suffice. Machine learning (ML), particularly Deep Learning (DL), emerges as a promising solution to address these challenges. By leveraging DL techniques, ISAC systems can reduce computational complexity while achieving near-optimal performance.
The intersection of deep learning and ISAC my provide substantial opportunities to enable the implementation of ISAC systems in future communication networks and reduce their energy consumption.
