Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities
2025-09-10
Summary
The article reviews deep learning-based techniques for Integrated Sensing and Communication (ISAC) systems, which are vital for the development of future 6G networks. It highlights the advantages of using deep learning to reduce computational complexity and improve performance in real-time tasks such as waveform design, channel estimation, and interference mitigation. Furthermore, the article discusses the challenges these techniques face and outlines potential future research directions.
Why This Matters
As the demand for advanced communication systems grows, particularly with the advent of 6G networks, integrating sensing and communication functionalities becomes crucial. This article emphasizes how deep learning can enhance the efficiency and effectiveness of these systems, making it a relevant resource for professionals interested in the intersection of communication technology and artificial intelligence.
How You Can Use This Info
Professionals can leverage the insights from this article to understand how deep learning techniques can optimize ISAC systems in their work, whether in telecommunications, smart vehicles, or robotics. Additionally, staying informed about the challenges and future directions can help organizations prepare for advancements in AI-driven communication technologies.