Integrated Sensing and Communication (ISAC) is emerging as a transformative technology in the evolution of 6G wireless networks. ISAC represents a game- changing paradigm by enabling the joint design and integration of radar sensing and communication systems, significantly enhancing spectrum efficiency while reducing both hardware costs and computational resources. These advancements are set to unlock innovative possibilities across various sectors, including automotive technology, the Internet of Things (IoT), Extended Reality (XR), and robotics.

Artificial intelligence (AI) and machine learning (ML) algorithms stand to play a crucial role in enhancing ISAC signal processing techniques. By enabling more efficient processing of communication and sensing data, AI can significantly boost the performance of ISAC systems in detection, estimation, and classification tasks, all while maintaining low online complexity.

This project focuses on several essential factors for the development of AI- based ISAC systems, including:

⁃ Create sophisticated deep learning models for accurate sensing parameter estimation within realistic ISAC systems.
⁃ Extensive simulations to study the performance of AI-based ISAC estimators versus classical approaches, which includes performance and complexity gains of ML-based ISAC estimators compared to classical approaches.

The project is expected to drive significant advancements in the efficiency and capabilities of 6G wireless networks, paving the way for a future where seamless integration of sensing and communication becomes the norm, and AI-based techniques are routinely employed for processing both communication and radar signals.