openThis work provides a general view of Integrated Sensing And Communication (ISAC) systems,
what are them and why this is a needed technology. The first section introduces the foundational
concepts of modern communication systems, including an overview of ISAC’s historical devel-
opment. The following section deep dives into ISAC’s architecture, analyzing strength points,
weaknesses and open challenges. We dealt with particular attention with the bi-static offset prob-
lem. The document then explores various Joint Sensing and Communication (JSC) use cases, such
as Human Activity Recognition (HAR) and security considerations, emphasizing the importance
of data privacy implications. The third section examines the role of Machine Learning (ML) in
ISAC, explaining data preparation, training, prediction, and specific roles that ML plays in en-
hancing ISAC capabilities ending with advanced techniques like federated learning. Finally, the
document presents the SHARP algorithm, a real case algorithm developed in 2022 by a UNIPD’s
research team, explaining the dataset, the techniques employed in the design of the algorithm and
the results. This comprehensive exploration underscores the critical role of ISAC in advancing
modern communication systems and highlights future directions for research and application.This work provides a general view of Integrated Sensing And Communication (ISAC) systems,
what are them and why this is a needed technology. The first section introduces the foundational
concepts of modern communication systems, including an overview of ISAC’s historical devel-
opment. The following section deep dives into ISAC’s architecture, analyzing strength points,
weaknesses and open challenges. We dealt with particular attention with the bi-static offset prob-
lem. The document then explores various Joint Sensing and Communication (JSC) use cases, such
as Human Activity Recognition (HAR) and security considerations, emphasizing the importance
of data privacy implications. The third section examines the role of Machine Learning (ML) in
ISAC, explaining data preparation, training, prediction, and specific roles that ML plays in en-
hancing ISAC capabilities ending with advanced techniques like federated learning. Finally, the
document presents the SHARP algorithm, a real case algorithm developed in 2022 by a UNIPD’s
research team, explaining the dataset, the techniques employed in the design of the algorithm and
the results. This comprehensive exploration underscores the critical role of ISAC in advancing
modern communication systems and highlights future directions for research and application
