The convergence of sensing and communication functionalities in next-generation wireless networks is driving a paradigm shift towards network-centric ISAC. This chapter explores the potential of cooperative ISAC architectures in 6G networks, where sensing tasks are no longer performed by isolated nodes but by distributed, interconnected devices. We first introduce the key enabling technologies and architectures that support networked ISAC. We then focus on cooperative sensing strategies, highlighting how inter- node coordination can enhance detection, localization, and tracking performance. Particular attention is devoted to resource allocation techniques, in which spectrum, time, and power are dynamically shared between communication and sensing functions based on application-specific priorities. Finally, we address AI-based methods for target classification and tracking in the networked ISAC context, discussing how target recognition can drive target-specific resource allocation and tracking strategies, ultimately boosting overall sensing performance.
Matricardi, E., Pucci, L., Favarelli, E., Paolini, E., Giorgetti, A. (2025). Towards Seamless Networked Integrated Sensing and Communication. Rome : Texmat [10.57620/CNIT-Report_16].
Towards Seamless Networked Integrated Sensing and Communication
E. Matricardi;L. Pucci;E. Favarelli;E. Paolini;A. Giorgetti
2025
Abstract
The convergence of sensing and communication functionalities in next-generation wireless networks is driving a paradigm shift towards network-centric ISAC. This chapter explores the potential of cooperative ISAC architectures in 6G networks, where sensing tasks are no longer performed by isolated nodes but by distributed, interconnected devices. We first introduce the key enabling technologies and architectures that support networked ISAC. We then focus on cooperative sensing strategies, highlighting how inter- node coordination can enhance detection, localization, and tracking performance. Particular attention is devoted to resource allocation techniques, in which spectrum, time, and power are dynamically shared between communication and sensing functions based on application-specific priorities. Finally, we address AI-based methods for target classification and tracking in the networked ISAC context, discussing how target recognition can drive target-specific resource allocation and tracking strategies, ultimately boosting overall sensing performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


