Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins
Fabio Saggese, Federico Chiariotti, Shashi Raj Pandey, Henk Wymeersch, Luca Sanguinetti, Petar Popovski
TL;DR
This work designs a push-based random access in which sensors with a high Value of Information (VoI) inform the network of their access requirements, followed by a pull-based scheduled transmission of the actual sensory data, which allows to combine the ISAC and reliable transmission requirements and maximize the VoI of the information delivered to the DT.
Abstract
The digital twins (DTs) of physical systems and environments enable real-time remote tracking, control, and learning, but require low-latency transmission of updates and sensory data to maintain alignment with their physical counterparts. In this context, augmenting sensory data with the network's own integrated sensing and communication (ISAC)capabilities can expand the DT's awareness of the environment by allowing it to precisely non-radar locate measurements from mobile nodes. However, this integration increases the complexity of the communication system, and can only be supported through intelligent resource allocation and access optimization. In this work, we propose a two-step goal-oriented approach to solve this problem: we design a push-based random access in which sensors with a high Value of Information (VoI) inform the network of their access requirements, followed by a pull-based scheduled transmission of the actual sensory data. This design allows to combine the ISAC and reliable transmission requirements and maximize the VoI of the information delivered to the DT, significantly outperforming existing schemes.