Sensing Resource Scheduling in 5G vRAN: An Elastic Approach

The emerging integrated sensing and communication (ISAC) technologies show great potential for 5G NR, offering a new wireless-sensing infrastructure paradigm. Users can benefit from pervasive sensing applications in various scenarios without communication penalties. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting the applicability in dealing with diverse sensing tasks in the real world. In this article, we introduce ElaSe , a pioneering sensing technique that enables elastic and prompt scheduling of sensing resources. At the core of ElaSe is the exploration of the user’s state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). ElaSe has been implemented on a CPU-based 5G vRAN and commercial 5G user equipments. We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.