Drift-Adaptive Slicing-Based Resource Management for Cooperative ISAC Networks

TL;DR

This paper introduces a drift-adaptive slicing-based resource management scheme for cooperative ISAC networks, improving service satisfaction and resource efficiency by accounting for non-stationary spatial distributions with digital twins.

Contribution

It presents a novel drift-adaptive resource management approach using digital twins to handle non-stationary distributions in cooperative ISAC networks.

Findings

Increases service satisfaction ratio by up to 18%.

Reduces resource consumption by up to 13.1%.

Effective in dynamic, non-stationary environments.

Abstract

In this paper, we propose a novel drift-adaptive slicing-based resource management scheme for cooperative integrated sensing and communication (ISAC) networks. Particularly, we establish two network slices to provide sensing and communication services, respectively. In the large-timescale planning for the slices, we partition the sensing region of interest (RoI) of each mobile device and reserve network resources accordingly, facilitating low-complexity distance-based sensing target assignment in small timescales. To cope with the non-stationary spatial distributions of mobile devices and sensing targets, which can result in the drift in modeling the distributions and ineffective planning decisions, we construct digital twins (DTs) of the slices. In each DT, a drift-adaptive statistical model and an emulation function are developed for the spatial distributions in the corresponding slice, which facilitates closed-form decision-making and efficient validation of a planning decision, respectively. Numerical results show that the proposed drift-adaptive slicing-based resource management scheme can increase the service satisfaction ratio by up to 18% and reduce resource consumption by up to 13.1% when compared with benchmark schemes.