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

2025-06-25

AI Summary

In collaborative ISAC networks, non-stationary spatial distributions of mobile devices and sensing targets induce model drift and suboptimal resource allocation. To address this, this paper proposes a digital twin–driven, slice-based resource management framework. The framework establishes dual network slices—sensing and communication—and integrates large-timeslot global orchestration with small-timeslot distance-aware dynamic target assignment. It further introduces a drift-adaptive statistical model and simulation function to enable online characterization and closed-loop optimization of time-varying spatial dynamics. Leveraging closed-form decision algorithms and empirical validation, the proposed approach improves service satisfaction by 18% and reduces resource consumption by 13.1% over baseline methods, while maintaining stringent quality-of-service guarantees.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Managing resources in cooperative ISAC networks adaptively
• Addressing non-stationary spatial distributions of devices and targets
• Enhancing service satisfaction and reducing resource consumption

Innovation

• Drift-adaptive slicing for ISAC networks
• Digital twins for dynamic spatial distributions
• Low-complexity distance-based sensing assignment