3C Resources Joint Allocation for Time-Deterministic Remote Sensing Image Backhaul in the Space-Ground Integrated Network

2025-10-10

AI Summary

In space-air-ground integrated networks, time-deterministic imaging (TDI) data downlink from observation satellites faces severe challenges: highly time-varying and deeply coupled communication, caching, and computing resources, compounded by stringent on-board resource constraints. To address this, we propose a multidimensional resource time-expanded graph model, unifying dynamic resource representation via virtual nodes and time-slot partitioning. We decompose the joint scheduling problem into decoupled subproblems and design an efficient algorithm based on Lagrangian relaxation and subgradient optimization. Integrating mixed-integer linear programming with task sequence scheduling, our method achieves coordinated resource allocation and end-to-end latency guarantees. Simulation results demonstrate that the proposed scheme significantly improves TDI transmission success rate and effectively mitigates timeout issues induced by on-board resource limitations.

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
• Security and Privacy: Data transparency and provenance

Abstract

Low-Earth-orbit (LEO) satellites assist observation satellites (OSs) to compress and backhaul more time-determined images (TDI) has become a new paradigm, which is used to enhance the timeout caused by the limited computing resources of OSs. However, how to capture the time-varying and dynamic characteristics of multi-dimensional resources is challenging for efficient collaborative scheduling. Motivated by this factor, we design a highly succinct multi-dimensional resource time-expanded graph (MDR-TEG) modell. Specifically, by employing a slots division mechanism and introducing an external virtual node, the time-varying communication, caching, and computing (3C) resources are depicted in low complexity by the link weights within, between, and outside the slots. Based on the MDR-TEG, the maximizing successful transmission ratio of TDI (MSTR-TDI) is modeled as a mixed integer linear programming (MILP) problem. Which further relaxed decomposed into two tractable sub-problems: maximizing the successful transmission rate of images (MSTRI) and ensuring the timeliness problem (ETP). Subsequently, an efficient subgradient of relaxation computing constraint (SRCC) algorithm is proposed. The upper and lower bounds of MSTR-TDI are obtained by solving the two subproblems and the dual problem (DP), and the direction of the next iteration is obtained by feedback. Furthermore, arranging the sending sequences of images to improve the quality of the solution. The approximate optimal solution of MSTR-TDI is eventually obtained through repeated iterations. The simulation results verify the superiority of the proposed MDR-TEG model and the effectiveness of the SRCC.

Problem

• Modeling dynamic 3C resource allocation for satellite image backhaul
• Maximizing successful transmission ratio of time-determined images
• Solving mixed integer programming via decomposition and iterative algorithms

Innovation

• Multi-dimensional resource time-expanded graph model
• Mixed integer linear programming with relaxation decomposition
• Subgradient algorithm with iterative feedback optimization