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

Chongxiao Cai, Yan Zhu, Min Sheng, Jiandong Li, Yan Shi, Di Zhou, Ziwen Xie, Chen Zhang

TL;DR

The paper addresses the challenge of time-deterministic remote sensing image backhaul in space-ground integrated networks by introducing MDR-TEG to model dynamic 3C resources (storage, computing, transmission). It casts the objective as maximizing the timely delivery of images ($MSTR-TDI$) and solves it via a Lagrange-dual framework that decomposes into two subproblems, coordinated by the SRCC algorithm. An accompanying ESA-based allocation strategy, plus FSC and LSA refinements, yields a practical, near-optimal solution with substantial runtime and performance gains over exhaustive and baseline methods. The results demonstrate that the proposed approach enables efficient on-board processing, cross-slot planning, and reliable timely delivery in dynamic LEO-based backhaul scenarios, offering a scalable design for 6G space-ground computing and communication integration.

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.

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

TL;DR

The paper addresses the challenge of time-deterministic remote sensing image backhaul in space-ground integrated networks by introducing MDR-TEG to model dynamic 3C resources (storage, computing, transmission). It casts the objective as maximizing the timely delivery of images () and solves it via a Lagrange-dual framework that decomposes into two subproblems, coordinated by the SRCC algorithm. An accompanying ESA-based allocation strategy, plus FSC and LSA refinements, yields a practical, near-optimal solution with substantial runtime and performance gains over exhaustive and baseline methods. The results demonstrate that the proposed approach enables efficient on-board processing, cross-slot planning, and reliable timely delivery in dynamic LEO-based backhaul scenarios, offering a scalable design for 6G space-ground computing and communication integration.

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.

Paper Structure

This paper contains 35 sections, 2 theorems, 37 equations, 11 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contribution
  4. System Model
  5. Capacity Constraint
  6. Transmission capacity constraint
  7. Caching capacity constraint
  8. Computing capacity constraint
  9. Flow Conservation
  10. The flow conservation of source node
  11. The flow conservation of destination node
  12. The flow conservation of ordinary node
  13. The flow conservation of virtual computing node
  14. Compression constraint
  15. Delay constraint
  16. ...and 20 more sections

Key Result

Proposition 1

Assume that the optimal solution of the dual problem is $d^{*}$ and the optimal solution of the OP $f(\lambda,x,\lambda_{u}^{sg})$ is $p^{*}$. According to the weak duality theorem, the $d^{*}$ is the maximum lower bound of the $p^{*}$ (for minimization problems).

Figures (11)

  • Figure 1: Multi-dimensional Resource Services Image Applications in the SGIN
  • Figure 2: Examples of resource characterization and path planning based on TEG.
  • Figure 3: Multi-dimensional resource representation based on MDR-TEG and adjust the path in time.
  • Figure 4: The rigidity of directly allocating resources for cross slots path.
  • Figure 5: Per slot Resource Allocation Strategy.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2