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Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks

Yu Zhang, Yanmin Gong, Lei Fan, Yu Wang, Zhu Han, Yuanxiong Guo

TL;DR

This paper tackles the challenge of minimizing the time-average expected service delay in MEC-enabled SATINs that integrate LEO satellites, HAPs, and terrestrial BSs under energy constraints.It introduces a Lyapunov-based online optimization framework that decomposes the stochastic problem into sequential one-slot problems, each solved via a Hybrid Quantum-classical Generalized Benders’ Decomposition (HQCGBD).HQ CGBD splits each slot into a convex subproblem solved classically and a master MILP solved on quantum hardware through a QUBO reformulation, with a multi-cut extension to accelerate convergence.Numerical results show HQCGBD outperforms classical GBD and baseline schemes in minimizing delay while respecting energy budgets, illustrating the practical potential of quantum-accelerated optimization in dynamic SATIN MEC systems.

Abstract

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.

Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks

TL;DR

This paper tackles the challenge of minimizing the time-average expected service delay in MEC-enabled SATINs that integrate LEO satellites, HAPs, and terrestrial BSs under energy constraints.It introduces a Lyapunov-based online optimization framework that decomposes the stochastic problem into sequential one-slot problems, each solved via a Hybrid Quantum-classical Generalized Benders’ Decomposition (HQCGBD).HQ CGBD splits each slot into a convex subproblem solved classically and a master MILP solved on quantum hardware through a QUBO reformulation, with a multi-cut extension to accelerate convergence.Numerical results show HQCGBD outperforms classical GBD and baseline schemes in minimizing delay while respecting energy budgets, illustrating the practical potential of quantum-accelerated optimization in dynamic SATIN MEC systems.

Abstract

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.
Paper Structure (27 sections, 2 theorems, 36 equations, 8 figures, 3 tables, 2 algorithms)

This paper contains 27 sections, 2 theorems, 36 equations, 8 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Resource Management for SATINs
  4. Quantum Annealing for Optimization
  5. Preliminaries
  6. Theoretical Background of QA
  7. QA Workflow
  8. System Model and Problem Formulation
  9. Network Model
  10. Task Model
  11. Task Execution Model
  12. Communication Model
  13. Computation Model
  14. Problem Formulation
  15. Online Control Algorithm Design
  16. ...and 12 more sections

Key Result

Lemma 1

Under any feasible action that can be implemented at time slot $t$, we have where $C^*= (1/2)\sum_{m \in \mathcal{M}}((\sum_{u \in \mathcal{U}}E_{u,m}^{\text{AP},t})^2 + \Bar{e}_m^2)$ is a constant value over all time slots. Here $E_{u,m}^{\text{AP},t} = \max{\left\{P_m D_u / r_{m,c}, \kappa_m(f_m^{\max})^2D_{u}^tC_{u}^t\right\}}$.

Figures (8)

  • Figure 1: Overview of QA workflow on a quantum annealer platform.
  • Figure 2: System architecture of a MEC-enabled SATIN system.
  • Figure 3: An overview of (a) Single-cut HQCGBD and (b) Multi-cut HQCGBD.
  • Figure 4: The objective function value for each iteration of different HQCGMBD strategies compared to the GBD approach.
  • Figure 5: Cumulative solver accessing time of master problems for GBD and HQCGBDs.
  • ...and 3 more figures

Theorems & Definitions (2)

  • Lemma 1
  • Theorem 1