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Quantum-Assisted Joint Caching and Power Allocation for Integrated Satellite-Terrestrial Networks

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

TL;DR

This work addresses caching and content delivery in an integrated satellite-terrestrial network (ISTN) by maximizing the total network throughput $Q^T$ through joint optimization of content delivery policy, cache placement, and transmission power. It introduces a hybrid quantum-classical generalized Benders' decomposition (HQCGBD) that decomposes the MINLP into a convex subproblem solved classically and a master problem reformulated as a QUBO for quantum annealing, with a multi-cut strategy to accelerate convergence. The approach demonstrates faster convergence and significant reductions in master-solver time compared with classical CBD, while achieving near-optimal throughput on a small-scale test setup. This quantum-assisted optimization pipeline holds potential to tackle large-scale ISTN resource management problems and could impact future 6G content delivery and network planning.

Abstract

Low earth orbit (LEO) satellite network can complement terrestrial networks for achieving global wireless coverage and improving delay-sensitive Internet services. This paper proposes an integrated satellite-terrestrial network (ISTN) architecture to provide ground users with seamless and reliable content delivery services. For optimal service provisioning in this architecture, we formulate an optimization model to maximize the network throughput by jointly optimizing content delivery policy, cache placement, and transmission power allocation. The resulting optimization model is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computer solvers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithm to address this challenge. Specifically, we first exploit the generalized Benders' decomposition (GBD) to decompose the problem into a master problem and a subproblem and then leverage the state-of-art quantum annealer to solve the challenging master problem.

Quantum-Assisted Joint Caching and Power Allocation for Integrated Satellite-Terrestrial Networks

TL;DR

This work addresses caching and content delivery in an integrated satellite-terrestrial network (ISTN) by maximizing the total network throughput through joint optimization of content delivery policy, cache placement, and transmission power. It introduces a hybrid quantum-classical generalized Benders' decomposition (HQCGBD) that decomposes the MINLP into a convex subproblem solved classically and a master problem reformulated as a QUBO for quantum annealing, with a multi-cut strategy to accelerate convergence. The approach demonstrates faster convergence and significant reductions in master-solver time compared with classical CBD, while achieving near-optimal throughput on a small-scale test setup. This quantum-assisted optimization pipeline holds potential to tackle large-scale ISTN resource management problems and could impact future 6G content delivery and network planning.

Abstract

Low earth orbit (LEO) satellite network can complement terrestrial networks for achieving global wireless coverage and improving delay-sensitive Internet services. This paper proposes an integrated satellite-terrestrial network (ISTN) architecture to provide ground users with seamless and reliable content delivery services. For optimal service provisioning in this architecture, we formulate an optimization model to maximize the network throughput by jointly optimizing content delivery policy, cache placement, and transmission power allocation. The resulting optimization model is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computer solvers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithm to address this challenge. Specifically, we first exploit the generalized Benders' decomposition (GBD) to decompose the problem into a master problem and a subproblem and then leverage the state-of-art quantum annealer to solve the challenging master problem.
Paper Structure (22 sections, 23 equations, 8 figures, 2 tables, 2 algorithms)

This paper contains 22 sections, 23 equations, 8 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. ISTN System
  4. Quantum Annealing
  5. System Model And Problem Formulation
  6. System Architecture
  7. Cache Model
  8. Communication Model
  9. Problem Formulation
  10. Solution Methodology
  11. Hybrid Quantum-classical Generalized Benders' Decomposition
  12. Classical Optimization for Subproblem
  13. Quantum Optimization for Master Problem
  14. Overall Algorithm
  15. Multi-cut Strategy
  16. ...and 7 more sections

Figures (8)

  • Figure 1: The integrated satellite-terrestrial network system
  • Figure 2: An overview of (a) CBD and (b) Multi-Cut HQCGBD
  • Figure 3: Convergence of the proposed HQCGBD algorithm.
  • Figure 4: The convergence performances of CBD and different multi-cut HQCGBD strategies.
  • Figure 5: The cumulative solver accessing time of master problems for CBD and different multi-cut HQCGBD strategies.
  • ...and 3 more figures