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Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks

Gyu Seon Kim, Yeryeong Cho, Jaehyun Chung, Soohyun Park, Soyi Jung, Zhu Han, Joongheon Kim

TL;DR

A quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency and is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

Abstract

Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks

TL;DR

A quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency and is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

Abstract

Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

Paper Structure

This paper contains 22 sections, 1 theorem, 26 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Related Work
  4. Quantum Neural Network
  5. QMARL for Scheduling
  6. Modeling
  7. Global SAGIN Access Scheduling Modeling
  8. HALE-UAV
  9. CubeSat
  10. Two Line Element (TLE)
  11. Orbital Elements of CubeSats
  12. Latitude and Longitude of CubeSat
  13. Distance between GS and CubeSat
  14. Problem Formulation and Algorithm Design
  15. Main Objective for Global SAGIN Mobile Access
  16. ...and 7 more sections

Key Result

Lemma 1

The distance between $G_i$ and $S^i_j$, varies over time due to the updated latitude and longitude of the CubeSat. It can be formulated as, where $H^i_j(t)$ and $V^i_j(t)$ represent the respective horizontal and vertical distances between $G_i$ and $S_j^i$, and note that $V^i_j(t)$ indicates the altitude of $S^i_j$ relative to $G_i$. Then, where $p^\phi_i(t)$ and $p^\lambda_i(t)$ denote the lati

Figures (9)

  • Figure 1: Reference network model.
  • Figure 2: Qubit and QNN architecture.
  • Figure 3: Flight aerodynamics of HALE-UAV.
  • Figure 4: TLE configuration of the satellite used in the experiment..
  • Figure 5: Orbital elements of CubeSat and the geometric relationship of great circle distance between two CubeSats.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Lemma 1
  • proof