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Stochastic Geometry-Based Performance Evaluation for LEO Satellite-Assisted Space Caching

Chunyi Ma, Jiajie Xu, Jianhua Yang, Mustafa A. Kishk

TL;DR

The paper addresses the performance of mobile edge computing in remote and maritime regions by introducing space caching on LEO satellites and analyzing the integrated terrestrial-satellite network with stochastic geometry. It builds a tractable multi-tier model where SATs (with BPP on a sphere) and terrestrial CSs (PPP) serve diverse tasks, derives downlink/uplink coverage probabilities and association probabilities, and computes average delay via queuing theory under an orthogonal bandwidth allocation. The results show that SAT space caching can substantially reduce average delay and that SAT altitude and constellation size significantly influence performance, offering practical design guidance for SAT deployments in MEC. The framework enables system-level planning for SAT density, altitude, and caching strategies to achieve global low-latency MEC services.

Abstract

To achieve the Internet of Things (IoT) vision,Mobile Edge Computing (MEC) is a promising technology aimed at providing low-latency computing services to user equipment (UE). However, terrestrial MEC network struggles to provide service to UEs in remote and maritime region. Low Earth Orbit (LEO) satellite networks have the potential to overcome geographical restrictions and provide seamless global coverage for UEs. In this paper, we provide the first attempt to use stochastic geometry to investigate the performance of implementing space caching with LEO satellites (SATs) in the MEC network. We study a LEO satellite-assisted space caching MEC network, and LEO SATs can be equipped with servers to enable space caching, with the advantage of seamless coverage to assist terrestrial CSs for serving UEs in remote or maritime reigon. Using stochastic geometry and queuing theory, we establish an analytical framework for this MEC network. Meanwhile, we develop association strategies for UEs to connect with LEO SATs or CSs and utilize stochastic geometry to derive uplink and downlink coverage probabilities, considering the diversity of task and service types. On this basis, we employ the queuing theory to calculate the average delay to evaluate the system performance. Through Monte Carlo simulations and numerical results, the system performance is evaluated. The results show the potential of SAT spatial caching in improving the performance of the MEC network. Additionally, our results reveal useful insights such as the significant impact of the altitude and number of LEO SATs on the average delay of the network, providing helpful system-level recommendations for the design and configuration of the space-caching MEC network.

Stochastic Geometry-Based Performance Evaluation for LEO Satellite-Assisted Space Caching

TL;DR

The paper addresses the performance of mobile edge computing in remote and maritime regions by introducing space caching on LEO satellites and analyzing the integrated terrestrial-satellite network with stochastic geometry. It builds a tractable multi-tier model where SATs (with BPP on a sphere) and terrestrial CSs (PPP) serve diverse tasks, derives downlink/uplink coverage probabilities and association probabilities, and computes average delay via queuing theory under an orthogonal bandwidth allocation. The results show that SAT space caching can substantially reduce average delay and that SAT altitude and constellation size significantly influence performance, offering practical design guidance for SAT deployments in MEC. The framework enables system-level planning for SAT density, altitude, and caching strategies to achieve global low-latency MEC services.

Abstract

To achieve the Internet of Things (IoT) vision,Mobile Edge Computing (MEC) is a promising technology aimed at providing low-latency computing services to user equipment (UE). However, terrestrial MEC network struggles to provide service to UEs in remote and maritime region. Low Earth Orbit (LEO) satellite networks have the potential to overcome geographical restrictions and provide seamless global coverage for UEs. In this paper, we provide the first attempt to use stochastic geometry to investigate the performance of implementing space caching with LEO satellites (SATs) in the MEC network. We study a LEO satellite-assisted space caching MEC network, and LEO SATs can be equipped with servers to enable space caching, with the advantage of seamless coverage to assist terrestrial CSs for serving UEs in remote or maritime reigon. Using stochastic geometry and queuing theory, we establish an analytical framework for this MEC network. Meanwhile, we develop association strategies for UEs to connect with LEO SATs or CSs and utilize stochastic geometry to derive uplink and downlink coverage probabilities, considering the diversity of task and service types. On this basis, we employ the queuing theory to calculate the average delay to evaluate the system performance. Through Monte Carlo simulations and numerical results, the system performance is evaluated. The results show the potential of SAT spatial caching in improving the performance of the MEC network. Additionally, our results reveal useful insights such as the significant impact of the altitude and number of LEO SATs on the average delay of the network, providing helpful system-level recommendations for the design and configuration of the space-caching MEC network.

Paper Structure

This paper contains 23 sections, 6 theorems, 59 equations, 12 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Work
  4. Contribution
  5. System model
  6. Network Model
  7. Channel model
  8. U-S Link
  9. U-C Link
  10. S-U Link
  11. C-U Link
  12. Association Strategy
  13. Service and Computation Model
  14. Coverage Analysis
  15. Downlink Analysis
  16. ...and 8 more sections

Key Result

Lemma 1

Contact Distance Distribution of S-U link. The CDF and PDF of the distance ${D_{d,S-U}}$ between the nearest offloadable type-$i$ SAT and the typical UE is talgat2020stochastic and the PDF of distance ${D_{d,S-U}}$ is where, ${d_{\max }}= \sqrt {2r_ea_s + a_s^2}$.

Figures (12)

  • Figure 1: Schematic diagram of the network system.
  • Figure 2: S-U downlink geometry diagram.
  • Figure 3: U-S uplink geometry diagram.
  • Figure 4: Effect of $a_s$.
  • Figure 5: Comparison of average delay of our system versus that of the individual server network.
  • ...and 7 more figures

Theorems & Definitions (7)

  • Lemma 1
  • Proposition 1
  • Lemma 2
  • Lemma 3
  • Lemma 4
  • Lemma 5
  • Definition 1