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Enhancing Reliability in LEO Satellite Networks via High-Speed Inter-Satellite Links

Jinho Choi

TL;DR

This work addresses buffer overflow in store-and-forward LEO satellite networks with finite on-board buffers by leveraging high-speed inter-satellite links (ISLs) to reallocate packets across satellites. It introduces the minimum queue-length allocation via ISL (MQLA-ISL) policy to optimally balance per-satellite queues and analyzes a virtual-queue model that shows the aggregated QoS exponent scales as $\bar{\theta}^* = L\,\theta^*$ under equal-queue-length averaging. The paper derives a practical reallocation framework and demonstrates, through simulations, substantial reductions in the required per-satellite buffer size to meet target overflow probabilities, with performance approaching the ideal virtual-queue benchmark as resources increase. These results highlight ISLs as a viable mechanism to improve reliability and efficiency in global NTN IoRT deployments.

Abstract

Low Earth orbit (LEO) satellites play a crucial role in providing global connectivity for non-terrestrial networks (NTNs) and supporting various Internet-of-Remote-Things (IoRT) applications. Each LEO satellite functions as a relay node in the sky, employing store-and-forward transmission strategies that necessitate the use of buffers. However, due to the finite size of these buffers, occurrences of buffer overflow leading to packet loss are inevitable. In this paper, we demonstrate how inter-satellite links (ISLs) can mitigate the probability of buffer overflow. Specifically, we propose an approach to reallocate packets among LEO satellites via ISLs to minimize the occurrence of buffer overflow events. Consequently, the implementation of ISLs can lead to a more reliable satellite network, enabling efficient packet reallocation to reduce the probability of buffer overflow.

Enhancing Reliability in LEO Satellite Networks via High-Speed Inter-Satellite Links

TL;DR

This work addresses buffer overflow in store-and-forward LEO satellite networks with finite on-board buffers by leveraging high-speed inter-satellite links (ISLs) to reallocate packets across satellites. It introduces the minimum queue-length allocation via ISL (MQLA-ISL) policy to optimally balance per-satellite queues and analyzes a virtual-queue model that shows the aggregated QoS exponent scales as under equal-queue-length averaging. The paper derives a practical reallocation framework and demonstrates, through simulations, substantial reductions in the required per-satellite buffer size to meet target overflow probabilities, with performance approaching the ideal virtual-queue benchmark as resources increase. These results highlight ISLs as a viable mechanism to improve reliability and efficiency in global NTN IoRT deployments.

Abstract

Low Earth orbit (LEO) satellites play a crucial role in providing global connectivity for non-terrestrial networks (NTNs) and supporting various Internet-of-Remote-Things (IoRT) applications. Each LEO satellite functions as a relay node in the sky, employing store-and-forward transmission strategies that necessitate the use of buffers. However, due to the finite size of these buffers, occurrences of buffer overflow leading to packet loss are inevitable. In this paper, we demonstrate how inter-satellite links (ISLs) can mitigate the probability of buffer overflow. Specifically, we propose an approach to reallocate packets among LEO satellites via ISLs to minimize the occurrence of buffer overflow events. Consequently, the implementation of ISLs can lead to a more reliable satellite network, enabling efficient packet reallocation to reduce the probability of buffer overflow.
Paper Structure (9 sections, 19 equations, 7 figures)

This paper contains 9 sections, 19 equations, 7 figures.

Figures (7)

  • Figure 1: LEO satellites in an orbit.
  • Figure 2: An illustration of satellite communication with ground users and ground gateway.
  • Figure 3: An inter-connected LEO satellite network with individual buffers, where dashed lines represent ISLs.
  • Figure 4: The probability of buffer overflow versus threshold, $\tau$, when $(\alpha, \beta) = (0.7, 0.3)$, $\lambda = 10$, $c = 16$, and $L = 10$.
  • Figure 5: Probability of buffer overflow versus $c$ when $(\alpha, \beta) = (0.7, 0.3)$, $\lambda = 10$, and $L = 10$: (a) $q_{\rm max} = 15$; (b) $q_{\rm max} = 30$.
  • ...and 2 more figures