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Edge Information Hub-Empowered 6G NTN: Latency-Oriented Resource Orchestration and Configuration

Yueshan Lin, Wei Feng, Yunfei Chen, Ning Ge, Zhiyong Feng, Yue Gao

TL;DR

The paper tackles rapid disaster-response data upload in MEC-enabled non-terrestrial networks by introducing an Edge Information Hub (EIH) that couples communication, computing, and storage. It formulates a joint optimization over user-UAV bandwidth, UAV-satellite backhaul rate, computing allocation, and data scheduling to minimize upload latency, and proves that the problem can be transformed into a convex form yielding an optimal scheme. A key contribution is the derivation of resource configuration principles under payload constraints, including a closed-form limiting case with $F_{ m total}^{\rm lim}$ and $T^{\rm lim}$ that prescribes when additional computing capacity improves latency. Simulations confirm substantial latency reductions, validate the scheduling and orchestration approach, and provide design insights for payload-constrained edge-capable NTN deployments in future 6G networks.

Abstract

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

Edge Information Hub-Empowered 6G NTN: Latency-Oriented Resource Orchestration and Configuration

TL;DR

The paper tackles rapid disaster-response data upload in MEC-enabled non-terrestrial networks by introducing an Edge Information Hub (EIH) that couples communication, computing, and storage. It formulates a joint optimization over user-UAV bandwidth, UAV-satellite backhaul rate, computing allocation, and data scheduling to minimize upload latency, and proves that the problem can be transformed into a convex form yielding an optimal scheme. A key contribution is the derivation of resource configuration principles under payload constraints, including a closed-form limiting case with and that prescribes when additional computing capacity improves latency. Simulations confirm substantial latency reductions, validate the scheduling and orchestration approach, and provide design insights for payload-constrained edge-capable NTN deployments in future 6G networks.

Abstract

Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.
Paper Structure (23 sections, 6 theorems, 110 equations, 6 figures, 1 table)

This paper contains 23 sections, 6 theorems, 110 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Main Contributions
  4. System Model
  5. Problem Formulation and Proposed Scheme
  6. Joint Data Scheduling and Resource Orchestration
  7. Optimal Data Scheduling
  8. Optimal Resource Orchestration
  9. Resource Configuration
  10. Simulation Results
  11. Conclusions
  12. Proof of Proposition 1
  13. The amount of to-be-computed data and to-be-uploaded data in the storage both grow during the user-UAV transmission, and the former runs out first afterwards.
  14. The amount of to-be-computed data and to-be-uploaded data in the storage both grow during the user-UAV transmission, and the latter runs out first afterwards.
  15. Only the amount of to-be-computed data in the storage grow during the user-UAV transmission.
  16. ...and 8 more sections

Key Result

Proposition 1

The expression of $T_u(B_u,R_u^S,F_u,\eta_u)$ is given by (expr_T), and the expression of $V_u(B_u,R_u^S,F_u,\eta_u)$ is given by (expr_V) in the next page.

Figures (6)

  • Figure 1: Illustration of the EIH-based non-terrestrial network.
  • Figure 2: Data flow diagram within the EIH during the sensing-data upload process.
  • Figure 3: Overall latency comparison among different algorithms with different maximum data size.
  • Figure 4: Overall latency and the minimum storage required varying with the data scheduling variable.
  • Figure 5: Sufficient condition of the configured total computing capability and corresponding overall upload latency.
  • ...and 1 more figures

Theorems & Definitions (12)

  • Proposition 1
  • Proof 1
  • Theorem 1
  • Proof 2
  • Theorem 2
  • Proof 3
  • Theorem 3
  • Proof 4
  • Theorem 4
  • Proof 5
  • ...and 2 more