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MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things

Chengxiao Liu, Wei Feng, Xiaoming Tao, Ning Ge

TL;DR

This work tackles the challenge of enabling wide-area, time-sensitive IoT in 6G by pairing MEC with a non-terrestrial network that hierarchically combines satellites and UAVs under a cell-free architecture. It proposes a process-oriented, time-division framework and formulates a non-convex latency minimization problem using large-scale CSI, then decomposes and solves it via block-coordinate descent and successive convex approximation, yielding a robust, iterative resource orchestration scheme. Key findings show that the proposed MEC-empowered NTN achieves lower latency than satellite-only or heuristic schemes, and that predesign of UAV payloads can significantly improve resource efficiency; the framework also demonstrates adaptability to varying data sizes. Overall, integrating NTN with MEC under a process-oriented, segmentation-based approach offers practical latency improvements for wide-area time-sensitive IoT in 6G environments.

Abstract

In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures including satellites and unmanned aerial vehicles (UAVs), where a non-terrestrial network (NTN) can be built under the cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN is required to be empowered by mobile edge computing (MEC) while providing oasis-oriented on-demand coverage for the devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of complex propagation environment in the MEC-empowered NTN, which makes it hard to orchestrate the resources. In this paper, we propose a process-oriented framework to design the communication and MEC systems in a time-division manner. Under this framework, the large-scale channel state information (CSI) is used to characterize the complex propagation environment with an affordable cost, where a non-convex latency minimization problem is formulated. After that, the approximated problem is given and it can be decomposed into subproblems. These subproblems are further solved in an iterative way. Simulation results demonstrate the superiority of the proposed process-oriented scheme over other algorithms. These results also indicate that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of resource use. Furthermore, the results imply that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.

MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things

TL;DR

This work tackles the challenge of enabling wide-area, time-sensitive IoT in 6G by pairing MEC with a non-terrestrial network that hierarchically combines satellites and UAVs under a cell-free architecture. It proposes a process-oriented, time-division framework and formulates a non-convex latency minimization problem using large-scale CSI, then decomposes and solves it via block-coordinate descent and successive convex approximation, yielding a robust, iterative resource orchestration scheme. Key findings show that the proposed MEC-empowered NTN achieves lower latency than satellite-only or heuristic schemes, and that predesign of UAV payloads can significantly improve resource efficiency; the framework also demonstrates adaptability to varying data sizes. Overall, integrating NTN with MEC under a process-oriented, segmentation-based approach offers practical latency improvements for wide-area time-sensitive IoT in 6G environments.

Abstract

In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures including satellites and unmanned aerial vehicles (UAVs), where a non-terrestrial network (NTN) can be built under the cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN is required to be empowered by mobile edge computing (MEC) while providing oasis-oriented on-demand coverage for the devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of complex propagation environment in the MEC-empowered NTN, which makes it hard to orchestrate the resources. In this paper, we propose a process-oriented framework to design the communication and MEC systems in a time-division manner. Under this framework, the large-scale channel state information (CSI) is used to characterize the complex propagation environment with an affordable cost, where a non-convex latency minimization problem is formulated. After that, the approximated problem is given and it can be decomposed into subproblems. These subproblems are further solved in an iterative way. Simulation results demonstrate the superiority of the proposed process-oriented scheme over other algorithms. These results also indicate that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of resource use. Furthermore, the results imply that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.

Paper Structure

This paper contains 18 sections, 2 theorems, 50 equations, 11 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Main Contributions
  4. System Model
  5. Joint Power Allocation and Data Stream Scheduling under the Process-Oriented Framework
  6. Problem Decomposition
  7. The Solution to (\ref{['pro1']})
  8. The Solution to (\ref{['pro2']})
  9. The solution to (\ref{['pro2.1']})
  10. The solution to (\ref{['pro2.2']})
  11. The Solution to (\ref{['pro3']})
  12. Convergence Analysis
  13. Simulation Results and Discussions
  14. Conclusions
  15. Justification of the Ergodic Rate Approximation
  16. ...and 3 more sections

Key Result

Theorem 1

$\hat{R}^U_{u, k, t}(\mathbf{P})$ is concave with respect to $p_{u, t}$ and convex with respect to $\{p_{v, t} \ \ \forall v \neq u\}$. Accordingly, at any given point $\mathbf{P}^0$, we have where

Figures (11)

  • Figure 1: Illustration of an MEC-empowered hierarchical NTN for wide-area time-sensitive IoT.
  • Figure 2: Diagram of the process-oriented framework in the MEC-empowered NTN.
  • Figure 3: Numerical evaluations of the accuracy of approximated ergodic rate.
  • Figure 4: Convergence performances of the proposed algorithms.
  • Figure 5: Comparison between different algorithms when $D$ is small.
  • ...and 6 more figures

Theorems & Definitions (4)

  • Remark 1
  • Theorem 1
  • Theorem 2
  • Remark 2