融合低軌道衛星與智慧型空中基地台之自組織非地面網路系統

Project Details

Description

Non-terrestrial networks (NTNs) systems composed of low earth orbit (LEO) satellites and unmanned airborne vehicles (UAVs) have been proposed by 3GPP as a complementary enhancement to the existing terrestrial communication systems. LEO satellite communication system can extend wireless coverage to under-served zones, which is a key feature for the next generation cellular networks. On the other hand, UAVs can improve transmission efficiency by dynamically adjusting their 3D positions and resource allocation according to the network environment.Nevertheless, due to the mobility and limited communication range, the network topology in UAV relay networks becomes highly dynamic and time-varying, resulting in increased difficulty of network optimization. In this project, we investigate UAVs aided hybrid satellite-terrestrial network, where UAVs are served as mobile relay base stations to assist the communication between the satellite and ground users. We aim to maximize the number of served users by optimizing UAV trajectory and user link selection. To solve the formulated mixed-integer and non-convex optimization problem, we first find the optimal link selection via a designed graph neural network (GNN), and then adjust the UAV locations by using model-free reinforcement learning (RL), alternately. Numerical results demonstrate that our proposed scheme is superior to the state-of-the-art RL algorithms.
StatusFinished
Effective start/end date1/08/2131/07/22

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 17 - Partnerships for the Goals

Keywords

  • LEO satellites
  • Non-terrestrial networks
  • Unmanned aerial vehicles
  • Machine learning

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