Project Details
Description
The sixth-generation (6G) network aims to provide seamless global connectivity and high-speed broadband access through the integration of low earth orbit (LEO) satellite communications and the existing terrestrial cellular networks. On the other hand, with the three-dimensional (3D) mobility features, unmanned aerial vehicles (UAV) system can be combined with the development of the multiaccess edge computing (MEC). In particular, MEC-enabled UAVs can be dynamically deployed and moved to deliver the requested files or accomplish many tasks, thereby reducing network delay and task completion time. The design of MEC-enabled UAV system in hybrid satellite-terrestrial networks is more challenging compared with traditional networks. First, the UAV trajectory design should take the mobility of satellites into consideration. Due to the location-dependent wireless channel conditions, it is necessary to jointly optimize the UAV trajectories and access control of ground users to maximize the transmission efficiency. Second, UAV is constrained by its limited battery capacity, storage space, and computing power. Hence, the design of an efficient MEC-enabled UAV system requires a comprehensive consideration of the wireless channel between the UAVs and satellite as well as the power levels of multiple UAVs, which makes the design even more complicated and challenging. Through this project, we hope that we can understand more about the resource allocation and network control of the satellite-UAV integrated 6G system. The practical research outcomes of this project can contribute to the key technologies of satellite on-board systems and terrestrial communication networks.
Status | Finished |
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Effective start/end date | 1/08/22 → 31/07/23 |
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):
Keywords
- edge computing
- non-terrestrial networks
- unmanned aerial vehicle
- reinforcement learning
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