Computation Offloading and Resource Allocation Optimization for Mobile Edge Computing-Aided UAV-RIS Communications

doi:10.1109/ACCESS.2024.3435483

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Computation Offloading and Resource Allocation Optimization for Mobile Edge Computing-Aided UAV-RIS Communications

2024-01-01

DOI：10.1109/ACCESS.2024.3435483

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Abstract

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The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.

Keywords:

Autonomous aerial vehicles

Optimization

Resource management

Task analysis

6G mobile communication

Wireless networks

Base stations

Edge computing

Reconfigurable intelligent surfaces

Computation offloading

mobile edge computing