Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

By Zhaohui Yang, Cunhua Pan, Kezhi Wang, Mohammad Shikh-Bahaei. IEEE Transactions on Wireless Communications, 2019

324 citations recorded by the source corpus.

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ef26c3fd47fa4296dc3c4baca46fea561f57e7a0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing (2020, background): “By providing both computing and storage hardware at the 2 network edges, namely at the BSs or access points (APs), the resource-limited UEs have the option of offloading their computation-intensive and latency-critical applications to the MEC servers [19].”
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks (2021, methodology): “Taking the flexibility advantage of UAVs, mounting MEC servers in UAVs can help to address the above issues by dispatching the MEC-mounted UAVs to assist the designated BSs [11], [20].”
Terahertz Band: The Last Piece of RF Spectrum Puzzle for Communication Systems (2019, background): “In addition, the authors in [282] considered resource allocation in a UAV-enabled MEC network with multiple UAVs.”
Resource Scheduling in Edge Computing: A Survey (2021, background): “in [50] considered a UAV-enabled mobile edge computing (MEC) network, where the computation tasks from MUs can be processed by”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “Internet of Vehicles √ (c) [152] √ (c) [53] √ √ (b) [153] √ (b) [157]”
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future (2020, background): “In a MEC network formed by multiple UAVs, the sum power minimization problem was considered in [234].”