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Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC.

Huaiwen He1, Xiangdong Yang1,2, Feng Huang1,2

  • 1School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.

Sensors (Basel, Switzerland)
|July 27, 2024
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Summary
This summary is machine-generated.

This study optimizes Unmanned Aerial Vehicle (UAV) networks for better Quality of Experience (QoE) in Multi-access Edge Computing (MEC). It reduces network shrinkage by intelligently managing UAV paths and resource allocation.

Keywords:
large-scale IoT networkmulti-access edge computingshrinkage ratiotask offloadingunmanned aerial vehicle

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Unmanned Aerial Vehicles (UAVs) are crucial for next-generation (B5G/6G) Multi-access Edge Computing (MEC) networks.
  • Optimizing Quality of Experience (QoE) in large-scale UAV-MEC networks is challenging due to complex interdependencies.

Purpose of the Study:

  • To minimize the shrinkage ratio in large-scale UAV-MEC networks.
  • To enhance Quality of Experience (QoE) through optimal decision-making in computation mode, UAV trajectory, bandwidth, and computing resource allocation.

Main Methods:

  • Formulated the problem as a mixed-integer nonlinear programming (MINLP) problem.
  • Proposed a two-tier optimization strategy: UAV partition coverage (Welzl method) and Traveling Salesman Problem (TSP) for trajectory.
  • Developed Coordinate Descent (CD) and Alternating Direction Method of Multipliers (ADMM) for resource allocation.

Main Results:

  • The CD-based method significantly reduces time complexity (by three orders of magnitude) compared to convex optimization.
  • The ADMM-based method achieved an approximate 8% reduction in shrinkage ratio compared to baseline methods.
  • Demonstrated high efficiency and simplicity of the CD-based method in large-scale UAV-MEC networks.

Conclusions:

  • The proposed two-tier optimization strategy effectively addresses challenges in large-scale UAV-MEC networks.
  • The developed methods provide efficient solutions for optimizing UAV trajectory and resource allocation, enhancing QoE.
  • The research offers practical insights for improving performance in future wireless communication systems.