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