Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach

2025/05/06 by Yue Chen, Chen, Yue, Hui Kang +15 · 3 citations
Computer Science · Engineering · #Battery (electricity) #Computational complexity theory #Convergence (economics) #Distributed Control Multi-Agent Systems #Energy management #Infrared Target Detection Methodologies #Markov decision process #Process (computing) #Reinforcement learning #Resource allocation #Resource management (computing) #UAV Applications and Optimization #Wireless

paper · pdf · doi:10.48550/arxiv.2505.03230

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/05/06 · openalex created_date 2025/10/16 · openalex updated_date 2026/08/05

Abstract

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms.

Citations

• Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks
• Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
• A Correlated Data-Driven Collaborative Beamforming Approach for Energy-efficient IoT Data Transmission
• Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning
• Joint Computation Offloading and Resource Allocation for Maritime MEC with Energy Harvesting
• Optimizing 6G Integrated Sensing and Communications (ISAC) via Expert Networks
• KAN: Kolmogorov-Arnold Networks
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning
• Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning
• A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
• TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing
• Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
• Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue
• Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles
• Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing
• A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
• 6G Internet of Things: A Comprehensive Survey
• Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
• Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks
• Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing
• A Comprehensive Survey on Transfer Learning
• A Comprehensive Survey on Transfer Learning
• Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
• Soft Actor-Critic Algorithms and Applications
• Energy Minimization for Wireless Communication with Rotary-Wing UAV
• Addressing Function Approximation Error in Actor-Critic Methods
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Simple Recurrent Units for Highly Parallelizable Recurrence
• Proximal Policy Optimization Algorithms
• Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks
• Continuous control with deep reinforcement learning
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems
• Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach
• Reinforcement Learning: An Introduction

Cited by

• Diffusion-Model-enhanced Multiobjective Optimization for Improving Forest Monitoring Efficiency in UAV-enabled Internet-of-Things
• Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks
• Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion
• Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions

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