用于优化无线多接入边缘计算系统计算延迟的深度强化学习：部分卸载方法

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks Pub Date : 2025-07-17 DOI:10.1016/j.adhoc.2025.103971

Bowen Huang , Xiaolong Chen , Jianqing Li , Hongfei Guo , Mohammed Atiquzzaman , Jindan Zhang

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引用次数: 0

摘要

无线电力传输与多接入边缘计算（MEC）的集成对于下一代无线网络至关重要，但用户激增对超低延迟提出了挑战。本研究考察了采用部分卸载策略的无线供电MEC网络。本研究的目的是设计一种在线算法，以最优地管理任务卸载和资源管理，适应动态信道条件。为了实现这一点，我们设计了一个深度强化在线卸载与两阶段优化（DROO-TSO）框架。该框架旨在预测部分卸载比率，优化充电时间和资源管理。实验结果表明，DROO-TSO在GPU和CPU平台上的执行时间都达到了亚毫秒。与基于ddpg的基线相比，DROO-TSO在自适应收敛于环境优化策略的同时，将总计算延迟降低了21.49%。算法运行时间和总计算延迟均满足严格的低延迟要求，验证了其在动态无线供电MEC网络中的能力。

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Deep reinforcement learning for optimizing computation latency in wireless-powered Multi-Access Edge Computing systems: A partial offloading approach

The integration of wireless power transfer with multi-access edge computing (MEC) is critical for next-generation wireless networks, yet the surge in users challenges ultra-low latency. This study examines a wireless-powered MEC network that employs a partial offloading strategy. The aim of this research is to devise an online algorithm that optimally manages task offloading and resource management, adapting to dynamic channel conditions. To achieve this, we design a Deep Reinforcement Online Offloading with Two-Stage Optimization (DROO-TSO) framework. This framework is aimed at predicting partial offloading ratios and optimizing charging time and resource management. Empirical results show DROO-TSO achieves sub-millisecond execution times on both GPU and CPU platforms. Compared to DDPG-based baselines, DROO-TSO reduces the total computation delay by 21.49% while adaptively converging to environment-optimized strategies. Both algorithm runtime and the total computation delay meet stringent low-latency requirements, validating its capability in dynamic wireless-powered MEC networks.

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来源期刊

Ad Hoc Networks 工程技术-电信学

CiteScore

10.20

自引率

4.20%

发文量

131

审稿时长

4.8 months

期刊介绍： The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.