室内异构多访问边缘计算系统：通道变化感知任务卸载的在线学习

IF 4.4 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-06-09 DOI:10.1109/LCOMM.2025.3577651

Ryangsoo Kim;Sung Chang Kim;Yonggang Kim

{"title":"室内异构多访问边缘计算系统：通道变化感知任务卸载的在线学习","authors":"Ryangsoo Kim;Sung Chang Kim;Yonggang Kim","doi":"10.1109/LCOMM.2025.3577651","DOIUrl":null,"url":null,"abstract":"We investigate task offloading in indoor heterogeneous multi-access edge computing (MEC) systems with cellular and WiFi networks. Due to unpredictable mobile device mobility and spatially varying multipath fading, MEC systems face time-varying wireless channel conditions, making it challenging to make deterministic task offloading decisions. We propose an online learning-based task offloading decision algorithm that enables mobile devices to learn spatially varying channel conditions and optimize task offloading policy over time. Our algorithm minimizes the energy consumption of each mobile device while ensuring maximum task offloading delay guarantees. Numerical simulation results demonstrate the effectiveness of our algorithm.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 8","pages":"1844-1848"},"PeriodicalIF":4.4000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Indoor Heterogeneous Multi-Access Edge Computing Systems: Online Learning for Channel Variation-Aware Task Offloading\",\"authors\":\"Ryangsoo Kim;Sung Chang Kim;Yonggang Kim\",\"doi\":\"10.1109/LCOMM.2025.3577651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate task offloading in indoor heterogeneous multi-access edge computing (MEC) systems with cellular and WiFi networks. Due to unpredictable mobile device mobility and spatially varying multipath fading, MEC systems face time-varying wireless channel conditions, making it challenging to make deterministic task offloading decisions. We propose an online learning-based task offloading decision algorithm that enables mobile devices to learn spatially varying channel conditions and optimize task offloading policy over time. Our algorithm minimizes the energy consumption of each mobile device while ensuring maximum task offloading delay guarantees. Numerical simulation results demonstrate the effectiveness of our algorithm.\",\"PeriodicalId\":13197,\"journal\":{\"name\":\"IEEE Communications Letters\",\"volume\":\"29 8\",\"pages\":\"1844-1848\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Communications Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11028095/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Communications Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11028095/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

我们研究任务卸载在室内异构多接入边缘计算（MEC）系统与蜂窝和WiFi网络。由于不可预测的移动设备移动性和空间变化的多径衰落，MEC系统面临时变的无线信道条件，这使得做出确定性的任务卸载决策具有挑战性。我们提出了一种基于在线学习的任务卸载决策算法，该算法使移动设备能够学习空间变化的信道条件并随着时间的推移优化任务卸载策略。我们的算法使每个移动设备的能耗最小化，同时保证最大的任务卸载延迟保证。数值仿真结果验证了算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Indoor Heterogeneous Multi-Access Edge Computing Systems: Online Learning for Channel Variation-Aware Task Offloading

We investigate task offloading in indoor heterogeneous multi-access edge computing (MEC) systems with cellular and WiFi networks. Due to unpredictable mobile device mobility and spatially varying multipath fading, MEC systems face time-varying wireless channel conditions, making it challenging to make deterministic task offloading decisions. We propose an online learning-based task offloading decision algorithm that enables mobile devices to learn spatially varying channel conditions and optimize task offloading policy over time. Our algorithm minimizes the energy consumption of each mobile device while ensuring maximum task offloading delay guarantees. Numerical simulation results demonstrate the effectiveness of our algorithm.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.