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Journal Article Indoor Heterogeneous Multi-Access Edge Computing Systems: Online Learning for Channel Variation-Aware Task Offloading
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Authors
Ryangsoo Kim, Sung Chang Kim, Yonggang Kim
Issue Date
2025-06
Citation
IEEE Communications Letters, v.권호미정, pp.1-5
ISSN
1089-7798
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/LCOMM.2025.3577651
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.
KSP Keywords
Decision algorithm, Delay guarantee, Edge Computing, Learning-based, Maximum task, Mobile devices, Multi-access, Numerical simulation(Trnsys16), Over time, Task offloading, Wi-Fi network