Joint Wireless Source Management and Task Offloading in Ultra-Dense Network
The ultra-dense network (UDN) based on mobile edge computing (MEC) is an important technology, which can achieve the low-latency of 5G communications and enhance the quality of user experience. However, how to improve the task offloading efficiency is a hot topic of UDN under the constraint on the l...
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doaj-f46fa0b809974dfd94e28358af6a854c2021-03-30T01:23:18ZengIEEEIEEE Access2169-35362020-01-018529175292610.1109/ACCESS.2020.29800329032155Joint Wireless Source Management and Task Offloading in Ultra-Dense NetworkShanchen Pang0https://orcid.org/0000-0002-5705-1218Shuyu Wang1https://orcid.org/0000-0003-4731-6443College of Computer Science and Technology, China University of Petroleum, Qingdao, ChinaCollege of Computer Science and Technology, China University of Petroleum, Qingdao, ChinaThe ultra-dense network (UDN) based on mobile edge computing (MEC) is an important technology, which can achieve the low-latency of 5G communications and enhance the quality of user experience. However, how to improve the task offloading efficiency is a hot topic of UDN under the constraint on the limited wireless resources. In this article, we propose a heuristic task offloading algorithm HTOA to optimize the delay and energy consumption of offloading tasks in UDN. Firstly, a convex programming model for MEC resource allocation is established, which aims to obtain the optimal allocation set of resources for offloading tasks, and optimize the execution delay of offloading tasks. Followed by, the problem of joint channel allocation and user upload power control is solved by the greedy strategy and golden section method, which aims to optimization the delay and energy consumption of task upload data. Compared with the random task offloading algorithm, numerical simulations show that the algorithm HTOA can effectively reduce the delay and energy consumption of task offloading, and perform better as the number of users increases.https://ieeexplore.ieee.org/document/9032155/Ultra-dense network (UDN)mobile edge computing (MEC)task offloading |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shanchen Pang Shuyu Wang |
spellingShingle |
Shanchen Pang Shuyu Wang Joint Wireless Source Management and Task Offloading in Ultra-Dense Network IEEE Access Ultra-dense network (UDN) mobile edge computing (MEC) task offloading |
author_facet |
Shanchen Pang Shuyu Wang |
author_sort |
Shanchen Pang |
title |
Joint Wireless Source Management and Task Offloading in Ultra-Dense Network |
title_short |
Joint Wireless Source Management and Task Offloading in Ultra-Dense Network |
title_full |
Joint Wireless Source Management and Task Offloading in Ultra-Dense Network |
title_fullStr |
Joint Wireless Source Management and Task Offloading in Ultra-Dense Network |
title_full_unstemmed |
Joint Wireless Source Management and Task Offloading in Ultra-Dense Network |
title_sort |
joint wireless source management and task offloading in ultra-dense network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The ultra-dense network (UDN) based on mobile edge computing (MEC) is an important technology, which can achieve the low-latency of 5G communications and enhance the quality of user experience. However, how to improve the task offloading efficiency is a hot topic of UDN under the constraint on the limited wireless resources. In this article, we propose a heuristic task offloading algorithm HTOA to optimize the delay and energy consumption of offloading tasks in UDN. Firstly, a convex programming model for MEC resource allocation is established, which aims to obtain the optimal allocation set of resources for offloading tasks, and optimize the execution delay of offloading tasks. Followed by, the problem of joint channel allocation and user upload power control is solved by the greedy strategy and golden section method, which aims to optimization the delay and energy consumption of task upload data. Compared with the random task offloading algorithm, numerical simulations show that the algorithm HTOA can effectively reduce the delay and energy consumption of task offloading, and perform better as the number of users increases. |
topic |
Ultra-dense network (UDN) mobile edge computing (MEC) task offloading |
url |
https://ieeexplore.ieee.org/document/9032155/ |
work_keys_str_mv |
AT shanchenpang jointwirelesssourcemanagementandtaskoffloadinginultradensenetwork AT shuyuwang jointwirelesssourcemanagementandtaskoffloadinginultradensenetwork |
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