Abstract
In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the noncooperative scheme.
Original language | English |
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Title of host publication | Proceedings of the 2019 2019 IEEE Global Communications Conference (GLOBECOM), December 9 – 13, in Waikoloa, Hawaii, USA |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
ISBN (Electronic) | 9781728109626 |
ISBN (Print) | 9781728109633 |
DOIs | |
Publication status | Published - Dec 2019 |
Event | 2019 IEEE Global Communications Conference - Waikoloa, Big Island, Hawaii, United States Duration: 9 Dec 2019 → 13 Dec 2019 https://globecom2019.ieee-globecom.org/ |
Conference
Conference | 2019 IEEE Global Communications Conference |
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Abbreviated title | GLOBECOM 2019 |
Country/Territory | United States |
Period | 9/12/19 → 13/12/19 |
Internet address |
Keywords
- Task analysis
- servers
- power demand
- device-to-device communication
- Mobile handsets
- computational modeling
- edge computing