Partial Computation Offloading for Double-RIS Assisted Multi-User Mobile Edge Computing Networks

LI Bin; LIU Wenshuai; XIE Wancheng; YE Yinghui

doi:10.11999/JEIT211595

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2309-2316

LI Bin, LIU Wenshuai, XIE Wancheng, YE Yinghui. Partial Computation Offloading for Double-RIS Assisted Multi-User Mobile Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2309-2316. doi: 10.11999/JEIT211595

Citation:

LI Bin, LIU Wenshuai, XIE Wancheng, YE Yinghui. Partial Computation Offloading for Double-RIS Assisted Multi-User Mobile Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2309-2316. doi: 10.11999/JEIT211595

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Partial Computation Offloading for Double-RIS Assisted Multi-User Mobile Edge Computing Networks

doi: 10.11999/JEIT211595

1.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts & Telecommunications, Xi'an 710121, China

Funds: The National Natural Science Foundation of China (62101277), The National Natural Science Foundation of Jiangsu Province (BK20200822), Shaanxi Key Laboratory of Information Communication Network and Security (ICNS202004)

Received Date: 2021-12-29
Rev Recd Date: 2022-03-29

Available Online: 2022-04-19

Publish Date: 2022-07-25

Abstract

Abstract

In order to compensate the performance loss caused by obstacle blocking in Mobile Edge Computing (MEC) system, a partial task offloading framework supported by Reconfigurable Intelligent Surface (RIS) is proposed. Firstly, the influence of the reflection between double-RIS on channel gain is analyzed. Then, a non-convex and multivariable coupling problem for minimization of total energy consumption of all users is formulated by the joint design of the transmit power of users, the offloading rate of users, the amount of offloading task of users, the time slot and the phase shift of RISs. To solve this problem, the alternating optimization technique is invoked to decouple the original non-convex problem into two subproblems which are solved by leveraging the Dinkelbach method and optimally conditions. Numerical results demonstrate that the proposed algorithm converges swiftly and reduces effectively the system energy consumption.
- Reconfigurable Intelligent Surface (RIS),
- Mobile Edge Computing (MEC),
- Partial offloading,
- Resource allocation

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References(21)

References

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