Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System

SHI Liqin; LIU Xuan; LU Guangyue

doi:10.11999/JEIT231033

Volume 46 Issue 7

Jul. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2888-2897

SHI Liqin, LIU Xuan, LU Guangyue. Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2888-2897. doi: 10.11999/JEIT231033

Citation:

SHI Liqin, LIU Xuan, LU Guangyue. Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2888-2897. doi: 10.11999/JEIT231033

Citation:

PDF( 1860 KB)

Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System

doi: 10.11999/JEIT231033

Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts and Telecommunications Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62301421)

Received Date: 2023-09-19
Rev Recd Date: 2024-03-15

Available Online: 2024-04-02

Publish Date: 2024-07-29

Abstract

Abstract

The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users’ data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, closed-form expression of each user’s optimal transmit power is firstly derived. Then the Successive Convex Approximation (SCA) method is used to approximate the non-convex constraints of the formulated problem, and An SCA based efficient iterative algorithm is proposed to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
- Partial compression,
- Resource allocation,
- Energy consumption minimization,
- NOMA

FullText(HTML)

References(14)

References

[1]	SONG Zhiyuan, MA Ruijiang, and XIE Yong. A collaborative task offloading strategy for mobile edge computing in internet of vehicles[C]. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2021: 1379–1384. doi: 10.1109/IAEAC50856.2021.9390817.
[2]	SATRIA D, PARK D, and JO M. Recovery for overloaded mobile edge computing[J]. Future Generation Computer Systems, 2017, 70: 138–147. doi: 10.1016/j.future.2016.06.024.
[3]	PAN Yijin, CHEN Ming, YANG Zhaohui, et al. Energy-efficient NOMA-based mobile edge computing offloading[J]. IEEE Communications Letters, 2018, 23(2): 310–313. doi: 10.1109/LCOMM.2018.2882846.
[4]	ZHANG Siqi, YI Na, and MA Yi. Correlation-based device energy-efficient dynamic multi-task offloading for mobile edge computing[C]. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 2021: 1–5. doi: 10.1109/VTC2021-Spring51267.2021.9448864.
[5]	GAO Mingjin, SHEN Rujing, LI Jun, et al. Computation offloading with instantaneous load billing for mobile edge computing[J]. IEEE Transactions on Services Computing, 2022, 15(3): 1473–1485. doi: 10.1109/TSC.2020.2996764.
[6]	HE Tianmi, WANG Dawei, ZHOU Fuhui, et al. Delay-aware offloading for cooperative NOMA-based near-and-far MEC networks[C]. 2021 IEEE/CIC International Conference on Communications in China (ICCC), Xiamen, China, 2021: 978–983. doi: 10.1109/ICCC52777.2021.9580414.
[7]	SHI Liqin, YE Yinghui, CHU Xiaoli, et al. Computation energy efficiency maximization for a NOMA-based WPT-MEC network[J]. IEEE Internet of Things Journal, 2021, 8(13): 10731–10744. doi: 10.1109/JIOT.2020.3048937.
[8]	REN Jinke, RUAN Yangjun, and YU Guanding. Data transmission in mobile edge networks: Whether and where to compress?[J]. IEEE Communications Letters, 2019, 23(3): 490–493. doi: 10.1109/LCOMM.2019.2894415.
[9]	XU Ding, LI Qun, and ZHU Hongbo. Energy-saving computation offloading by joint data compression and resource allocation for mobile-edge computing[J]. IEEE Communications Letters, 2019, 23(4): 704–707. doi: 10.1109/LCOMM.2019.2897630.
[10]	MHEICH Z and DUPRAZ E. Short length non-binary rate-adaptive LDPC codes for Slepian-Wolf source coding[C]. 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018: 1–5. doi: 10.1109/WCNC.2018.8377291.
[11]	LIU Chenglin. Predictor-based synchronization algorithms for multiple harmonic oscillators with communication delay[C]. 2015 IEEE International Conference on Information and Automation, Lijiang, China, 2015: 1003–1008. doi: 10.1109/ICInfA.2015.7279433.
[12]	GRANT M and BOYD S. CVX: Matlab software for disciplined convex programming[EB/OL]. http://cvxr.com/cvx, 2023.
[13]	BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2009: 104–112.
[14]	LUO Weiran, SHEN Yanyan, YANG Bo, et al. Joint 3-D trajectory and resource optimization in multi-UAV-enabled IoT networks with wireless power transfer[J]. IEEE Internet of Things Journal, 2021, 8(10): 7833–7848. doi: 10.1109/JIOT.2020.3041303.