Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing

Han HU; Nan BAO; Zhang LING; Le SHEN

doi:10.11999/JEIT200898

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3563-3570

Han HU, Nan BAO, Zhang LING, Le SHEN. Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3563-3570. doi: 10.11999/JEIT200898

Citation:

Han HU, Nan BAO, Zhang LING, Le SHEN. Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3563-3570. doi: 10.11999/JEIT200898

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Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing

doi: 10.11999/JEIT200898

Han HU^{1, 2
,
,},
Nan BAO¹,
Zhang LING²,
Le SHEN²

1.
College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (61871446, 61801244), The National Science Foundation Program of Jiangsu Province (BK20191378), The National Science Research Project of Jiangsu Higher Education Institutions (18KJB510034)

Received Date: 2020-10-20
Rev Recd Date: 2021-04-29

Available Online: 2021-11-10

Publish Date: 2021-12-21

Abstract

Abstract

Combing Mobile Edge Computing (MEC) and Non-Orthogonal Multiple Access (NOMA) technologies while considering fairness, this paper studies the fair energy efficiency of the MEC system using NOMA partial offloading. First, the ratio of user rate to power consumption based on the fair function is defined as the fair energy efficiency function. Then, two energy efficiency scheduling algorithms under the fair energy efficiency scheduling criteria are proposed, namely the DK-SCA algorithm under the maximum-minimum rate criterion and the DK-SCALE algorithm under the maximum system energy efficiency criterion. The optimal CPU-frequency cycle and optimal transmit power under these two fair energy efficiency scheduling criteria are obtained, respectively. Finally, simulations show that compared with the benchmark schemes, the proposed NOMA -based partial offloading scheme can effectively combine local computing with edge offloading based on NOMA, which can achieve the best fair energy efficiency performance.
- Edge computing,
- Computing offloading,
- Non-Orthogonal Multiple Access (NOMA),
- Energy efficiency,
- Fair

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References(17)

References

[1]	BARBERA M V, KOSTA S, MEI A, et al. To offload or not to offload? The bandwidth and energy costs of mobile cloud computing[C]. Proceedings - IEEE INFOCOM, Turin, Italy, 2013: 1285–1293. doi: 10.1109/INFCOM.2013.6566921.
[2]	TAN L T, HU R Q, and HANZO L. Twin-timescale artificial intelligence aided mobility-aware edge caching and computing in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3086–3099. doi: 10.1109/TVT.2019.2893898
[3]	TAN L T and HU R Q. Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10190–10203. doi: 10.1109/TVT.2018.2867191
[4]	HU Han, SONG Weiwei, WANG Qun, et al. Mobility-aware offloading and resource allocation in MEC-enabled IoT networks[C]. Proceedings of the 16th International Conference on Mobility, Sensing and Networking (MSN), Tokyo, Japan, 2020: 554–560. doi: 10.1109/MSN50589.2020.00092.
[5]	ZHANG Yuan, DU Peng, WANG Jiang, et al. Resource scheduling for delay minimization in multi-server cellular edge computing systems[J]. IEEE Access, 2019, 7(99): 86265–86273. doi: 10.1109/ACCESS.2019.2924032
[6]	CHEN Ying, ZHANG Ning, ZHANG Yongchao, et al. Energy efficient dynamic offloading in mobile edge computing for internet of things[J]. IEEE Transactions on Cloud Computing, 2021, 9(3): 1050–1060. doi: 10.1109/TCC.2019.2898657
[7]	TRAN T X and POMPILI D. Joint task offloading and resource allocation for multi-server mobile-edge computing networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 856–868. doi: 10.1109/TVT.2018.2881191
[8]	SUN Haijian, ZHOU Fuhui, and HU R Q. Joint offloading and computation energy efficiency maximization in a mobile edge computing system[J]. IEEE Transactions on Vehicular Technology, 2019, 8,68(3): 3052–3056. doi: 10.1109/TVT.2019.2893094
[9]	YANG Zhaohui, PAN Cunhua, HOU Jiancao, et al. Efficient resource allocation for mobile-edge computing networks with NOMA: Completion time and energy minimization[J]. IEEE Transactions on Communications, 2019, 67(11): 7771–7784. doi: 10.1109/TCOMM.2019.2935717
[10]	ZENG Ming, NGUYEN N P, DOBRE O A, et al. Delay minimization for NOMA-assisted MEC under power and energy constraints[J]. IEEE Wireless Communications Letters, 2019, 8(6): 1657–1661. doi: 10.1109/LWC.2019.2934453
[11]	PAN Yijin, CHEN Ming, YANG Zhaohui, et al. Energy-efficient NOMA-based mobile edge computing offloading[J]. IEEE Communications Letters, 2019, 23(2): 310–313. doi: 10.1109/LCOMM.2018.2882846
[12]	PHAM Q V and HWANG W J. Fairness-aware spectral and energy efficiency in spectrum-sharing wireless networks[J]. IEEE Transactions on Vehicular Technology, 2017, 66(11): 10207–10219. doi: 10.1109/TVT.2017.2717926
[13]	MO J and WALRAND J. Fair end-to-end window-based congestion control[J]. IEEE/ACM Transactions on Networking, 2000, 8(5): 556–567. doi: 10.1109/90.879343
[14]	GUO Chongtao, ZHANG Yan, SHENG Min, et al. Alphaα-fair power allocation in spectrum-sharing networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(5): 3771–3777. doi: 10.1109/TVT.2015.2436068
[15]	SCHWARZ S, MEHLFUHRER C, and RUPP M. Throughput maximizing multiuser scheduling with adjustable fairness[C]. IEEE International Conference on Communications (ICC), Kyoto, Japan, 2011: 1–5. doi: 10.1109/icc.2011.5963489.
[16]	YU Ye, BU Xiangyuan, YANG Kai, et al. Green large-scale fog computing resource allocation using joint benders decomposition, dinkelbach Algorithm, ADMM, and branch-and-bound[J]. IEEE Internet of Things Journal, 2019, 6(3): 4106–4117. doi: 10.1109/JIOT.2018.2875587
[17]	WEI Zhiqiang, ZHAO Lou, GUO Jiajia, et al. Multi-beam NOMA for hybrid mmWave systems[J]. IEEE Transactions on Communications, 2019, 67(2): 1705–1719. doi: 10.1109/TCOMM.2018.2879930