JOURNAL ARTICLE

Resource Allocation for Hybrid NOMA MEC Offloading

Jianyue ZhuJiaheng WangYongming HuangFang FangKeivan NavaieZhiguo Ding

Year: 2020 Journal:   IEEE Transactions on Wireless Communications Vol: 19 (7)Pages: 4964-4977   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as promising technologies for the beyond fifth generation networks to achieve significant capacity improvement and delay reduction. In this paper, the technologies of hybrid NOMA and MEC are integrated. In the hybrid NOMA MEC system, multiple users are classified into different groups and each group is allocated a dedicated time slot. In each group, a user first offloads its task by sharing a time slot with another user, and then solely offloads during a time interval. To reduce the delay and save the energy consumption, we consider jointly optimizing the power and time allocation in each group as well as the user grouping. As the main contribution, the optimal power and time allocation is characterized in closed form. In addition, by incorporating the matching algorithm with the optimal power and time allocation, we propose a low complexity method to efficiently optimize user grouping. Simulation results demonstrate that the proposed resource allocation method in the hybrid NOMA MEC systems not only yields better performance than the conventional OMA scheme but also achieves quite close performance as global optimal solution.

Keywords:
Computer science Noma Mobile edge computing Resource allocation Resource management (computing) Reduction (mathematics) Computer network Enhanced Data Rates for GSM Evolution Distributed computing Telecommunications link Real-time computing Mathematical optimization Server Telecommunications Mathematics

Metrics

94
Cited By
6.38
FWCI (Field Weighted Citation Impact)
41
Refs
0.98
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Wireless Communication Technologies
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications
IoT Networks and Protocols
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
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