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Article

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

by 1,*, 2 and 1
1
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
2
Department of Engineering, Durham University, Durham DH1 3LE, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Benjamin M. Zaidel and Ori Shental
Received: 31 March 2021 / Revised: 6 May 2021 / Accepted: 11 May 2021 / Published: 14 May 2021
Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme. View Full-Text
Keywords: deep reinforcement learning (DRL); multi-access edge computing (MEC); resource allocation; sixth-generation (6G); user grouping deep reinforcement learning (DRL); multi-access edge computing (MEC); resource allocation; sixth-generation (6G); user grouping
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MDPI and ACS Style

Li, H.; Fang, F.; Ding, Z. DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC. Entropy 2021, 23, 613. https://0-doi-org.brum.beds.ac.uk/10.3390/e23050613

AMA Style

Li H, Fang F, Ding Z. DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC. Entropy. 2021; 23(5):613. https://0-doi-org.brum.beds.ac.uk/10.3390/e23050613

Chicago/Turabian Style

Li, Haodong, Fang Fang, and Zhiguo Ding. 2021. "DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC" Entropy 23, no. 5: 613. https://0-doi-org.brum.beds.ac.uk/10.3390/e23050613

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