MARL-based Random Access Scheme for Delay-constrained umMTC in 6G
- Title
- MARL-based Random Access Scheme for Delay-constrained umMTC in 6G
- Author
- 조성현
- Keywords
- delay constraint; multi-agent reinforcement learning; multi-cell; random access
- Issue Date
- 2023-06
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), v. 2023-June, Page. 1.0-6.0
- Abstract
- With the development of IoT technology, 6G defines ultra-massive machine type communication (umMTC) as a core service type. Since umMTC in 6G is composed of a huge number of devices and various IoT service types, an efficient random access (RA) scheme for massive devices is required. We study a scheme that maximizes the successful RA ratio by applying multi-agent reinforcement learning (MARL) in the delay-constrained 6G umMTC environment. We define the necessary information for the optimal RA strategy and describe how to obtain the RA information with machine-type communication device (MTCD) grouping and learning framework. We utilize the QMIX learning framework to solve the non-stationarity problem in MARL and design the learning framework to select optimal RA for each MTCD group. We conduct a simulation to verify the proposed scheme and simulation results show that a successful RA ratio can be improved up to 20% compared to the state-of-the-art in non-uniform device distribution. © 2023 IEEE.
- URI
- https://ieeexplore.ieee.org/document/10200993?arnumber=10200993&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/187892
- ISSN
- 1550-2252
- DOI
- 10.1109/VTC2023-Spring57618.2023.10200993
- Appears in Collections:
- ETC[S] > ETC
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