MARL-Based Access Control for Grant-Free Non-Orthogonal Random Access in UDN
- Authors
- Youn, Jiseung; Park, Joohan; Kim, Soohyeong; Ahn, Seyoung; Kim, Yushin; Kim, Donghyun; Cho, Sunghyun
- Issue Date
- May-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Internet of things (IoT); Grant-free random access; umMTC; NOMA; UDN; multi-agent reinforcement learning
- Citation
- IEEE Internet of Things Journal, pp 1 - 16
- Pages
- 16
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Internet of Things Journal
- Start Page
- 1
- End Page
- 16
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/119464
- DOI
- 10.1109/JIOT.2024.3404418
- ISSN
- 2327-4662
- Abstract
- This study addresses the challenge of high power collision rates in Grant-Free Non-Orthogonal Random Access (GF-NORA) for ultra-massive machine-type communication (umMTC) in ultra-dense networks (UDN). We analyze the impact of power collision and inter-cell interference, defining the key factors affecting successive interference cancellation (SIC) decoding failure. To tackle power collision problem, we propose a multi-agent reinforcement learning (MARL) framework, QMIX algorithm, with joint optimization of access control and power-level design. We evaluate the performance of the proposed scheme with extensive random access simulations in an umMTC environment. Our approach outperforms state-of-the-art schemes, achieving at most 10% increase in successful SIC decoding rate with lower access delay.
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