Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees
- Title
- Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees
- Author
- 전상운
- Keywords
- Reinforcement learning; deep learning; random access; resource allocation; fairness
- Issue Date
- 2021-06
- Publisher
- IEEE
- Citation
- IEEE Transactions on Wireless Communications IEEE Trans. Wireless Commun. Wireless Communications, IEEE Transactions on. 21(6):3994-4008 Jun, 2022
- Abstract
- We consider a multichannel random access system
in which each user accesses a single channel at each time slot
to communicate with an access point (AP). Users arrive to
the system at random and be activated for a certain period
of time slots and then disappear from the system. Under
such dynamic network environment, we propose a distributed
multichannel access protocol based on multi-agent reinforcement
learning (RL) to improve both throughput and fairness between
active users. Unlike the previous approaches adjusting channel
access probabilities at each time slot, the proposed RL algorithm
deterministically selects a set of channel access policies for several
consecutive time slots. To effectively reduce the complexity of the
proposed RL algorithm, we adopt a branching dueling Q-network
architecture and propose an efficient training methodology for
producing proper Q-values over time-varying user sets. We perform extensive simulations on realistic traffic environments and
demonstrate that the proposed online learning improves both
throughput and fairness compared to the conventional RL
approaches and centralized scheduling policies.
- URI
- https://ieeexplore.ieee.org/document/9619960?arnumber=9619960&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/171623
- ISSN
- 1536-1276; 1558-2248
- DOI
- 10.1109/TWC.2021.3126112
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MILITARY INFORMATION ENGINEERING(국방정보공학과) > Articles
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