Cited By
View all- Zheng ZJiang SFeng RGe LGu C(2023)Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference ModelEntropy10.3390/e2501010125:1(101)Online publication date: 3-Jan-2023
Using Reinforcement Learning in Slotted Aloha for Ad-Hoc Networks
Pages 245 - 252
Abstract
Slotted ALOHA is known to have poor channel utilization (a maximum of 37% when average offered load is one packet per time slot). Reinforcement learning has recently been proposed as a technique that allows nodes to learn to coordinate their transmissions in order to attain much higher network utilization. All reinforcement learning schemes proposed to date assume immediate feedback on the outcome of a packet transmission. We introduce ALOHA-dQT, a reinforcement-learning protocol that achieves high utilization by having nodes broadcast short summaries of the channel history as known to them along with their packets. Our simulation results show that ALOHA-dQT leads to network utilization above 75%, with fair bandwidth allocation among nodes. ALOHA-dQT is the first reinforcement-learning approach applied to slotted ALOHA suitable for ad-hoc networks without centralized repeaters.
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Index Terms
- Using Reinforcement Learning in Slotted Aloha for Ad-Hoc Networks
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Published In
November 2020
278 pages
ISBN:9781450381178
DOI:10.1145/3416010
- General Chairs:
- Monica Aguilar Igartua,
- Paolo Bellavista,
- Program Chairs:
- Antonio A.F. Loureiro,
- Carlo Giannelli
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This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 16 November 2020
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MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
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View all- Zheng ZJiang SFeng RGe LGu C(2023)Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference ModelEntropy10.3390/e2501010125:1(101)Online publication date: 3-Jan-2023
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