A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs
This study introduces the Multi-Agent, Multi-Parameter, Interaction-Driven Contention Window Optimization (M2I-CWO) algorithm, a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to optimize multiple CW parameters in IEEE 802.11 Wireless LANs. Unlike single-parameter or specia...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | ICT Express |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959525000104 |
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| author | Yi-Hao Tu Yi-Wei Ma |
| author_facet | Yi-Hao Tu Yi-Wei Ma |
| author_sort | Yi-Hao Tu |
| collection | DOAJ |
| description | This study introduces the Multi-Agent, Multi-Parameter, Interaction-Driven Contention Window Optimization (M2I-CWO) algorithm, a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to optimize multiple CW parameters in IEEE 802.11 Wireless LANs. Unlike single-parameter or specialized multi-agent methods, M2I-CWO employs a Dueling-DQN architecture and an Adaptive Interaction Reward Function—spanning independent, cooperative, competitive, and mixed modes—and accommodates Hierarchical Multi-Agent System (HMAS) or Federated RL (FRL) for further scalability. First, multiple CW parameters are simultaneously adjusted to enhance collision management. Second, M2I-CWO consistently achieves throughput improvements in both static and dynamic scenarios. Extensive results confirm M2I-CWO's superiority in efficiency and adaptability. |
| format | Article |
| id | doaj-art-0ab3d35fd08446c8bda03ea3f1c4cf52 |
| institution | OA Journals |
| issn | 2405-9595 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj-art-0ab3d35fd08446c8bda03ea3f1c4cf522025-08-20T02:35:50ZengElsevierICT Express2405-95952025-06-0111347348010.1016/j.icte.2025.01.010A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANsYi-Hao Tu0Yi-Wei Ma1Corresponding authors.; Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan.Corresponding authors.; Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan.This study introduces the Multi-Agent, Multi-Parameter, Interaction-Driven Contention Window Optimization (M2I-CWO) algorithm, a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to optimize multiple CW parameters in IEEE 802.11 Wireless LANs. Unlike single-parameter or specialized multi-agent methods, M2I-CWO employs a Dueling-DQN architecture and an Adaptive Interaction Reward Function—spanning independent, cooperative, competitive, and mixed modes—and accommodates Hierarchical Multi-Agent System (HMAS) or Federated RL (FRL) for further scalability. First, multiple CW parameters are simultaneously adjusted to enhance collision management. Second, M2I-CWO consistently achieves throughput improvements in both static and dynamic scenarios. Extensive results confirm M2I-CWO's superiority in efficiency and adaptability.http://www.sciencedirect.com/science/article/pii/S2405959525000104Adaptive interaction reward functionCW optimizationIEEE 802.11 WLANsM2I-CWO |
| spellingShingle | Yi-Hao Tu Yi-Wei Ma A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs ICT Express Adaptive interaction reward function CW optimization IEEE 802.11 WLANs M2I-CWO |
| title | A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs |
| title_full | A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs |
| title_fullStr | A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs |
| title_full_unstemmed | A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs |
| title_short | A comprehensive multi-agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in IEEE 802.11 Wireless LANs |
| title_sort | comprehensive multi agent deep reinforcement learning framework with adaptive interaction strategies for contention window optimization in ieee 802 11 wireless lans |
| topic | Adaptive interaction reward function CW optimization IEEE 802.11 WLANs M2I-CWO |
| url | http://www.sciencedirect.com/science/article/pii/S2405959525000104 |
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