Volume 23, Issue 2 (JIAEEE Vol.23 No.2 2026)
Journal of Iranian Association of Electrical and Electronics Engineers 2026, 23(2): 3-15 |
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Tabatabaei S. A Novel Multicast Routing Approach for MRMC Wireless Mesh Networks Using Fuzzy Logic and Reinforcement Learning. Journal of Iranian Association of Electrical and Electronics Engineers 2026; 23 (2) :3-15
URL: http://jiaeee.com/article-1-1815-en.html
URL: http://jiaeee.com/article-1-1815-en.html
Faculty of Multimedia, Tabriz Islamic Art University
Abstract: (32 Views)
In multi-radio multi-channel wireless mesh networks (MR-MC WMNs), the use of multi-radio nodes operating on non-overlapping channels significantly increases network capacity and improves available bandwidth. However, when the number of radios exceeds the number of available channels, channel reuse leads to inevitable link interference, which degrades overall network performance. This study aims to optimize network performance under high traffic conditions and multi-channel environments by proposing a routing algorithm called Reinforcement Learning and Fuzzy Logic-Based Routing Algorithm (RLFLBRA). The algorithm uses three fuzzy input variables interference level, available bandwidth, and hop count to select optimal multicast paths. By incorporating interference awareness into the routing process, the algorithm enables more efficient use of network resources. The effectiveness of the proposed approach is evaluated through simulations using OPNET version 10.5 and compared with benchmark protocols such as CAMF and FB_MR. The results demonstrate significant improvements in key metrics including throughput, packet delivery ratio, and end-to-end delay.
Keywords: Wireless Mesh Networks, Multicasting, Multi-Radio, Multi-Channel, Fuzzy Logic, Reinforcement Learning.
Type of Article: Research |
Subject:
Communication
Received: 2025/04/25 | Accepted: 2026/01/26 | Published: 2026/06/22
Received: 2025/04/25 | Accepted: 2026/01/26 | Published: 2026/06/22
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