Sah, Dinesh KumarNauman, AliJamshed, Muhammad AliCengiz, KorhanIvković, NikolaUroš, Vedran2025-04-182025-04-182024Kumar Sah, D., Nauman, A., Jamshed, M. A., Cengiz, K., Ivkovic, N., & Uros, V. (2024). Reinforcement Learning Infused MAC for Adaptive Connectivity. In IEEE Wireless Communications and Networking Conference (IEEE WCNC), APR 21-24, 2024, Dubai, U ARAB EMIRATES. IEEE.979-835030358-215253511http://dx.doi.org/10.1109/WCNC57260.2024.10571273https://hdl.handle.net/20.500.12713/7073The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.eninfo:eu-repo/semantics/closedAccessConnectivityIoTMACReinforcement LearningResource UtilizationConference ObjectWOS:0012685693041002-s2.0-8519885001110.1109/WCNC57260.2024.10571273