Talk Title: Intelligent Random Access for Next-Generation IoT Networks

With the new wave of digital revolution, wireless communication networks are experiencing a radical paradigm shift from the conventional human-to-human (H2H) communications to machine-to-machine (M2M) communications. To facilitate the massive access of machine-type devices, random access is expected to play a crucial role in the next-generation Internet-of-Things (IoT) networks.
Thanks to its distributed nature, random access has been widely applied to various wireless networks including 5G cellular networks and WiFi networks. Yet it has long been observed that the performance of random access protocols may significantly degrade under heavy traffic. To improve the design of access strategies, reinforcement learning (RL) based approaches have shown tremendous potential. Due to the lack of theoretical guidance, however, they are often designed in an empirical manner. In this talk, I will demonstrate how to leverage our recently proposed unified theoretical framework to optimize the design of RL-based random access. I will conclude the talk by highlighting the challenges and opportunities for intelligent random access for next-generation IoT networks.