Autonomous RACH Resource Slicing for Heterogeneous IoT Devices Communication Using Deep Reinforcement Learning

Abstract

In a wireless network infrastructure, the initial synchronization process primarily decides whether to send or receive data between a device and base station. This process is usually powered by a random access (RA) mechanism to share and allocate radio resources dynamically. Over the past years, telecommunication industry has witnessed a massive growth in the Internet of Things (IoT) technologies which continue to be rolled out around the world with different services and having a variety of requirements. However, when massive IoT (mIoT) devices attempt to access the network over a limited number of Random Access Channel (RACH) resources within a time frame, the network becomes overloaded, leading to a low performance of human to human (H2H) communication and Quality of Services (QoS) may not be assured. To solve the above problems, we propose a dynamic resource slicing and access class barring (ACB) mechanism using deep reinforcement learning (DRL) for a new RACH scenario to control and manage the resource dynamically. Simulation results prove that our proposed technique provides a fair RACH resource allocation for each class according to the available radio resource.