Reinforcement Learning-Based Grant-Free Mode Selection for O-RAN Systems

As technology advancements are leading to the creation of 5G and next-generation base stations (BS) that offer improved performance and application integration, current solutions are mostly reliant on established technical standards. By incorporating intelligent wireless resource management technology, the current small cell system can be optimized and its transmission performance enhanced. The implementation of deep reinforcement learning was then added. By using indication reports as the state, the smart agent is able to dynamically select the optimal GF parameters to achieve high-efficiency transmission. In the context of ultra-reliable low latency communication (URLLC) applications, we have utilized 5G ns-3 simulation to simulate an IIoT factory scenario that diverges from traditional uplink methods. By implementing grant-free (GF) techniques, we can reduce delays while maintaining a suitable level of reliability. To dynamically select the most appropriate transmission mode under varying conditions, we have developed reinforcement learning (RL) methods. Our numerical results demonstrate a promising trend in the overall satisfaction rate.