Real-Time Data Transmission Optimization on 5G Remote-Controlled Units Using Deep Reinforcement Learning

The increasing demand for real-time data transmission for the remote-controlled units and the complexity of 5G networks pose significant challenges to achieving optimal performance in device-based scenarios, when the 5G network cannot be controlled by its users. This paper proposes a model-free Deep Reinforcement Learning approach for this task. The model learns an optimal policy for maximizing the data transmission rate while minimizing the latency and packet loss. Such an approach aims to investigate the applicability of the environment-agnostic agents driven purely by the transmission statistics of the acknowledged packets. The evaluation is done with the help of a 5G simulation based on the OMNeT++ network simulator and the obtained results are compared to a classic throughput-based adaptive bitrate streaming approach. Multiple questions and challenges that arose on the way to the final model and evaluation procedure are highlighted in detail. The resulting findings demonstrate the effectiveness of Deep Reinforcement Learning for optimizing real-time data transmission in 5G networks in an online manner.