RLRBM: A Reinforcement Learning-based RAN Buffer Management Scheme

The development of 5G pushes the research community to concentrate on more innovative 5G beyond/6G networks to provide instantaneous connectivity to futuristic applications, such as e-health, autonomous vehicles, and entertainment services broadcasting, etc., which leads to a huge traffic data explosion. How to mitigate the traffic congestion and the bufferbloat problem is a formidable task. The interaction between transport congestion control protocol and the radio access network (RAN) buffer management scheme drastically impacts the congestion and bufferbloat problem. In this paper, we aim to study the RAN buffer management problem from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL). We propose a model-free approach, RLRBM, which enables the agent to learn the best buffer size tuning policy as human beings learn skills. We simulate several typical scenarios to evaluate RLRBM. Experimental results show that RLRBM achieves best trade-off between high throughput and low latency.