Deep Reinforcement Learning based MCS Decision Model

A-Hyun Lee, Hyeongho Bae, Young-Ky Kim, Chong-kwon Kim

http://doi.org/10.5626/JOK.2022.49.8.663

In wireless mobile communication systems, link adaptation techniques are used to increase channel throughput and frequency efficiency to adaptively adjust transmission parameters according to the changes in the channel state. Adaptive modulation and coding is a link adaptation technique that determines predefined modulation and coding scheme depending on the channel condition and performed based on the reported CQI from UE and HARQ feedback on packet transmission. In this paper, we propose an MCS decision model that applies deep reinforcement learning to adaptive modulation and coding. The proposed model adaptively determines the MCS level in a dynamically changing network, thereby increasing the transmission efficiency of UEs. We evaluated our proposed model through UE log-based simulations and demonstrated that our model performs much better than the existing outer loop rate control method.