TY  - JOUR
T1  - Deep Reinforcement Learning based MCS Decision Model
AU  - Lee, A-Hyun 
AU  - Bae, Hyeongho 
AU  - Kim, Young-Ky 
AU  - Kim, Chong-kwon 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.8.663
KW  - deep reinforcement learning
KW  - cellular network
KW  - link adaptation
KW  - modulation and coding scheme
AB  - 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.