Alert button
Picture for Francisco Hugo Costa Neto

Francisco Hugo Costa Neto

Alert button

Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks

Add code
Bookmark button
Alert button
Nov 25, 2019
Mateus P. Mota, Daniel C. Araujo, Francisco Hugo Costa Neto, Andre L. F. de Almeida, F. Rodrigo P. Cavalcanti

Figure 1 for Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks
Figure 2 for Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks
Figure 3 for Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks
Figure 4 for Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks

We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding scheme (MCS) that maximizes the spectral efficiency while maintaining a low block error rate (BLER). In this framework, the BS chooses the MCS based on the channel quality indicator (CQI) reported by the user equipment (UE). A transmission is made with the chosen MCS and the results of this transmission are converted by the BS into rewards that the BS uses to learn the suitable mapping from CQI to MCS. Comparing with a conventional fixed look-up table and the outer loop link adaptation, the proposed framework achieves superior performance in terms of spectral efficiency and BLER.

* Accepted for presentation at the IEEE GLOBECOM 2019 
Viaarxiv icon
Github IconAccess Paper or Ask Questions
Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon