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

Autor:	Mota, Mateus P., Araujo, Daniel C., Neto, Francisco Hugo Costa, de Almeida, Andre L. F., Cavalcanti, F. Rodrigo P.
Rok vydání:	2019
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Information Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted for presentation at the IEEE GLOBECOM 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.04030 Zobrazit plný text záznamu View this record from Arxiv