Adaptive modulation and coding based on reinforcement learning for 5G networks

• Autor(a) principal: Mota, Mateus Pontes
• Data de Publicação: 2019
• Outros Autores: Araújo, Daniel Costa, Costa Neto, Francisco Hugo, Almeida, André Lima Férrer de, Cavalcanti, Francisco Rodrigo Porto
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/69733
• topic: Reinforcement learning; Adaptive modulation and coding; Link adaptation; Machine learning; Q-Learning; Inteligência artificial

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.