Name
Abstract | ||
|---|---|---|
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 arc 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GCWkshps45667.2019.9024384 | 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS) |
Keywords | DocType | ISSN |
Reinforcement Learning, Adaptive Modulation and Coding, Link Adaptation, Machine Learning, Q-Learning | Conference | 2166-0069 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mateus P. Mota | 1 | 0 | 0.34 |
| Daniel C. Araujo | 2 | 19 | 3.40 |
| Francisco Hugo Costa Neto | 3 | 0 | 0.34 |
| André L. F. de Almeida | 4 | 371 | 48.73 |
| Francisco Rodrigo P. Cavalcanti | 5 | 77 | 18.54 |