Name
Title | ||
|---|---|---|
Power Allocation Based on Reinforcement Learning for MIMO System With Energy Harvesting |
Abstract | ||
|---|---|---|
This paper focuses on the use of a reinforcement learning (RL) approach to find two online power allocation policies in a point to point EH-MIMO wireless communication system. In our study, we train the power allocation policies in order to learn the map between the environment and the agent. Particularly, in order to avoid “dimension disaster” problem which may happen in our proposed SARSA power allocation policy, we introduce a linear approximation method to get an approximate SARSA power allocation policy. The linear approximation can handle infinite number of states and trade-off between complexity and performance of power allocation is significantly improved. The simulation results show that the proposed SARSA and approximate SARSA power allocation policies have a considerable throughput increase compared with the benchmark policies, such as greedy, random and conservative policies. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TVT.2020.2993275 | IEEE Transactions on Vehicular Technology |
Keywords | DocType | Volume |
Power allocation,throughout maximization,reinforcement learning,energy harvesting,multiple-input multiple-output | Journal | 69 |
Issue | ISSN | Citations |
7 | 0018-9545 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingchi Mu | 1 | 1 | 0.35 |
| Xiaohui Zhao | 2 | 87 | 15.89 |
| Hui Liang | 3 | 1 | 0.35 |