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.

中文翻译：

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基于强化学习的能量收集 MIMO 系统功率分配


本文重点介绍使用强化学习 (RL) 方法在点对点 EH-MIMO 无线通信系统中找到两种在线功率分配策略。在我们的研究中，我们训练功率分配策略以学习环境和代理之间的映射。特别地，为了避免我们提出的SARSA功率分配策略中可能发生的“维数灾难”问题，我们引入了线性逼近方法来获得近似的SARSA功率分配策略。线性近似可以处理无限数量的状态，并且功率分配的复杂性和性能之间的权衡得到显着改善。仿真结果表明，与贪婪、随机和保守策略等基准策略相比，所提出的SARSA和近似SARSA功率分配策略具有相当大的吞吐量增加。