The multiple-input multiple-output (MIMO) technique for free-space optical (FSO) system was initially designed for combating fading events in the diversity mode. However, as people demand for higher throughput, extra freedom can be obtained from the multiple apertures in the spatial multiplexing mode, where the system transmits independent parallel data streams over multiple apertures to increase data rate. In this paper, we study a MIMO FSO system in the multiplexing mode. By maximizing long-term benefits on the average capacity within limited time slots, we propose a power allocation algorithm based on the reinforcement learning (RL) method. Our RL algorithm utilizes an actor–critic structure, where both action space and state space are continuous. We also add the constraints on the peak power and total power. A novel reward function is designed with a punishment item for remaining power. The proposed RL algorithm can achieve a better performance than the existing benchmarks.

最初设计了用于自由空间光学（FSO）系统的多输入多输出（MIMO）技术，以应对分集模式下的衰落事件。但是，随着人们对更高吞吐量的需求，可以在空间复用模式下从多个孔径中获得额外的自由度，其中系统通过多个孔径传输独立的并行数据流以提高数据速率。在本文中，我们研究了复用模式下的MIMO FSO系统。通过最大化有限时隙内平均容量的长期利益，我们提出了一种基于强化学习（RL）方法的功率分配算法。我们的RL算法利用行为者-批评结构，其中动作空间和状态空间都是连续的。我们还添加了对峰值功率和总功率的约束。设计了一种新颖的奖励功能，并带有针对剩余电量的惩罚项。所提出的RL算法可以实现比现有基准更好的性能。