## 1 Introduction  
With the massive growth of data transmission, the future Internet of Things (IoT) will face more severe challenges. Free-space optical (FSO) communication is crucial in the forthcoming 6G era, with its characteristics of wide bandwidth, high speed, and high capacity [1]. Multiple-input multiple-output (MIMO) technology has been proven by multiple studies to be equally applicable to FSO systems. In FSO communication, MIMO is applied to reduce turbulence-induced fading. Instead of using a single transmitter and receiver, FSO MIMO systems employ multiple transmitters and receivers along with an array of optical elements such as laser diodes and detectors. These elements are strategically positioned to create multiple optical channels for data transmission. The application of MIMO to FSO systems offers significant advantages in terms of data rate, reliability, and robustness [2, 3]. Ultraviolet (UV) communication has the characteristics of support for non-line-of-sight (NLOS) communication [4], and good confidentiality performance, which has attracted extensive attention. UV communication can be utilized in various scenarios. On-the-move communication is made possible through UV-C links, enabling devices in vehicles or drones to communicate seamlessly. Additionally, UV communication proves beneficial in IoT applications, facilitating communication between devices in harsh environments. Moreover, UV communication enables machine-to-machine communication, allowing machines or sensors to exchange data even in environments with high interference or restricted line-of-sight [5]. The atmospheric turbulence effect caused by random fluctuations in the atmosphere can seriously affect the communication quality of FSO systems. To solve these problems, precoding techniques are used at the transmitter to combat turbulence [6, 7, 8]. However, it should be pointed out that traditional precoding methods are derived from the additive white Gaussian noise (AWGN) model. Optical communication systems use light to transmit data, and light consists of discrete particles known as photons. The arrival of photons at the receiver's detector follows a Poisson process, where photons are emitted randomly over time. The Poisson shot noise model accurately captures this discrete nature of optical signals, making it a better fit for optical communication systems. In other words, the optical communication models are generally modeled as signal-related Poisson shot noise [9; 10]. Precoders and detectors designed under the AWGN model are no longer suitable for photon-counting communication systems. The precoder and combiner should be redesigned for the Poisson shot noise model. The studies regarding the combination of communication and artificial intelligence (AI) have attracted widespread attention from scholars [11; 12; 13], which is a novel way to solve problems in mobile communication. The authors of [11] jointly design the active beamforming and passive beamforming to maximize the sum rate, using the deep deterministic policy gradient (DDPG) algorithm. The action space of this algorithm is designed by the beamforming matrix and the phase shift matrix. In [12], the authors utilize soft actor-critic (SAC) algorithm to design active analog precoder and passive beamformer. The authors of [13] propose a DRL-based precoding framework in both codebook-based and non-codebook-based MIMO precoding systems and examine the performance of the DQN and DDPG algorithms. The advantage of combining reinforcement learning (RL) and mobile communication is that different features can be extracted from a large number of raw data, and by learning to continuously adjust the parameter settings in the internal structure, RL can flexibly approximate the mathematical model of the simulated communication environment and deal with some complex physical channels. RL is applied in practice in various domains such as robotics, navigation, and smart grids. In robotics, RL can be used to train robotic arms to perform tasks like opening doors and picking up objects.