## 1 Introduction  
Free space optical (FSO) communication has attracted widespread attention from both academia and industry. It has been demonstrated as a promising technology as a complementary solution to conventional radio frequency (RF), fiber optics, and microwave communications, particularly for applications with size, weight, and power restrictions. The potential advantages of FSO communication including eye safety, no spectrum licensing issues, smaller and lighter payloads, low probability of intercept, and immunity from jamming make it a natural candidate for maritime long-range communication scenarios [1; 2; 3; 4]. Among the various modulation schemes, Morse code is still widely used as a common language between maritime vessels due to its simplicity, efficiency, and small bandwidth costs. Traditionally, the ubiquitously deployed Morse lamp with LEDs, particularly on naval ships, is operated by a signalman translating the message into Morse code (dots and dashes) and manually turning the lamp \"on\" and \"off\" in the correct sequence (on-off keying), to transmit a light signal. On the recipient ship, the blinking light signal is visually observed and directly interpreted by persons trained in the skill. However, there remains several outstanding issues to this kind of manual signaling method nowadays [5; 6]. First, it would require a long training period for a skilled recipient on shipboard. In practice, Morse code relies on precise time intervals between elements while operators have difficulties in maintaining an absolutely stable sending pace, the data length may vary even if it is operated by the same person, and, thus, the reliability of communication cannot be guaranteed [7]. Moreover, as it is long-term and repetitive work, the recognition accuracy is susceptible to the physical and psychological conditions of the human body, which can make them unwieldy in an emergency as well [8]. To cope with the aforementioned issues, research efforts have been made for Morse code automatic recognition in wireless communications. Previous related works are mostly based on signal processing tools, such as Kalman filters, phase-locked loops, and time-frequency analysis [9; 8; 10; 11]. [PERSON] et al. [9] proposed an automatic method to lock the frequency of Morse code based on the phase-locked loop circuit. [PERSON] et al. [10] presented an automatic reception approach for high-frequency (HF) continue-wave (CW) telegrams. A Kalman filtering algorithm combined with the support vector machine (SVM) is utilized to extract the time-domain characteristics of CW signals and deal with unstable code speed. The authors of [11] adopted the Cooley-Tukey Fast Fourier Transform (FFT) algorithm to analyze the spectrogram of a noisy audio Morse signal, and implemented a real-time decoder based on a digital signal processor. In [12], a wavelet transform-based automatic decoding method was proposed for multiplexed Morse telegraph recognition, and the feasibility of this method was analyzed via experiments. With the further advances of computers and embedded technologies, various high-performance microcontrollers have come out, facilitating the implementation of automated process control, signal recognition, data processing, and algorithm verification. Furthermore, motivated by the remarkable achievements of machine learning, some machine learning (ML)-based approaches have been proposed to model Morse signals and enhance the system robustness in a time-varying noisy environment. [PERSON] et al. [13] provided a machine-learning method for automatic Morse signal detection. An SVM classifier, named HSVM, was proposed to be trained, based on graphical features extracted from the Morse spectrum. The authors of [14] combined signal processing and the deep learning method to construct a Morse identification system, an improved feature extraction algorithm was proposed, and the experimental results indicated a better result. [PERSON] et al.