## 1 Introduction  
The rapid development of mobile communication technology has greatly improved the performance of OWC systems, which are now widely used in various applications such as intelligent transportation, the Internet of Things, and 5G and future 6G networks [1]. To achieve higher transmission rates and better signal quality, advanced technologies like MIMO have become essential. MIMO maximizes system gain by deploying multiple antennas at both ends to improve spectral efficiency, enhance interference immunity, and maintain high-quality signal transmission in complex environments [2]. Additionally, FTN technology offers new opportunities for increasing OWC system rates by enabling approximately 25% higher data transmission within the same bandwidth through shorter symbol intervals and increased signaling rates [3]. FTN is a non-orthogonal transmission technique that offers distinct advantages over Power Domain Non-Orthogonal Multiple Access (PDNOMA) and Code Domain Non-Orthogonal Multiple Access (CD-NOMA). FTN achieves higher data transmission rates within the same time by reducing symbol interval, thereby improving spectral efficiency. In contrast to PD-NOMA, which relies on power allocation and user interference cancellation, FTN primarily focuses on optimizing information transmission in high-bandwidth-demanding scenarios. Additionally, CD-NOMA differentiates users through various coding techniques, which can enhance capacity; however, its performance may be limited by coding complexity and the number of users in the applications that require high spectral efficiency. Therefore, FTNs demonstrate superior performance in specific scenarios which necessitate high-speed data transmission and thus warrant further research and application [4; 5]. The implementation of FTN technology, however, poses challenges regarding ISI. When symbol transmission rates exceed the Nyquist rate, non-orthogonal heterodyne pulses can occur, resulting in degraded demodulation performance and impaired signal identification. In high data rate transmissions, signal quality instability may significantly impact communication effectiveness [6]. Therefore, effectively mitigating the ISI introduced by FTNs in MIMO-FTN-OWC systems is crucial as it directly affects signal reliability and user experience [7]. A low-complexity equalization algorithm based on the variational method suggested in [8] exhibits linearly increasing complexity with longer block lengths due to its limited ability to eliminate the ISI caused by strong mutual interference. However, the FTN technique allows for full knowledge of the introduced ISI at the transmitter, eliminating the need for feedback of the channel state information. An alternative approach proposed in [9] is linear pre-equalization, which transforms FTN signals into orthogonal signals at higher baud rates for improved spectral efficiency and to achieve optimal BER performance. For the complete elimination of ISI, ref. [10] proposed an adaptive point-by-point elimination pre-equalization algorithm that calculates interference weights using shaping filters and transmitted symbols for various acceleration factors. It then offsets interference values after FTN shaping on a point-by-point basis based on these weights. However, this method requires increased computational complexity in exchange for higher spectral efficiency and BER performance. In recent years, there has been growing attention towards integrating DL techniques into communications. Notably, ref. [11] utilized a recurrent neural network (RNN) structure based on LSTM networks to eliminate ISI introduced by FTN, achieving similar performance to the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. Additionally, ref. [12] introduced an equalization algorithm based on Bi-LSTM networks to address successive ISI elimination between neighboring signals, effectively enhancing system BER performance. Meanwhile, ref.