## 2 System Model  
The schematic diagram of the MIMO-FTN-OWC system is illustrated in Figure 1. Assuming the presence of \(N_{t}\) transmitting antennas and \(N_{r}\) receiving antennas, the binary data undergo Gray coding and subsequent mapping into a PAM signal with a modulation order of 4. The modulated 4 PAM signal \(g(t)\) is then passed through an FTN shaping filter to obtain the FTN signal \(S_{FTN}(t)\), which is subsequently fed into a trained neural network for ISI mitigation. Following this, the signal traverses a digital-to-analog converter (DAC) and drives a laser diode (LD) to generate an optical signal \(S_{T}(t)\) that is transmitted via multiple optical antennas through the atmospheric channel. In this system, FTN signals are directly converted to optical signals through the LD after passing through the DAC, eliminating the need for an optical amplifier (e.g., EDFA). The utilization of an optical amplifier can introduce undesirable effects such as nonlinear distortion, increased noise, and gain saturation, which may result in signal distortion, reduced signal-to-noise ratio, and compromised demodulation performance and signal reliability. Therefore, although opting out of an optical amplifier may lead to insufficient signal strength during long-distance transmission or in interference-prone environments, it enhances anti-interference capability by employing a high-precision DAC to minimize signal distortion, selecting a stable and linear laser diode and utilizing a 4 PAM modulation method suitable for FTN transmission. Additionally, we apply the MHSA-1D CNN-Bi-LSTM algorithm to mitigate ISI effectively while ensuring high-quality output of optical signals. The optical signal \(S_{R}(t)\) received by multiple optical antennas at the receiver end are converted into an electrical signal through a photodetector (PD). These electrical signals then go through an analog-to-digital converter (ADC), followed by matched filtering, sampling, and subsequent transmission to a maximum likelihood detector. Finally, the data are recovered after Gray decoding. In the MIMO-FTN-OWC system, the PAM signal is denoted as \(P=[p_{1}p_{2}\cdots p_{n}]\). The system employs a rising cosine filter \(g(t)\) with a roll-off factor \(\psi\), \(r\) denotes the number of truncation segments, \(p\) denotes the number of samples per segment, \(T=p\) denotes the Nyquist transmission interval and filter length is \(L=r\times p+1\). The vector form of the rising cosine filter \(g(t)\) can be expressed as follows [17]:  
\[G=[g_{1},g_{2},\ldots,g_{L-1},g_{L}] \tag{1}\]  
Figure 1: Schematic of MIMO-FTN-OWC system. where \(G\) denotes the peak point of the filter coefficients, which corresponds to the sampling point of the received signal. By multiplying PAM signal values with \(G\) and superimposing them after a delay, the FTN signal \(S_{FTN}(t)\) can be expressed as follows [18]:  
\[S_{FTN}(t)=\sum_{i=0}^{n}p_{i}g(t-(i-1)\tau T),0<\tau<1 \tag{2}\]  
where \(n\) denotes the number of modulated symbols transmitted per frame, \(p_{i}\in P\) denotes the \(i\)-th symbol, \(P=\{a_{m}=2m-1-U,m=1,2,\ldots,U\}\), \(U\) denotes the modulation order of PAM, \(\tau T\) is the delay interval, \(\tau=p^{\prime}/p\) denotes the acceleration factor, \(p^{\prime}\) denotes the sampling factor when shaping FTN signal. The FTN signal is subsequently input into a trained neural network that combines 1D CNN, the Bi-LSTM network, and the MHSA mechanism. The 1D CNN extracts crucial features from the data and arranges them into sequences for the Bi-LSTM network to learn from. Additionally, the MHSA mechanism assigns extra weight to enhance learning outcomes and ensures that the pre-processed de-interference signal \(S_{FTN-DL}(t)\) closely resembles the original signal. Subsequently, \(S_{FTN-DL}(t)\) is converted to the digital-to-analog format before driving the LDs to generate an optical signal \(S_{T}(t)\).