## 4 Enabling Technologies for THz-FSO Communication
### Signal Processing Algorithms  
Algorithms used in signal processing contribute significantly to overcoming obstacles that arise due to the fluctuating channel conditions in THz-FSO systems. These algorithms should improve link availability, data throughput, and latency, which are fundamental to efficient system operation.  
Machine learning for signal reconstruction: the current state-of-the-art deep learning algorithms for untarring distorted THz signals include convolutional neural networks (CNNs) [55; 74; 94]. These models involve pattern recognition and the utilization of recorded signatures from which the models conclude the details of real-time environments to predict and compensate for distortions of the signals in real time. These implementations effectively enhance the signal-to-noise ratio and ensure data efficiency for even relatively unstable channels. However, concocting corrected signals for high-error-rate channels through the use of generative adversarial networks (GANs) is promising [95; 96].  
Beam alignment optimization: RL is used in the present analysis to dynamically schedule the alignment of THz beams through reinforcement-learning-based algorithms. These methods guarantee little or no pointing direction errors and improve energy utilization due to the constant changes in the pointing direction of the transmitter. The algorithms determine the conditions in the environment and act depending on the amount of turbulence when less power is used. These optimization methods are generally validated via Monte Carlo simulations, which compare the performance of the method under various circumstances.  
Error correction codes [85; 87]: Low-density parity-check (LDPC) codes and turbo codes, for example, are an essential component of reducing data loss in noisy conditions [2; 6; 7]. In this case, LDPC codes are especially efficient because they provide near  
\begin{table}
\begin{tabular}{p{56.9 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Studies** & **Technology** & **Advantages** & **Limitations** & **Applications** \\ \hline
[91] & Wavefront correction & Improves Strehl ratio & Computational cost & Signal integrity \\ \hline
[92] & Real-time AO systems & Dynamic correction & Requires high-speed hardware & Long-distance communication \\ \hline
[93] & Phase-only SLMs & High-resolution phase control & Limited by modulation speed & Turbulence compensation \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of adaptive optics technologies.  
capacity performance along with reduced complexity. The effectiveness of these codes is measured by the bit error rate (BER), which is calculated as follows:  
\[\text{BER}=\text{Q}\Bigg{(}\sqrt{\frac{2\text{E}_{\text{b}}}{\text{N}_{0}}}\Bigg{)} \tag{21}\]  
where BER: the bit error rate. Q: the Q-function, representing the tail probability of the standard normal distribution. E\({}_{\text{b}}\): the energy per bit. N\({}_{0}\): the noise power spectral density. LDPC codes are known to offer good performance in high-interference channels and, therefore, are recommended for use in correcting errors in THz-FSO systems.  
Input: the received signal (S\({}_{r}\)); channel state information (CSI) output: the corrected signal (S\({}_{c}\)) develop a deep learning model for reconstruction and establishing the estimations of channel impairments can be done by using CSI. Reinforcement learning-based beam alignment optimization can also be done in this way. Optimizing signal equalization and noise removal can be done as well. The received signal can be decoded using LDPC or turbo codes.  
The algorithm discussed above combines the use of artificial intelligence and conventional signal processing to deliver the best result for THz-FSO links. It is possible to optimize data communication, since these techniques guarantee that information can be reliably sent across harsh environments.