## 2 Techniques Used for Adaptive Equalization
### Deep Learning  
Deep learning techniques, specifically deep neural networks, are increasingly used for adaptive equalization tasks [44]. Deep learning approaches involve training neural networks to learn the mapping between the received signal and the desired signal [45]. In the context of adaptive equalization, deep neural networks can model the complex and nonlinear relationship between the input signal and the equalized output [46]. By utilizing large amounts of training data and employing sophisticated network architectures, deep learning techniques can adapt to a wide range of channel characteristics and achieve superior equalization performance [47; 48]. A deep learning approach requires significant computational resources, substantial training data, and careful regularization techniques to mitigate overfitting [49]. Table 1 provides a comprehensive overview of the pros and cons of techniques used for adaptive equalization.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technique** & **Limitations** & **Advantages** \\ \hline \multirow{3}{*}{LMS} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{Susceptible to getting stuck in local optima [50].} & \(\bullet\) & Simplicity and ease of implementation [25]; \\  & & & \(\bullet\) & Low computational complexity [26]; \\  & & & \(\bullet\) & Achieves significant error reduction and \\  & & & & convergence with reasonable \\  & & & & computational resources. \\ \hline \multirow{3}{*}{RLS} & \multirow{3}{*}{\(\bullet\)} & High computational complexity and memory requirements [34]; \\  & & Sensitive to numerical issues due to & & \(\bullet\) & Achieves improved convergence speed and \\  & & matrix inversion. & & provides accurate filter estimation. \\ \hline \multirow{3}{*}{PSO} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{May suffer from premature convergence and lack of diversity [51];} & \multirow{3}{*}{\(\bullet\)} & Provides global search capability; \\  & & & & \(\bullet\) & Can handle complex and nonlinear optimization \\  & & & & problems; \\  & & & & problems; \\  & & & & \(\bullet\) & Achieves optimal filter coefficients with \\  & & & & enhanced convergence and improved \\  & & & & equalization performance [38]. \\ \hline \multirow{3}{*}{GA} & \multirow{3}{*}{\(\bullet\)} & Convergence speed may be slower compared to other algorithms; \\  & & & & \(\bullet\) & Can handle complex optimization problems; \\  & & & & \(\bullet\) & Provides a diverse set of solutions; \\ \cline{1-1}  & & & \(\bullet\) & Achieves improved equalization performance \\ \cline{1-1}  & & & & with diverse and globally optimal solutions [43]. \\ \hline \multirow{3}{*}{Deep Learning} & \multirow{3}{*}{\(\bullet\)} & \multirow{3}{*}{\(\bullet\)} & Can adapt to complex and nonlinear \\  & & & \(\bullet\) & channel characteristics; \\ \cline{1-1}  & & & \(\bullet\) & Provides high flexibility in modeling the \\ \cline{1-1}  & & & \(\bullet\) & equalization process [46]; \\ \cline{1-1}  & & & \(\bullet\) & Achieves superior equalization performance with \\ \cline{1-1}  & & & \(\bullet\) & accurate mapping of the \\ \cline{1-1}  & & & & input–output relationship. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Techniques used for adaptive equalization.