## 5 Machine Learning Application for Conventional and Universal Decoding Schemes  
The next phase of fifth-generation development, fifth-generation-advanced (5G-A), was unveiled by the Third Generation Partnership Project (3 GPP) during Release 18, which aims to further enhance 5G performance by enabling AI-powered 5G [52]. Optimization of network energy efficiency, coverage area, mobility support, multiple-input multiple-output (MIMO), and broadcast services, can lead to further enhancement in 5G performance. Taking into account these parameters, Release 18 leverages machine learning (ML) and artificial intelligence (AI) technologies to effectively manage the ever-complex 5G networks. In order to attain optimal end-to-end performance in communication networks, machine learning methods are being developed nowadays. Although ML is categorized under AI, it serves as an extension of AI by incorporating probabilistic decision-making with deterministic decision-making performed by AI. The application of ML algorithms makes conventional decoding techniques more adaptable across different codes. Authors in [53] have categorized ML models into _supervised_, _unsupervised_, _and reinforcement learning_. They have discussed in detail how each ML technique can provide potential solutions to the 5G/B5G network requirements of each service class. The following machine learning models are being incorporated to support decoding for next-generation forward error-correction (FEC) techniques:  
1. Reinforcement learning (RL)--The RL-based decoder can dynamically adopt decoding strategies according to changing channel conditions. 2. Deep learning (DL)--DL-based models excel in decoding as it is regarded as a classification problem. Deep neural network (DNN)-based adaptive decoders reduce computational complexity. 3. Graph neural network (GNN)--Approximate decoding with fewer iterations, achieving ultra-low latency [54]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specification** \\ \hline Channel, modulation & AWGN, BPSK \\ \hline Coding scheme & Random linear code \\ \hline Block length (N), information lengths (K) & 128, 103, 64 \\ \hline Code rates (R) & 0.8047, 0.5 \\ \hline Decoding algorithms & GRAND, AED, and OSD \\ \hline SNR range & 0 dB to 12 dB \\ \hline Simulation method, number of iterations & Monte Carlo, \(10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation parameters. Autoencoding neural network (ANN)--An unsupervised learning approach traditionally used for data reconstruction is used for signal processing and decoding of LDPC codes [55]. Reinforcement learning (RL) techniques have recently been studied to decode linear block codes in addition to the primary research technique that concentrates on employing supervised learning algorithms. A comprehensive survey of DL applications for channel coding problems is done in [56], where the authors have discussed state-of-the-art DL models for application in communication systems. They also have presented deep learning methods for model-free decoders, which employ neural networks for code-agnostic algorithms. It is demonstrated that model-free decoders are particularly suitable for low-latency applications. Further, a DL-based adaptive decoder is proposed by [PERSON] et al. in [57], where the designed decoder can decode both polar and LDPC codes using the same decoding algorithm. [PERSON] et al. [58] have proposed a graph neural network (GNN)-based universal decoder for binary linear codes based on bipartite graphs. Google introduced the federated learning (FL) concept, which avoids data leakage when developing machine learning models using data sets dispersed across numerous devices. AI and ML are displacing code-specific algorithms with decoders that are learning-based, flexible, and real-time. These universal decoders will be essential to 6G, allowing for reliable, low-latency, and energy-efficient communications in dynamic settings.