## 3 Leveraging DL of Mm-Wave in B5G
### Enhancing Channel Prediction Accuracy  
Addressing the sparsity and fast channel fluctuations of mm-wave transmissions requires improving the accuracy of channel prediction using DRL in mm-wave communication for B5G. In dynamic B5G network situations, precise forecasts allow for more effective beam alignment, lower signaling overhead, and providing reliable performance. By using novel approaches intended to handle real-world complexity including mobility, interference, and environmental unpredictability, the channel modeling in this article is validated. Beam training overhead is reduced, and the model dynamically adjusts to network conditions by utilizing DL for OFDM systems. Beam alignment, excessive path loss, and channel fluctuations are all successfully addressed by the RBT-DRL-based beam training approach, which guarantees reliable system performance and high-resolution channel estimation. Their alignment, including limited scattering, beam misalignment, and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**AI**} & \multirow{2}{*}{**Application on**} & \multirow{2}{*}{**Performance**} & \multirow{2}{*}{**On**} & \multirow{2}{*}{**Challenges**} & \multirow{2}{*}{**Future Direction**} & \multirow{2}{*}{**Remarks**} \\ \cline{3-4} \cline{6-7}  & & **DL** & & & & & & \\ \hline
[33] & 2020 & / & x & / & / & x & / & This paper focuses on how Deep IA reduces the beam sweep time compared to utilizing only a subset of the available beams. \\ \hline
[46] & 2021 & / & & / & / & / & x & To reduce an MU-mm-wave mMIMO system’s hardware cost and downlink power usage, this article investigates beam allocation and power optimization. \\ \hline
[48] & 2019 & x & / & / & / & / & x & This work focuses on a novel method that can learn to forecast both short- and long-term variations: the NN-based channel prediction model. \\ \hline
[49] & 2022 & / & / & / & / & / & This paper intends to address the linear ISI using a former RNN with memory that is combined with two cascading sub-networks, i.e., the DL detector. \\ \hline
[54] & 2022 & / & x & / & / & / & This paper focuses on a novel beam training algorithm via DRL for mm-wave channels considering user mobility effects to improve EE and SE. \\ \hline This article & 2025 & / & / & / & / & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of existing research on DL mm-wave. Figure 2: A representation of the DNN training in mm-wave sets for training [50]. the effect of low-resolution ADCs, validates the channel modeling, which is analytically tractable due to its intrinsic simplification. Using HP in conjunction with RBT-DRL for beam training is specifically made to handle real-world challenges including interference, mobility, and environmental variability. By including features that dynamically adjust to changing network conditions, the chosen channel models are created to handle real-world complications like mobility, interference, and environmental unpredictability. In dynamic situations like channel variations and significant path loss, the RBT-DRL-based beam training framework optimizes channel estimates while lowering beam training overhead. Additionally, spatial domain quantization signals are used to increase channel accuracy and overcome low-resolution ADC issues as shown in Figure 3. By lowering the number of RF chains while preserving strong beamforming capabilities, the HAD architecture improves channel modeling even further. MSE optimization is used to validate these models, guaranteeing that the predicted channels closely resemble actual conditions. By integrating DRL with AB-HP to tackle specific issues, such as bandwidth allocation and high-resolution channel estimation, shows how DRL may successfully increase channel prediction accuracy. Using DNNs to reduce the error between estimated and actual parameters, the model automatically adjusts to channel circumstances by dynamically improving EE and SE.