## 3 Leveraging DL of Mm-Wave in B5G
### DL in Artificial Intelligence-Driven B5G Mm-Wave Networks  
DL is a powerful technique in artificial intelligence (AI) that has been widely applied in various fields, including wireless communication networks. In the context of B5G mm-wave networks, which operate in high-frequency bands, DL can play an important role in optimizing system performance and improving user experience.  
In [26], a deep IA framework with a DNN is described for enabling dependable and prompt primary access for AI-driven networks outside of B5G mm-wave as shown in Figure 2. The authors in [54] proposed that by using a small portion of the available beams, deep initial access (IA) might also reduce the beam sweep time. This sweep time demonstrates the superiority of deep AI over a conventional AI algorithm that also functions as adaptive learning and an efficient RA, and enhances the overall reliability and speed of communication. IA should only use a portion of the available beams to cut down on sweep time. Received signal strengths, which are obtained from the subset of beams, are mapped to the beam, and deep IA then determines which beam is best oriented towards the receiver side [52]. In particular, in the prediction accuracy of line-of-sight and non-line-of-sight (NLoS) scenarios, deep AI significantly outperforms the conventional or standard IA's beam and reduces IA time [57], with difficulties such as increased sensitivity to obstructions and signal attenuation, especially in situations when there is NLOS. Through its ability to help the system learn and adapt to complex and dynamic situations, deep AI is a lot faster and more reliable and can help address these difficulties by delivering higher reliability, especially in the case of correct prediction [60; 61; 62; 63]. DL can also be used in the development of beamforming and beam-tracking algorithms. Beamforming allows signals to be focused on users or places, which can help to lower interference and boost signal strength. By learning from vast amounts of data and generating predictions based on the circumstances at hand, DL can assist in automating this process. Future wireless communication systems should see even more creative uses of DL. The proposed approach leverages RBT-DRL-based beam training to minimize beam training overhead and adapt dynamically to network conditions, significantly improving both SE and EE in mm-wave systems as shown in Table 4. When compared to existing works such as [33], which focus on reducing beam sweep time by using a subset of available beams, our method offers a more dynamic and robust solution through deep reinforcement learning. Additionally, research like [46] emphasizes beam allocation and power optimization to reduce hardware costs and power usage, while our solution directly addresses the challenges of beam training and adaptation in a HP structure. Compared to [48], which improves system EE using DNN and DRL, the proposed RBT-DRL approach integrates channel estimation and beamforming, offering a more holistic solution for real-world applications with mobility and interference. Moreover, recent works like [57] discuss DRL-based beam training for mm-wave channels considering user mobility, which aligns closely with our work; however, our method provides enhanced performance by incorporating low-resolution ADC considerations and intelligent resource management. These comparisons validate the novelty and effectiveness of the proposed method in comparison to existing solutions.