## 3 Leveraging DL of Mm-Wave in B5G  
For sensing-related technologies, DL for mm-wave communication in B5G is essential because it allows adaptive beamforming, accurate channel estimates, and real-time environmental awareness, all of which improve system efficiency and dependability under complex and dynamic network settings. DL addresses problems like dynamic channel fluctuations, beam alignment, and high route loss, and so it is crucial to use it for mm-wave communication in B5G. Through adaptive beamforming, efficient channel prediction, and intelligent resource management, DL enhances the performance of mm-wave communication. The use of DL, a potent machine learning technique well-known for its effectiveness in fields including speech recognition, image identification, and natural language processing, has recently drawn attention from researchers looking to address these issues in mm-wave communication systems [57, 58, 59, 60]. Channel prediction and estimation is one of the primary uses of DL in mm-wave communication. Real-time channel state prediction is possible with DL-based techniques since they can learn the channel characteristics from vast volumes of data. This can boost signal quality and decrease interference, which will enhance the functionality of mm-wave communication systems. Beamforming is another area where DL is used in mm-wave communication. To handle the dual optimization of EE and SE in the face of intrinsic difficulties including high path loss, beam alignment overhead, and environmental variability, DL for mm-wave communication in B5G is essential. Because DL is so good at finding patterns in complicated data, network parameters may be optimized in real-time and adaptively to improve both EE and SE at the same time. By demonstrating its significance, the DL-based solutions can operate around mm-wave restrictions, lessen the computational load, and enhance resource efficiency.  
One method for enhancing signal quality and lowering interference is beamforming, which focuses signal transmission in a particular direction. Beamforming techniques based on deep learning can enhance the efficiency of mm-wave communication networks by determining the ideal beamforming weights based on user data DL and have demonstrated significant potential in a number of wireless communication technologies, such as mMIMO. Channel estimate, or predicting the channel matrix between the BS and the users, is one of the primary issues in mMIMO. To effectively estimate the channel in mMIMO systems, this method can be computationally complex and necessitates a high number of pilots [61]. By learning the channel properties directly from the received signals, neural networks can be used in DL-based channel estimation to circumvent these difficulties. This can lower the overhead related to pilot training and transmission while increasing the accuracy of the channel estimation. Beamforming is a further use of DL in mMIMO systems [8, 19, 62], and DL-based beamforming can reduce interference and increase signal quality. It can also enhance the performance of mMIMO systems by learning the ideal beamforming weights from channel data [55, 62].  
The combination of DL and mMIMO has the potential to significantly improve the performance of wireless communication systems, including 5G and beyond 5G. By leveraging the power of DL, it is possible to overcome some of the challenges associated with traditional wireless communication techniques and enable high-speed and reliable wireless communication systems. Although enormous MIMO is a potential technique for future communication networks to maximize SE, the system's performance is negatively impacted by its extremely high spatial complexity. We apply DL to a huge MIMO system with a focus on the difficulties of the direction of arrival (DOA) estimation and channel estimation, and the viability of this innovative framework is confirmed using in-depth simulations [25].