## 1 Introduction
DRL is used in mMIMO systems to solve computationally complicated and demanding problems including resource allocation (RA), beamforming, and channel estimation that arise from the enormous number of users and antennas by providing more intelligent beam training. DL enables these systems to process high-dimensional data efficiently, adjust to changing conditions, and maximize performance measures like latency, energyeconomy, and spectrum efficiency (SE). The development of B5G wireless communication systems depends heavily on DL since it also makes data-driven methods to model and mitigate uncertainties, improve user experience, and make lower computational complexity possible [20]. With DL applied to mm-wave systems, the distinct features of the mm-wave frequency spectrum make RA a crucial issue in mm-wave communication systems. Because of the directional nature of mm-wave signals, significant path loss, and interference, using mm-wave technology can lead to greater data rates and network capacity. Utilizing time, frequency, and power as well as other available resources to their fullest potential can be achieved by using the RA [6, 21, 22, 23]. As the need for high-speed wireless communication services grows, this may help to sustain it. Moreover, power resource optimization, a crucial aspect of battery-powered devices, can be achieved by applying RA. RA helps lessen the environmental impact of wireless communication and prolongs device battery life by consuming less power. Nonetheless, to meet the varying bandwidth and latency needs of various applications, like gaming and video streaming, the quality-of-service demands must be improved. To this end, mm-wave implementation in DL necessitates a substantial amount of data and computational resources, and careful DL algorithm design and optimization are required to achieve optimal EE [24, 25]. Through intelligent and adaptive RA based on real-time feedback from the system, DL can be utilized to enhance RA in mm-wave systems. DL makes intelligent and adaptive signal processing possible, which can enhance the performance of mm-wave systems [26]. Furthermore, the performance, reliability, and efficiency of mm-wave systems could be greatly enhanced by the service of DL for RA. It is necessary to carefully evaluate the system requirements, channel conditions, and available computational resources when designing and optimizing DL algorithms for RA [27, 28, 29, 30]. Effective channel estimation and beamforming are severely limited by the drawbacks of traditional mm-wave systems, such as the difficulty of using a high number of antennas with a small number of RF chains. As the number of antennas in these systems increases, so does the complexity of precisely calculating the channel, resulting in enormous processing demands. Furthermore, to cover the large beam space, classic beamforming approaches frequently call for many pilot symbols, which raises pilot overhead and lowers system efficiency. It becomes harder to control the necessary RF chains and the related beam training overhead in mm-wave systems as the number of antennas increases. These limitations highlight the need for creative solutions, such as the proposed angular-based hybrid precoding (AB-HP), which optimizes beam training procedures and reduces RF-chain utilization by utilizing angular-based information. This method paves the door for more effective and scalable mm-wave systems in B5G networks by directly addressing the problems of high computational complexity, decreased beamforming accuracy in low SNR zones, and power of the signal pilot.