## 1 Introduction
### Related Works  
The optimization of RA, including beamforming and power, in special AB-HP systems is an interesting but difficult subject that merits further research. Through mm-wave massive MIMO and hybrid beamforming optimization, dynamic beam selection and power allocation (PA), SE enhancement, and reduced complexity, DRL improves B5G performance. A convolutional neural network (CNN) is utilized to extract channel response characteristics in a DL-based channel estimate network, while a long short-term memory (LSTM) network is used to boost performance. This approach uses machine learning techniques to teach neural networks to carry out particular tasks [31, 32, 33, 34]. To accommodate the time-varying nature of wireless channels, the suggested DL-based channel estimation network employs an LSTM network to capture temporal dependencies and a CNN to compress channel response data into feature vectors. In dynamic contexts, this combination increases the accuracy of channel estimates. Compared to massive MIMO systems, mm-wave technology requires fewer transmit antennas and RF chains in high-speed mobile situations, where the method is very successful [35; 36]. To improve hybrid analog/digital beamforming architecture, baseband precoding must be transmitted to use fewer RF chains. Baseband decoding can then be applied to the received signal in the uplink to produce low-cost hybrid beamforming [37; 38]. Moreover, the digital baseband (DB) beamforming could be used by using microprocessing, while the analog RF chains beamforming might be implemented to produce a high data rate by using phase shift [39; 40; 41; 42]. By using mMIMO baseband precoding instead of dedicating an unconnected RF chain for every antenna, the high cost and high power consumption of combined signal components could be improved. The combiner hardware in [43] featured a completely connected phase shifter, and every aerial for the analog signal was connected to the feeding RF chains dependent on the phase shifter on/off switch. The authors of [44] propose multi-user mMIMO systems by suggesting a DL-PA and hybrid precoding technique. To cut down on the amount of RF chains and channel estimation overhead, an angular-based hybrid precoding (AB-HP) technique has been employed. Ref. [45] proposed DL driven with non-orthogonal hybrid beamforming for the single-user, which provides a high SE, and a good balance between transmission performance and hardware complexity. Ref. [46] proposed a DRL to adopt user-pairing by assigning an orthogonal sub-channel to transmit data to the cognitive BS without causing interference to obtain the optimal transmission power and bandwidth allocation. However, Ref. [47] suggested a ground-breaking experience-driven PA method in the mm-wave for high-speed railway systems with hybrid beamforming that is capable of learning power decisions from experience rather than the precise mathematical model. The proposed method in [47] aims to achieve higher SE by selecting the best DL training approach to reduce inter-channel interference which minimizes hardware complexity. With the hybrid beamforming approach, the number of RF chains required can be greatly reduced by combining analog and digital processing, which lowers power consumption and hardware complexity. In addition, the suggested approach makes use of DL to teach the system to choose the optimal beamforming weights for every channel, therefore lowering inter-channel interference and enhancing SE [48]. Considering that the DL method can adapt to changing channel circumstances and understand complicated patterns in the wireless channel, it is especially successful in reaching this goal. All things considered, the suggested technique offers a novel way to increase the spectral efficiency of digital transceivers with a lot of antennas. Through the use of DL and affordable digital-analog hybrid beamforming algorithms, this approach can improve wireless channel performance while reducing hardware complexity [49; 50; 51; 52].