## 1 Introduction
### Related Works
Increasing the number of antennas per RF chain transceiver can make it more practical to implement fully connected beamforming. In related work, the author of [53] proposed a new beam training algorithm via DRL with lower training overhead in B5G to achieve higher performance for EE or SE with a lower training overhead. However, [PERSON]. [54] proposed the development of DL techniques to optimize EE/SE and optimize its spectrum sensing process. In addition, [PERSON]. [28; 55] proposed joint energy-spectral efficiency optimization for a dynamic adaptation approach based on machine learning (ML) to achieve EE for connected users. The distributed environment, subject to sharing limitations, was optimized to maximize the EE-SE trade-off by [56] using multi-objective optimization. Furthermore, they employed Pareto-optimal solutions to optimize the framework by combining methods applied by different UEs and operators simultaneously. The writers of [40; 57] investigated the trade-off between EE-SE and made it possible for HetNets to enhance UE, bandwidth allocation, and a large number of antennas, by taking advantage of the backhaul capacity limitation to optimize both EE and SE. This allowed them to guarantee high data rates for users. According to the total-transmit-PA limitation, the author of [58] formulates multi-objective optimization to maximize the EE-SE trade-off and determine the feasible DL rate. Because SE is unrelated to transmitting power in a huge MIMO system, reducing the number of antennas can boost EE [59]. Key elements like transmission power and antenna count can be efficiently optimized in both EE and SE by using alternative optimization strategies [60]. Therefore, our proposed approach has the potential to significantly improve the efficiency and reliability of hybrid precoding technique multi-user mMIMO systems. Current techniques, like traditional hybrid beamforming, for optimizing SE and EE in multi-user mMIMO systems try to minimize the number of RF chains by combining analog and digital processing. These methods still have a number of drawbacks, though, such as lengthy beam training processes and high pilot power needs for channel estimation, which raises overhead and computational complexity and ultimately restricts their scalability and usefulness for use in sixth-generation wireless networks. DRL-based hybrid precoding techniques have been introduced to address these issues, yet many fail to effectively balance the beam training overhead with RF chain utilization. The proposed AB-HP architecture with RBT-DRL overcomes these limitations by incorporating angular domain feedback to dynamically optimize EE and SE while significantly reducing training overhead and RF chain usage. Unlike prior approaches that focus solely on reducing RF chains or improving training efficiency, AB-HP with RBT-DRL achieves joint optimization by leveraging DRL's adaptability to changing channel conditions. This innovative integration enhances real-time flexibility, minimizes pilot overhead, and ensures efficient RF chain distribution, offering a robust solution for mm-wave multi-user mMIMO systems in B5G networks. The novelty of the proposed AB-HP framework lies in its unique integration of angular domain information and RBT-DRL to simultaneously optimize EE and SE in mm-wave multi-user mMIMO systems for B5G networks. While DRL-based hybrid precoding has been explored in prior studies, the proposed framework distinguishes itself by dynamically leveraging angular information to minimize RF chain utilization and channel estimation complexity, a feature not comprehensively addressed in existing work. Furthermore, including the RBT-DRL mechanism enables dual optimization of EE and SE while drastically reducing the beam training overhead, thereby improving system flexibility and adaptability to dynamic network conditions.