## 1 Introduction
### Motivation and Contributions  
This study aims to optimize a beam training technique that takes into account the present channel conditions and minimizes the error between the estimated and true channel parameters by enabling dual optimization of EE and SE while drastically lowering beam training. By minimizing beam training overhead and dynamically adapting to network conditions, RBT-DRL-based beam training optimizes EE and SE while enhancing system performance. Enhancing system performance depends on the relay station's and the BS's transmit power restrictions necessary to exhibit a unique AB-HP that maximizes EE-SE. This action was taken in response to DL pilot contamination. By leveraging EE results to reduce SE enactment and vice versa, a unified matrix was utilized to enhance the EE-SE trade-off through multi-object optimization. We develop methods for beam tracking, relay selection, joint user scheduling, and codebook optimization for millimeter-wave networks, according to the authors of [48]. Using DRL to reduce system latency, these techniques are based on the design of an online controller that schedules users dynamically and establishes their links. The speed and scalability of B5G wireless networks can be enhanced by mm-wave systems that dynamically adapt to changing channel conditions and user demands. By leveraging DRL's ability to handle complex optimization tasks in real-time, DRL-integrated user scheduling and link configuration can significantly improve the performance of mm-wave networks.  
The contributions of this article can be summarized as follows:  
* To emphasize utilizing an AB-HP and hybrid precoding technique tailored for large multi-user mMIMO systems. The hybrid precoding method, grounded in angular basis principles, mitigates the need for extensive RF chains and measurement overhead. Addressing the formidable challenges facing the realization of B5 GG wireless networks, specifically the demanding criteria for high bandwidth and EE, our approach aligns with recent literature directives. The literature advocates strategies to minimize interference and optimize transmission power and bandwidth allocation through the allocation of orthogonal sub-channels.
* To provide a comprehensive analysis of the prediction services that can be expected from B5G wireless networks, including their fundamental ideas and wide range of uses. To achieve accurate and high-resolution channel estimation, this includes a special emphasis on DL applications made for orthogonal frequency division multiplexing (OFDM) systems.
* To highlight difficulties that need to be solved to achieve improved computational efficiency using DL. Therefore, to enable optimal performance, attention must be directed to improving the accuracy of channel predictions.
* To exhibit a beam training algorithm leveraging the AB-HP constructed through a DNN. This algorithm operates within the wireless environment, adapting to changes in channels, and concurrently optimizing EE/SE. The objective is to enhance overall performance while reducing the overhead associated with beam training.
* To maximize the long-term predicted reward through interactions with the environment. The reward is designed to represent this balance, which will be optimized throughout the applied maximum-reward beam training using deep reinforcement learning (RBT-DRL) to minimize the overhead related to beam training while still achieving good EE or SE performance.