Deep Reinforcement Learning-Driven Hybrid Precoding for Efficient Mm-Wave Multi-User MIMO Systems

High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks.