摘要
5G 毫米波通信频带宽、速率高，却面临信道复杂、能耗大等难题。本研究聚焦其波束成形算法，利用深度学习、混合架构与智能反射面技术优化。经仿真与实测，优化算法精准追踪信道，增强信号、降低干扰，能耗显著降低，有效提升通信质量，为 5G 毫米波通信商用化加速。
关键词： 5G 毫米波；波束成形；深度学习；混合架构；智能反射面
Abstract
5G millimeter-wave communication has high bandwidth and speed but faces challenges such as complex channels and high energy consumption. This study focuses on its beamforming algorithms, optimizing them using deep learning, hybrid architectures, and intelligent reflector technology. Through simulation and measurement, the optimized algorithm accurately tracks the channel, enhances signals, reduces interference, significantly lowers energy consumption, and effectively improves communication quality, accelerating the commercialization of 5G millimeter-wave communications.
Key words： 5G millimeter wave; Beamforming; Deep learning; Hybrid architecture; Intelligent reflector
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