摘要：在全球“双碳”战略推动下，5G基站高能耗问题亟待解决。剖析了5G基站商用节能存在的四大关键问题：凝露引发的硬件风险、多制式共用射频限制节能效果、性能与感知下降，以及复杂场景节能策略难定义。针对性地提出软硬件协同、频谱规划优化和感知-能耗动态平衡等方案，基于人工智能和大模型构建“大模型+大数据”驱动的逆向节能决策体系，为5G网络实现高效、智能、可靠节能提供系统性解决路径。

关键词：5G；节能；神经网络；大模型

Abstract：Driven by the global "dual-carbon" strategy, the high energy consumption of 5G base stations has become an urgent issue to address. This paper analyzes four key challenges in the commercial deployment of 5G energy-saving technologies: hardware risks induced by condensation, limited energy-saving effectiveness due to multi-mode shared radio frequency units, degradation in network performance and user perception, and difficulties in defining energy-saving strategies for complex scenarios. To address these challenges, we propose targeted solutions, including hardware-software co-design, spectrum planning optimization, and dynamic balancing between user perception and energy efficiency. Furthermore, leveraging artificial intelligence and large models, we establish a "large model + big data"-driven inverse energy-saving decision framework, providing a systematic approach to achieve efficient, intelligent, and reliable energy savings for 5G networks.

Keywords: 5G; energy-saving; neural network; large model