LLM 驱动多目标深度强化学习优化 5G UPF 选择
LLM-based Multi-Criteria Deep Reinforcement Learning for Dynamic Efficient 5G UPF Selection
把 LLM 的意图理解和强化学习决策拼在了一起：运营商用自然语言说需求，NWDAF 告警触发动态调整，5G UPF 选择比固定策略好 16.89%。做电信网络自动化或 DRL 落地的开发者可以看看实验设计。
一篇论文提出用深度强化学习（DRL）解决 5G 网络中用户面功能（UPF）的动态选择问题，核心创新是引入动态多目标奖励函数，而非固定奖励。该方法借助 LLM 将网络运营商意图和 NWDAF 异常告警转译为可执行的决策信号。
在真实 5G 仿真平台上的实验显示，该方案在延迟、功耗和流量分配三个目标之间取得平衡，较启发式基线和固定 DRL 方案提升 16.89%。论文验证了意图驱动 UPF 选择在真实环境中的可行性，对电信网络自动化研究有直接参考价值。
LLM-based Multi-Criteria Deep Reinforcement Learning for Dynamic Efficient 5G UPF Selection
User plane function (UPF) selection in 5G networks is an active area of research, with various limited approaches proposed to enhance network efficiency and manage resources effectively. In this paper, we address this gap in the related literature by proposing a Deep Reinforcement Learning (DRL) UPF selection method that utilizes a dynamic, multi-objective reward function and validating it on a real-world 5G emulation platform. The proposed method is based on the large language models' (LLMs) translation of network operator intents and the network data analytics function (NWDAF) abnormality alerts. The experimental results validated in the real-world experimental setup demonstrate that our dynamic DRL solution achieves an effective balance between latency, power consumption, and traffic distribution, outperforming the heuristic baselines and fixed DRL by 16.89%. These findings highlight the real-world applicability of our approach for dynamic, intent-driven UPF selection.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：深度强化学习、5G、LLM、NWDAF，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Abdelrahman Soliman, Ahmed Refaey Hussein, Lei Lei），经 AITOP 多维评分管线结构化提炼。