基于在线学习的自适应波束切换用于6G网络：提升效率与韧性
Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience
AI总结：
本文提出基于在线深度强化学习的自适应波束切换方法，通过增强状态表示和稳定性导向奖励函数，提升6G网络的链路稳定性和数据速率。
摘要：
自适应波束切换对于关键任务军事和商业6G网络至关重要，但面临来自高载波频率、用户移动性和频繁阻塞的主要挑战。尽管现有机器学习（ML）解决方案通常侧重于最大化瞬时吞吐量，这可能导致不稳定策略和高信令开销。本文提出了一种在线深度强化学习（DRL）框架，旨在学习一个操作稳定的策略。通过为DRL智能体配备包含阻塞历史的增强状态表示以及以稳定性为中心的奖励函数，我们使其能够优先考虑长期链路质量而非短暂收益。在使用Sionna库验证的具有挑战性的100用户场景中，我们的智能体实现了与反应性多臂老虎机（MAB）基线相当的吞吐量。具体而言，与传统DRL方法相比，我们提出的方法将链路稳定性提高了约43%，在保持高数据速率的同时，实现了与MAB相媲美的操作可靠性。这项工作表明，通过将优化目标重新定位到操作稳定性，DRL能够为下一代关键任务网络提供高效、可靠和实时的波束管理解决方案。
英文摘要：
Adaptive beam switching is essential for mission-critical military and commercial 6G networks but faces major challenges from high carrier frequencies, user mobility, and frequent blockages. While existing machine learning (ML) solutions often focus on maximizing instantaneous throughput, this can lead to unstable policies with high signaling overhead. This paper presents an online Deep Reinforcement Learning (DRL) framework designed to learn an operationally stable policy. By equipping the DRL agent with an enhanced state representation that includes blockage history, and a stability-centric reward function, we enable it to prioritize long-term link quality over transient gains. Validated in a challenging 100-user scenario using the Sionna library, our agent achieves throughput comparable to a reactive Multi-Armed Bandit (MAB) baseline. Specifically, our proposed framework improves link stability by approximately 43% compared to a vanilla DRL approach, achieving operational reliability competitive with MAB while maintaining high data rates. This work demonstrates that by reframing the optimization goal towards operational stability, DRL can deliver efficient, reliable, and real-time beam management solutions for next-generation mission-critical networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
