基于DDPG的STAR-RIS辅助6G网络智能切换
DDPG-Based Intelligent Handover For STAR-RIS-Assisted 6G Networks
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中文总结 AI 辅助
针对6G网络中STAR-RIS辅助系统的切换管理问题，提出基于DDPG的基站部署优化方法，以最大化最差信噪比，消除覆盖盲区，显著提升动态移动性下的切换成功率与网络韧性。
中文摘要 AI 辅助
智能切换管理是未来6G网络的关键能力。本文研究了由同时透射和反射可重构智能表面（STAR-RIS）辅助的系统中的增强切换策略。此外，本文还探讨了多个基站（BS）的最优部署位置，以进一步提升网络性能。为消除覆盖盲区，采用深度确定性策略梯度（DDPG）框架强制执行最大-最小公平性目标，直接最大化最差情况下的信噪比（SNR）。通过动态移动性压力测试评估该优化拓扑，模拟180名行人在随机瞬态阻塞下执行A3事件切换。结果表明，DDPG部署相比随机基线平均高出3.17 dB，实现了24 dB的最差情况信噪比和0.963的Jain公平性指数。至关重要的是，这种最优静态部署显著提升了动态移动性韧性，确保100%的切换成功率，并将故障后中断时间降至可忽略的0.01%。
英文摘要
Intelligent handover management is a key capability for future 6G networks. This paper investigates enhanced handover strategies in systems assisted by Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS). In addition, it explores the optimal placement of multiple base stations (BSs) to further improve network performance. To eliminate coverage dead zones, a Deep Deterministic Policy Gradient (DDPG) framework is utilized to enforce a Max-Min Fairness objective, directly maximizing the worst-case Signal-to-Noise Ratio (SNR). This optimized topology is evaluated via a dynamic mobility stress test simulating 180 pedestrians executing A3-event handovers under stochastic transient blockages. Results demonstrate that the DDPG deployment outperforms random baselines by a 3.17 dB mean gap, achieving a 24 dB worst-case SNR and a 0.963 Jain's fairness index. Crucially, this optimal static placement drastically improves dynamic mobility resilience, ensuring a 100% successful handover rate and reducing post-failure outage time to a negligible 0.01%.
发表机构
- The German University in Cairo（开罗德国大学）
- The German International University（德国国际大学）
机构由 AI 辅助整理，请以论文原文为准。
