基于深度强化学习的自适应混合光射频传输用于超可靠低延迟通信
Adaptive Hybrid Optical RF Transmission Based on Deep Reinforcement Learning for Ultra-Reliable Low Latency Communication
- Enugu State University of Sci. & Tech.（埃努古州科学技术大学）
- University of Greenwich（格林威治大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于深度强化学习的混合光射频传输框架，自适应选择模式，实现URLLC下延迟降低35%、吞吐量提升25-30%，并保持可靠性超99.999%。
AI中文摘要：
超可靠低延迟通信（URLLC）要求亚毫秒级延迟和高于99.999%的可靠性，这在动态无线和大气条件下难以保证。尽管自由空间光（FSO）链路提供高容量传输，但其性能在湍流和雾天条件下会下降，而射频（RF）链路虽提供鲁棒性但带宽有限。本文提出一种基于强化学习的混合光射频框架，自适应选择传输模式以满足严格的URLLC要求。混合决策过程被建模为马尔可夫决策过程，并使用深度Q网络（DQN）控制器进行优化。在不同衰减和衰落场景下的大量仿真表明，与仅RF和静态混合方案相比，延迟降低高达35%，吞吐量提升25%至30%，同时保持可靠性高于99.999%。一个工作在1550 nm和28 GHz的实验室规模测试平台进一步验证了该方法，显示仿真与实测结果之间的偏差小于6%。所提出的框架展示了智能混合传输在任务关键型下一代无线网络中的实际可行性。
英文摘要：
Ultra Reliable Low-Latency Communication (URLLC) demands submillisecond latency and reliability above 99.999%, which are difficult to guarantee under dynamic wireless and atmospheric conditions. Although free space optical (FSO) links provide high-capacity transmission, their performance degrades under turbulence and fog, while radiofrequency (RF) links offer robustness but limited bandwidth. This paper presents a reinforcement learning-based hybrid optical RF framework that adaptively selects transmission modes to satisfy stringent URLLC requirements. The hybrid decision process is modeled as a Markov Decision Process and optimized using a Deep Q Network (DQN) controller. Extensive simulations under varying attenuation and fading scenarios demonstrate up to 35% latency reduction and 25 to 30 % throughput improvement compared to RF-only and static hybrid schemes, while maintaining reliability above 99.999%. A laboratory-scale testbed operating at 1550 nm and 28 GHz further validates the approach, showing less than 6% deviation between simulated and measured results. The proposed framework demonstrates the practical feasibility of intelligent hybrid transmission for mission-critical next generation wireless networks.
