用AI预测5G O-RAN延迟，提升通信效率。
Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting
- 采用双向LSTM模型实时预测网络延迟
- 在真实原型中实现损失低于0.04的精准预测
- 开源框架支持可扩展部署，适合运营商与研发者
5G开放无线接入网（O-RAN）日益复杂且动态变化，给保持低延迟、高吞吐量和资源效率带来挑战。现有基于机器学习的延迟预测与资源管理方法常缺乏实际可扩展性与硬件验证。本文提出一种集成于功能型O-RAN原型的人工智能延迟预测系统，采用双向长短期记忆模型（bidirectional LSTM）在基于FlexRIC构建的可扩展开源框架中实现实时延迟预测。实验结果表明，该模型在动态5G环境中表现优异，损失指标低于0.04，验证了其实际应用可行性。
原文摘要 · Abstract (English)
The increasing complexity and dynamic nature of 5G open radio access networks (O-RAN) pose significant challenges to maintaining low latency, high throughput, and resource efficiency. While existing methods leverage machine learning for latency prediction and resource management, they often lack real-world scalability and hardware validation. This paper addresses these limitations by presenting an artificial intelligence-driven latency forecasting system integrated into a functional O-RAN prototype. The system uses a bidirectional long short-term memory model to predict latency in real time within a scalable, open-source framework built with FlexRIC. Experimental results demonstrate the model's efficacy, achieving a loss metric below 0.04, thus validating its applicability in dynamic 5G environments.
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