用手机实测数据，机器学习预测5G下行速率和误码率。
ML-Based Real-Time Downlink Performance Prediction in Standalone 5G NR Using Smartphones
- 基于手机采集的信道质量等物理层参数，训练回归模型。
- 在多种场景下，吞吐量与误码率预测误差低于10%。
- 适合网络优化、移动应用性能分析人员参考。
我们提出一种基于机器学习的5G下行链路性能预测框架，利用商用现成（COTS）用户设备（UE）进行实时测量。实验平台采用部署在戴尔台式机上的srsRAN 5G NR栈作为5G下一代基站（gNB），工作频段为3.4 GHz。使用两部谷歌Pixel 7a智能手机采集包括信道质量指示符（CQI）、调制编码方案（MCS）、比特率、传输时间间隔（TTI）和块误码率（BLER）在内的物理层特征，作为模型训练的输入。通过Ookla等商用流量生成工具，在视距（LOS）与非视距（nLOS）条件下开展静止与移动测试，数据涵盖全球Ookla服务器（如美国、葡萄牙、加纳、埃及、日本）、iperf TCP/UDP数据及YouTube视频流会话。为分析用户间干扰，还设置了多用户同位置场景。评估了五种监督回归模型：线性回归、决策树回归、随机森林回归、极端梯度提升（XGBoost）、轻量梯度提升机（LightGBM）。结果表明，仅用COTS硬件与标准机器学习方法，即可在多样化的实际5G场景中准确预测吞吐量与BLER。
原文摘要 · Abstract (English)
We propose a machine learning (ML)-based framework for downlink performance prediction in 5G networks using real-time measurements from commercial off-the-shelf (COTS) user equipment (UE). Our experimental platform integrates the srsRAN 5G New Radio (NR) stack deployed on a Dell desktop serving as the 5G next generation nodeB (gNB), operating at 3.4 GHz. Two Google Pixel 7a smartphones are used to collect physical layer characteristics such as channel quality indicator (CQI), modulation and coding scheme (MCS), bit rate, transmission time interval (TTI), and block error rate (BLER), which are leveraged as predictors in model training. We use commercial-grade traffic generation tools, including Ookla, for stationary and mobility measurements under line-of-sight (LOS) and non-line-of-sight (nLOS) conditions. Test data includes global Ookla servers (e.g., USA, Portugal, Ghana, Egypt, Japan), iperf TCP/UDP data, and video streaming sessions from YouTube. To analyze inter-user interference, we also include scenarios with multiple UEs at the same location. We evaluate the predictive performance of five supervised regression models - linear regression, decision tree regression, random forest regression, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM). Our results demonstrate that throughput and BLER can be accurately predicted using COTS hardware and standard ML techniques in diverse real-world 5G scenarios.
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