用数据驱动方法预测7GHz频段信道，提升5G+网络性能
Data driven approach for Outdoor Channel Prediction in 5G and Beyond
- 基于射线追踪生成数据，用线性回归预测信道系数
- 线性回归在7GHz频段达到MAE 7.5155×10⁻⁵、RMSE 9.2861×10⁻⁵
- 适合5G及未来网络中的数字孪生场景部署
无线通信向5G及更远演进，提升了服务质量与用户体验。准确理解和估计信道信息对实现优质体验至关重要。传统信道估计依赖周期性发送导频信号，不仅增加计算开销，也带来通信负担。为此，本文提出一种数据驱动的信道估计方法，可应用于5G及未来网络的数字孪生系统。研究聚焦于7GHz频段下特定用户位置的信道预测，通过射线追踪生成数据，并训练包含发射机位置、用户位置等特征变量的机器学习模型，目标为预测信道系数。对比了线性回归、支持向量回归和决策树回归三种方法。仿真结果表明，线性回归表现最优，平均绝对误差（MAE）为7.5155×10⁻⁵，均方根误差（RMSE）为9.2861×10⁻⁵，优于其他两种方法。
原文摘要 · Abstract (English)
An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional methods of channel estimation involves periodically sending pilots (known signals), estimating channel and send back estimated channel information to the BS which increases computational complexity and communication complexity. Hence, we focus on data driven approach for channel estimation. This work can be deployed as Digital twin in 5G and beyond wireless networks. In this work, we explore a channel estimation mechanism at 7GHz frequency band for a given user location. This work involves data generation using Ray tracing mechanism and Machine learning model training that contains feature variables such as transmitter location, user location and target variable as channel coefficient . We explored Linear Regression, Support Vector Regression and Decision Tree Regression. We found via simulations that Linear Regression performs (with MAE of $\mathbf{7.5155\times10^{-5}}$ and RMSE of $\mathbf{9.2861\times10^{-5}}$) better than Support Vector Regression and Decision Tree Regression.
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