Data driven approach for Outdoor Channel Prediction in 5G and Beyond

TL;DR

This paper proposes a data-driven channel estimation method for 5G and beyond, using machine learning models trained on ray-traced data at 7GHz to improve efficiency and accuracy.

Contribution

It introduces a machine learning-based approach for outdoor channel prediction, demonstrating linear regression's superior performance over other models at 7GHz.

Findings

Linear Regression achieved MAE of 7.5155×10⁻⁵ and RMSE of 9.2861×10⁻⁵.

The data-driven approach can be deployed as a digital twin in 5G networks.

Linear Regression outperformed Support Vector Regression and Decision Tree Regression.

Abstract

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.