Cross-frequency band and cross-scenario path loss prediction method in urban environments based on machine learning
To address the limitations of traditional path loss models,which fail to account for environmental information and perform poorly in cross-scenario and cross-band predictions,an environment-driven path loss prediction method for cross-band and cross-scenario applications is proposed.The method combines two-dimensional linear and rectangular environmen-tal features to describe the propagation environment and incorporates transfer learning into a random forest-based path loss prediction model.Two urban scenarios were constructed:Scenario 1 includes frequency bands of 140,220,280,and 300 GHz,while Scenario 2 focuses on the 140 GHz band.The method uses datasets at 140 and 220 GHz to predict path loss at 280 and 300 GHz and employs Scenario 1 data to predict Scenario 2 path loss.Results demonstrate that the proposed meth-od reduces the root mean square error(RMSE)for achieving cross-band predictions at 280 and 300 GHz by 3.331 1 and 4.321 5 dB and for cross-scenario predictions by 0.724 4 dB compared to methods without transfer learning optimization.
machine learningsub-terahertzpath loss predictionrandom foresttransfer learning
万方数据
