基于机器学习的城市环境跨频段跨场景路径损耗预测方法.

廖希; 周萍; 周思洋; 陈心睿; 王洋; 何占林

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 environmental 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 method 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.

STANDARD deviations; PREDICTION models; ENVIRONMENTAL auditing; RANDOM forest algorithms

Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) / Chongqing Youdian Daxue Xuebao (Ziran Kexue Ban), 2024, Vol 36, Issue 6, p1099

1673-825X

Academic Journal

10.3979/j.issn.1673-825X.202408280227