A Hybrid Model-Assisted Approach for Path Loss Prediction in Suburban Scenarios

TL;DR

A model assisted hybrid path loss prediction method that introduces an environment adaptive compensation on top of the classic close-in free-space reference distance (CI) path loss model is proposed.

Abstract

Accurate path loss prediction is crucial for wireless network planning and optimization in suburban environments with complex terrain variation and diverse land cover. This paper proposes a model assisted hybrid path loss prediction method that introduces an environment adaptive compensation on top of the classic close-in free-space reference distance (CI) path loss model. By jointly predicting the path loss exponent and a compensation term, the proposed approach dynamically adjusts the empirical trend. To improve the effectiveness of environmental representation, three environmental image organization schemes are constructed and evaluated. Experiments on measurement data collected in Pingtan Island show that the proposed method outperforms the CI model and a conventional model assisted baseline, achieving a test root mean square error of 4.04 dB.

Figures (5)

• Figure 1: Overview of the measurement data distribution in Pingtan, Fujian Province, China. The red routes indicate the training set, the yellow routes indicate the validation set, and the blue routes indicate the test set.
• Figure 2: Illustration of the three environmental image input formats. (a) Resize format. (b) Stacksize format. (c) Fullsize format.
• Figure 3: Overview of the proposed model-assisted hybrid architecture, including the dual-branch feature extraction module, the MHSA-based feature fusion module, and the environmental compensation prediction module.
• Figure 4: Test RMSE comparison under different environmental image formats and baseline configurations.
• Figure 5: Path loss fitting on two test routes and the corresponding predicted path loss exponent.