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

TL;DR

This paper introduces a hybrid path loss prediction method that adaptively compensates for environmental factors in suburban areas, significantly improving accuracy over traditional models.

Contribution

It presents a novel environment adaptive compensation technique combined with environmental image organization schemes for enhanced path loss prediction.

Findings

Outperforms traditional CI model with 4.04 dB RMSE

Effective environmental representation improves prediction accuracy

Hybrid approach adapts to complex terrain variations

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.