Paper
A Hybrid Model-Assisted Approach for Path Loss Prediction in Suburban Scenarios
Authors
Chenlong Wang, Bo Ai, Ruiming Chen, Ruisi He, Mi Yang, Yuxin Zhang, Weirong Liu, Liu Liu
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.
Metadata
Related papers
Gen-Searcher: Reinforcing Agentic Search for Image Generation
Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, Kaixuan Fan, Yilei Jian... • 2026-03-30
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or • 2026-03-30
Graphilosophy: Graph-Based Digital Humanities Computing with The Four Books
Minh-Thu Do, Quynh-Chau Le-Tran, Duc-Duy Nguyen-Mai, Thien-Trang Nguyen, Khan... • 2026-03-30
ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining
Anuj Diwan, Eunsol Choi, David Harwath • 2026-03-30
RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
Oliver Aleksander Larsen, Mahyar T. Moghaddam • 2026-03-30
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.09808v1</id>\n <title>A Hybrid Model-Assisted Approach for Path Loss Prediction in Suburban Scenarios</title>\n <updated>2026-03-10T15:37:20Z</updated>\n <link href='https://arxiv.org/abs/2603.09808v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.09808v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.SP'/>\n <published>2026-03-10T15:37:20Z</published>\n <arxiv:primary_category term='eess.SP'/>\n <author>\n <name>Chenlong Wang</name>\n </author>\n <author>\n <name>Bo Ai</name>\n </author>\n <author>\n <name>Ruiming Chen</name>\n </author>\n <author>\n <name>Ruisi He</name>\n </author>\n <author>\n <name>Mi Yang</name>\n </author>\n <author>\n <name>Yuxin Zhang</name>\n </author>\n <author>\n <name>Weirong Liu</name>\n </author>\n <author>\n <name>Liu Liu</name>\n </author>\n </entry>"
}