---
格式版本: 2
标题: "A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN"
原文链接: "https://arxiv.org/abs/2608.11083"
发布日期: "2026-08-11"
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发布时间校准时间: "2026-08-12T11:14:58+08:00"
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发现时间: "2026-08-12T11:13:54+08:00"
入库时间: "2026-08-12T03:14:58.784Z"
来源平台: "arXiv 学术论文搜索"
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AI优质: "否"
AI打分: 10
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "论文主题为LoRa LPWAN的路径损耗预测，与超节点、AI Rack等机柜级AI基础设施完全无关，仅命中通用词deployment，属于明显无关资料。"
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AI质检时间: "2026-08-12T11:15:05+08:00"
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采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11083"
---

## Computer Science > Networking and Internet Architecture

## Title:A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN

[View PDF](https://arxiv.org/pdf/2608.11083) [HTML (experimental)](https://arxiv.org/html/2608.11083v1)

> Abstract:Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning. Traditional (empirical) propagation models often exhibit limited accuracy in these scenarios. We investigate machine learning models for LoRa path loss prediction, systematically analyzing how prediction accuracy scales with training set size using real-world measurements from an urban deployment. Our approach employs a Random Forest with LiDAR-derived terrain features and k-Nearest Neighbors with coordinate data, comparing their performance against established empirical models and specialized LPWAN models. Under random pooled splits, both ML models consistently outperform the considered baseline models across the evaluated training-set sizes. At maximum training size, they achieve RMSE values below 6.5 dB compared to 9.7 dB for the best baseline, indicating accurate within-deployment interpolation. A leave-one-gateway-out check qualifies this result: RF shows placement-dependent transfer to held-out gateways, with moderate degradation for several gateways but larger errors for others, whereas coordinate-only k-NN degrades substantially when the gateway location is unseen

| Comments: |  |
| --- | --- |
| Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG) |
| Cite as: | [arXiv:2608.11083](https://arxiv.org/abs/2608.11083) \[cs.NI\] |
|  | (or [arXiv:2608.11083v1](https://arxiv.org/abs/2608.11083v1) \[cs.NI\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11083](https://doi.org/10.48550/arXiv.2608.11083) |

## Submission history

From: Michael Rademacher \[[view email](https://arxiv.org/show-email/9c240d4e/2608.11083)\]  
**\[v1\]** Tue, 11 Aug 2026 15:48:54 UTC (379 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11083) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
