A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture

Rui Lu, Lei Shi (), Yinlong Liu () and Zhongkai Dang
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Rui Lu: National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
Lei Shi: National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
Yinlong Liu: Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China
Zhongkai Dang: National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China

Future Internet, 2025, vol. 17, issue 5, 1-22

Abstract: In complex indoor and outdoor scenarios, traditional GPS-based ranging technology faces limitations in availability due to signal occlusion and user privacy issues. Wireless signal ranging technology based on 5G base stations has emerged as a potential alternative. However, existing methods are limited by low efficiency in constructing static signal databases, poor environmental adaptability, and high resource overhead, restricting their practical application. This paper proposes a 5G wireless signal ranging framework that integrates mobile edge computing (MEC) and crowdsourced intelligence to systematically address the aforementioned issues. This study designs a progressive solution by (1) building a crowdsourced data collection network, using mobile terminals equipped with GPS technology to automatically collect device signal features, replacing inefficient manual drive tests; (2) developing a progressive signal update algorithm that integrates real-time crowdsourced data and historical signals to optimize the signal fingerprint database in dynamic environments; (3) establishing an edge service architecture to offload signal matching and trajectory estimation tasks to MEC nodes, using lightweight computing engines to reduce the load on the core network. Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. The proposed framework outperforms traditional static methods by 3× in ranging accuracy while maintaining computational efficiency, achieving significant improvements in environmental adaptability and service scalability.

Keywords: 5G network; environment perception service; mobile edge computing (MEC); signal fingerprint (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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