# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 7 Conclusions  
This paper proposes a crowdsourced fingerprint signal ranging framework that integrates mobile edge computing (MEC) and swarm intelligence to address the limitations  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Index** &
\begin{tabular}{c} **k-Nearest** \\ **Neighbors** \\ \end{tabular} & **Decision Tree** & **Random Forest** \\ \hline Building error rate & 0.21\% & 0.42\% & 0.29\% \\ \hline Floor error rate & 0.24\% & 3.46\% & 0.55\% \\ \hline Average distance error & 1.28 m & 4.57 m & 4.08 m \\ \hline Probability (distance error \(<\) 10 m) & 97.30\% & 87.04\% & 92.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different localization methods.  
Figure 6: Cumulative probability distribution of errors for different methods.  
of traditional 5G fingerprint-based signal ranging technologies in terms of dynamic environment adaptability, data collection efficiency, and resource overhead--systematically resolving the challenges of providing reliable ranging services in complex scenarios. By designing a swarm intelligence-driven crowdsourced data collection network, GPS-enabled mobile terminals autonomously collect base station signal characteristics during movement, effectively replacing the inefficient manual drive-testing paradigm. This innovation significantly reduces data acquisition and operational costs while overcoming efficiency bottlenecks in ultra-dense network deployment.  
Building on this foundation, we developed a progressive fingerprint update algorithm that dynamically optimizes signal ranging reference data by fusing real-time crowdsourced inputs with historical fingerprint databases. Experimental results demonstrate that this mechanism achieves a building prediction error rate below 0.5% and a floor prediction error rate of approximately 1%. It effectively mitigates performance degradation over long-term operation and substantially improves signal ranging accuracy in GPS-denied environments, achieving a mean positioning error of less than 5 m, with 95% of devices demonstrating errors within 10 m. These metrics outperform traditional static methods in environmental adaptability and stability. Furthermore, through an edge-centric service architecture, fingerprint matching and trajectory estimation tasks are offloaded to MEC nodes. Combined with a lightweight computing engine, the system dramatically reduces core network transmission loads, efficiently leverages edge computing resources in dense terminal scenarios, and enhances service responsiveness and scalability.  
Validation experiments confirm that the framework achieves systemic breakthroughs in data collection cost, environmental adaptability, and service scalability through the co-design of crowdsourced data collection, dynamic fingerprint database updates, and edge-centric service architecture. The proposed framework completes environmental sensing processes within sub-second latency and updates fingerprint maps incrementally, demonstrating real-time capability that is essential for mission-critical applications. This work provides a practical solution for deploying 5G base station-assisted signal ranging technologies, advancing the real-world implementation of high-precision, GPS-independent localization services in dynamic and complex environments.  
Conceptualization, R.L. and L.S.; methodology, R.L.; software, Y.L.; validation, L.S., Y.L., and Z.D.; formal analysis, L.S.; investigation, Y.L.; resources, Z.D.; data curation, L.S.; writing--original draft preparation, R.L.; writing--review and editing, Y.L.; visualization, L.S.; supervision, Y.L. All authors have read and agreed to the published version of the