# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 3 Solution Design  
We constructed a multi-layer collaborative 5G wireless signal ranging framework based on mobile edge computing (MEC) [32] and crowdsourcing intelligence [33], as shown in Figure 1. This framework dynamically collects environmental data through the terminal perception layer, optimizes ranging services in real time through the edge computing layer, and coordinates resources globally through the cloud coordination layer, forming a closed-loop intelligent positioning system. It breaks through the static bottleneck of traditional architectures, relies on crowdsourcing intelligence for low-cost data acquisition, combines edge computing for dynamic incremental updates to the fingerprint database, and strikes a balance between privacy protection and computational efficiency, meeting the real-time ranging demands in complex 5G scenarios.  
Specifically, the framework is divided into three layers--the cloud coordination layer manages the dynamic signal fingerprint database and global collaborative optimization, maintaining the reference model and dynamically adjusting the collaboration between MEC nodes. The edge computing layer serves as the core hub, deploying a lightweight ranging service and incremental signal fingerprint update engine with multi-algorithm collaboration, achieving cross-node load balancing through dynamic task scheduling. The terminal perception layer constructs a crowdsourced data collection network, and through  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Reference** & **Contribution** \\ \hline Explainable models & [PERSON]/[PERSON] et al., 2023 [18] & Explainable BLE localization using SHAP-based interpretation. \\ \hline Dynamic fingerprinting & [PERSON] et al., 2020 [20] & Dynamic fingerprint window (DFW-WKNN) for adaptive BLE fingerprint matching. \\ \hline \multicolumn{3}{c}{**LoRa-assisted Signal Ranging**} \\ \hline Altitude-aided localization & [PERSON] et al., 2024 [22] & Drone LoRa localization using altitude fusion and Huber loss. \\ \hline Wearable hardware optimization & [PERSON] et al., 2024 [23] & ML-optimized wearable antenna for improved LoRa tracking. \\ \hline Unsupervised learning & [PERSON] et al., 2024 [24] & Unsupervised symbolization for dynamic LoRa localization. \\ \hline Drift correction & [PERSON] et al., 2024 [25] & SynLoc framework correcting gateway drift in multi-node LoRa networks. \\ \hline Energy-efficient outdoor localization & [PERSON] et al., 2023 [27] & Mobile-anchor-based multi-target outdoor LoRa localization with path optimization. RSSI noise mitigation for robust large-scale LoRa positioning. \\ \hline Physical challenge analysis & [PERSON] et al., 2018 [21] & Analysis of challenges in RSSI, TDOA, and TOA for LoRa localization. \\ \hline \multicolumn{3}{c}{**Environment perception fingerprint collection**} \\ \hline Fine-grained Grid Model & [PERSON] et al., 2024 [28] & Fine-grained grid model with machine learning for indoor localization. \\ \hline Bag-of-features ML approach & [PERSON] et al., 2022 [31] & Machine learning Bag-of-features approach for WLAN fingerprinting. \\ \hline Uniform grid + RSSI matching & [PERSON] et al., 2015 [30] & Uniform grid-based RSSI fingerprinting achieving 2–4 m accuracy. \\ \hline Irregular grid layout & [PERSON] et al., 2014 [29] & Irregular grid map design to optimize coverage and reduce errors. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._encrypted transmission and policy adjustment mechanisms, transforms a large number of mobile devices into environmental perception units.