# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 1 Introduction  
In emerging fields such as smart cities and industrial Internet of Things (IoT), continuous and reliable ranging services have become fundamental in supporting key applications like autonomous driving and emergency response. However, existing ranging technologies face dual real-world constraints--first, in complex environments such as indoor parking lots, underground transportation hubs, and urban high-rise buildings, GPS signals frequently fail due to physical obstructions; second, user privacy protection policies lead to a large number of mobile devices actively disabling ranging features, further limiting the availability of traditional ranging solutions. Against this backdrop, with the commercialization of 5G networks and the widespread deployment of 5G base stations [1], how to-levelrage ubiquitous 5G base station networks to achieve GPS-independent or weak-GPS-dependent ranging services has become a focal point for both academia and industry. Wireless signal ranging technology based on 5G signals estimates the distance of a terminal by analyzing the characteristics of base station signals, such as signal strength and phase, providing a potential solution to the aforementioned challenges. However, the practical deployment of this technology faces core challenges such as poor dynamic environmental adaptability and high operational and maintenance costs. This study proposes an innovative solution that integrates mobile edge computing (MEC) [2] and crowdsourcing intelligence [3], aiming to promote the evolution of high-precision ranging services toward low-cost and high-robustness directions. Current research on base station-based wireless signal ranging mainly focuses on the following three areas: (1) constructing static signal databases based on professional equipment; (2) using machine learning algorithms to improve signal matching accuracy; (3) updating signal databases through periodic re-collection. However, the existing methods expose the following key flaws in engineering practice:  
1. **Data collection efficiency bottleneck**: Traditional signal database construction relies on manual drive tests, which require professional teams to collect signal features point by point. In a scenario involving ultra-dense 5G networks, the reduced coverage of each base station dramatically increases the number of points to be collected, resulting in long data collection cycles and high costs, severely restricting the scalability of the technology. 2. **Insufficient dynamic environmental adaptability**: Existing static signal databases fail to adapt to dynamic changes in wireless signal propagation paths, leading to significant deviations in ranging results over time. Although some studies have attempted to introduce re-collection mechanisms, their high operational and maintenance costs are not sustainable. 3. **Limited system scalability**: Cloud-based signal matching architectures require terminal measurement data to be transmitted back to remote servers, increasing network latency. Additionally, schemes using complex AI models for incremental updates impose excessive demands on base station computational resources, making it difficult to support large-scale concurrent requests from terminals. To address the above challenges, this paper proposes a 5G crowdsourced wireless signal ranging framework based on MEC. We first design a data collection mechanism driven by crowdsourcing intelligence, constructing a crowdsourced data collection network with user terminals participating collaboratively. Devices equipped with GPS functionality automatically collect device signal features and category labels during mobility. This mechanism breaks through the traditional manual drive test efficiency bottleneck and achieves continuous, low-cost signal data acquisition. Secondly, we deploy a lightweight signal update engine on the MEC server.