# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 3 Solution Design
### Mobile Network Edge Deployment  
In the 5G wireless signal ranging framework, mobile network edge deployment is a core component for achieving low-latency, high-efficiency, and high-precision ranging services. By offloading computational tasks from the core network to MEC (mobile edge computing) servers close to the users, the latency in the ranging process can be significantly reduced, and system resource usage can be optimized to meet the real-time ranging demands in dynamic environments. First, the design requires the reasonable deployment of MEC servers. These servers should cover multiple key points within the target area, especially areas with high base station density and complex signal environments. Each MEC server must possess powerful computational capacity and storage capacity to support real-time signal data processing and dynamic updating and optimization of the signal fingerprint database. In addition, efficient collaboration between different MEC nodes is necessary to ensure data sharing and collaborative computing, improving the overall system's response speed and accuracy. Next, terminal devices collect and upload signal data by connecting to the nearest MEC server. User equipment not only collects wireless signal features, such as signal strength and phase information, but also provides other relevant information about the device. By transmitting the data to the MEC server, the system can perform real-time analysis and computation using edge computing capabilities, reducing dependency on remote servers and the core network, thus alleviating the network burden and improving the real-time performance of the ranging system. Even in environments where GPS is unavailable or signals are limited, the system can still collect signal data using network-based indirect inference methods, ensuring the system's high availability in various environments. In terms of data processing, the system adopts an incremental update mechanism. Whenever new data are uploaded from the terminal to the MEC node, the edge computing platform dynamically updates the fingerprint database based on the new signal features. This progressive update method effectively avoids the high cost and low efficiency problems associated with traditional static signal database updates, and it can better adapt to environmental changes, such as variations in base station density or user behavior. Let \(S_{i}\) represent the signal features at the \(i\)-th collection point, and let \(F(t)\) represent the signal  
Figure 1: Overview of the 5G wireless signal ranging framework. fingerprint database at time \(t\). The new fingerprint database \(F(t+1)\) can be dynamically updated using Equation (1):  
\[F(t+1)=F(t)+\alpha\cdot\Delta F(t) \tag{1}\]  
where \(\Delta F(t)\) represents the increment of the new signal features collected at time \(t\), and \(\alpha\) is a parameter that controls the update rate. Through this incremental update mechanism, the system can real-time optimize the fingerprint database based on newly collected data, avoiding the need for full re-collection and the high computational overhead associated with traditional methods. In the actual ranging process, the accuracy of signal matching is crucial for the system's performance. To accurately match signal features and estimate the device's distance, we adopt a weighted matching method based on signal strength and propagation models. Let \(d_{i}\) represent the distance between the \(i\)-th device and the base station, and \(P_{i}\) represent the signal strength received by the device. The relationship between signal strength and distance can be calculated using Equation (2):  
\[P_{i}=P_{0}-10n\log_{10}(d_{i})+X_{\sigma} \tag{2}\]  
where \(P_{0}\) is the reference signal strength, \(n\) is the path loss exponent, and \(X_{\sigma}\) is Gaussian noise with zero mean and variance \(\sigma^{2}\). The system can estimate the distance between the device and the base station based on the received signal strength, thereby performing signal fingerprint matching and updating.