# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 2 Background and Related Work
### Wireless Signal Ranging Technology
#### 2.1.3 LoRa-Assisted Signal Ranging  
LoRa (long range)-assisted signal ranging, a low-power wide-area network (LPWAN)-based solution, addresses the demand for coarse-grained localization in scenarios such as wild asset tracking and agricultural monitoring, leveraging kilometer-level signal propagation and ultra-low power consumption. Device ranging primarily involves analyzing signal characteristics such as received signal strength (RSSI), propagation time, and phase variations. The physical layer properties of LoRa, including chirp spread spectrum (CSS) modulation and sub-GHz ISM band operation, provide strong penetration and high link budgets, making it suitable for challenging environments.  
However, significant challenges persist. LoRa signals tend to degrade below noise levels after penetrating obstacles, adversely impacting RSSI- and TDOA-based localization methods. Furthermore, the narrowband nature of LoRa limits the sharpness of time-domain waveforms, restricting the achievable performance of time-of-arrival (TOA) techniques [21]. Recent efforts have sought to overcome these challenges. [PERSON] et al. proposed an improved RSSI-based LoRa localization system for drones by integrating sensor-derived altitude data and employing Huber loss in multilateration, significantly enhancing localization accuracy in both simulation and real-world settings [22]. [PERSON] et al. developed a compact, machine learning-optimized wearable monopole antenna for LoRa tracking,which improved RSSI performance by 3 dBm and maintained high efficiency even under bending, advancing LoRa-based wearable localization [23]. [PERSON] et al. introduced an unsupervised symbolization approach for outdoor LoRa localization, leveraging maximum entropy partitioning and D-Markov machines to eliminate the need for offline training and adapt to dynamic environments [24]. [PERSON] et al. proposed SynLoc, a TDoA-based LoRa localization framework that corrects gateway time drift at the nanosecond level, achieving over 2\(\times\) the accuracy and scalability improvements in multi-node concurrent localization [25]. [PERSON] et al. designed a series of RSSI-based algorithms for large-scale LoRa localization, focusing on mitigating Gaussian and non-Gaussian noise effects, and demonstrated that their system can achieve performance comparable to GPS in outdoor scenarios [26]. [PERSON] et al. developed a mobile-anchor-based multi-target outdoor localization scheme that optimizes path planning to minimize anchor movement while maintaining signal ranging accuracy, thereby effectively reducing energy consumption in large outdoor deployments [27]. These works collectively demonstrate that enhancing signal processing, hardware design, synchronization accuracy, and learning algorithms are key strategies to overcome the inherent challenges of LoRa-based localization in diverse applications.  
Despite these advancements, LoRa-assisted localization remains best suited for low-precision, wide-area applications due to inherent limitations in accuracy and deployment density, particularly when compared to base station-assisted solutions.  
In contrast to base station-assisted signal ranging technology, Bluetooth-assisted signal ranging is confined to short-range device localization and cannot address the demands of large-scale or wide-area deployments. Meanwhile, LoRa-assisted signal ranging, constrained by its sparse deployment, is primarily applicable to low-precision scenarios such as agricultural monitoring and logistics tracking, with limited adaptability. Given the high-density deployment of 5G base stations, this study focuses on base station-assisted signal ranging technology, which enables refined signal ranging services in complex urban environments and overcomes the coverage limitations inherent to traditional short-range communication technologies.