# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 2 Background and Related Work
### Wireless Signal Ranging Technology
#### 2.1.1 Base Station-Assisted Signal Ranging  
Base station-assisted ranging leverages cellular infrastructure to provide localization over large areas. It primarily adopts three approaches based on signal characteristics, that is, time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength indicator (RSSI/CSI)-based methods. TDOA determines device location by measuring signal arrival time differences at multiple base stations, constructing hyperbolic intersections, with accuracy dependent on clock synchronization. AOA estimates signal direction using antenna arrays, requiring measurements from at least two base stations for triangulation. RSSI/CSI-based methods utilize pre-built signal fingerprints, with deep learning techniques enabling sub-meter accuracy by extracting time-frequency features from channel state information in multipath environments. Among these techniques, TDOA and AOA offer higher accuracy and stability in certain environments due to their independence from signal strength. For instance, [PERSON] et al. introduced a TDOA algorithm that eliminates the need for base station synchronization by leveraging continuous positional changes of mobile sources, reducing the required packet count and validating performance through Monte Carlo simulations [4]. As mentioned in [5], based on the modified polar representation (MPR), TDOA localization can unify near-field and far-field processing, decouple angle and range estimation, and achieve near-optimal performance with low computational complexity. [PERSON] et al. examined the impact of base station topology on TDOA and AOA, highlighting TDOA's reliance on high-precision synchronization and AOA's lower base station count requirement, but more complex antenna design [6]. AOA, which measures signal arrival angles, is particularly effective in scenarios with closely spaced base stations. [PERSON] et al. explored the combined use of UTDoA (uplink time difference of arrival) and AoA in 5G networks, along with base station selective exclusion, to enhance performance, especially in non-line-of-sight (NLoS) conditions [7]. [PERSON] et al. proposed a multi-path communication mechanism for UWB indoor positioning, addressing clock asynchrony by utilizing multi-path propagation to improve accuracy while reducing synchronization dependency [8]. These techniques demonstrate distinct strengths in various applications. For example, TDOA and its variants excel in high-precision scenarios, while AOA is more suitable in dense base station deployments with moderate angle measurement requirements. In modern communication networks, RSSI-based, base station-assisted signal ranging has become a critical research area. Numerous studies have been conducted in this domain. For example, [PERSON] et al. constructed a signal strength-to-location database and applied convolutional neural networks (CNNs) with squeeze-and-excitation (SE) blocks to improve localization accuracy [9]. [PERSON] et al. proposed a hybrid localization method, combining RSS and angle of arrival (AOA) measurements to overcome the limitations of RSS-only positioning under heterogeneous anchor conditions [10]. Using a two-stage linear weighted least squares (LWLS) estimator, their approach mitigates measurement noise inconsistencies, enhancing robustness and accuracy. Meanwhile, deep learning has been employed to model complex RSS patterns. [PERSON] et al. demonstrated that architectures such as MLP, CNN, and LSTM can effectively learn spatial and temporal features from WLAN RSS data [11], with LSTM networks excelling in dynamic tracking scenarios. Beyond deep learning, feature transformation and adaptive neighbor selection have improved traditional methods. An RSS-transform-based weighted k-nearest neighbor (Q-WKNN) algorithm [12] was introduced, using cardinality transformations and adaptive parameter selection to better handle signal variability. Finally, hybrid measurement fusion continues to advance.