Abstract: Systems and methods can support improved position locating of wireless devices. A suite of machine learning models may be established. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless signals associated with communications between the user equipment devices and the base station. The suite of machine learning models may be trained using wireless signals received from user equipment devices in known positions. The trained suite of machine learning models can be applied to wireless signals received from user equipment devices in unknown positions. The trained suite of machine learning models may be used to estimate the unknown positions. The estimated positions may be refined using additional information from the user equipment devices.

Abstract: Systems and methods can support improved position locating of wireless devices. A suite of machine learning models may be established. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless signals associated with communications between the user equipment devices and the base station. The suite of machine learning models may be trained using wireless signals received from user equipment devices in known positions. The trained suite of machine learning models can be applied to wireless signals received from user equipment devices in unknown positions. The trained suite of machine learning models may be used to estimate the unknown positions. The estimated positions may be refined using additional information from the user equipment devices.

Abstract: Systems and methods can support determining a physical position of wireless devices. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless downlink signals transmitted from the base station to the user equipment devices. Network identifiers may be extracted from the received wireless downlink signals. User equipment devices may be associated with the extracted network identifiers. Radio channel allocations may be determined for the extracted network identifiers. The radio frequency sensors can receive wireless uplink signals transmitted from the user equipment devices to the base station. Signal strength indicators from the received wireless uplink signals can be associated with the extracted network identifiers. Physical positions of the user equipment devices can be determined by analyzing the associated signal strength indicators.

Abstract: Systems and methods can support identifying wireless devices from radio frequency sensors positioned within an electromagnetic environment where one or more user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless uplink signals transmitted from the user equipment devices to the base station. Data samples can be generated from the received signals. Frequency domain samples can be computed from the data samples. The data samples can be partitioned in time and frequency to generate spectrum tiles. The spectrum tiles can be statistically aggregated in a frequency domain to reduce the quantity of data samples. A clustering algorithm may be applied to the statistically aggregated spectrum tiles. Unique instances of the user equipment devices may be identified from the clustered spectrum tiles to enumerate the instances of the user equipment devices.

Abstract: Systems and methods can support determining the physical position of a wireless device. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless uplink signals transmitted from the user equipment devices to the base station. Data samples can be generated from the received signals. The data samples may be partitioned in time and frequency to generate spectrum tiles. The spectrum tiles may be statistically aggregated to reduce the quantity of data samples. A clustering algorithm may be applied to the statistically aggregated spectrum tiles to determine clusters of spectrum tiles. Signal parameters associated with respective clusters of spectrum tiles may be computed. Physical locations associated with user equipment devices can be estimated from the signal parameters associated with the respective clusters of spectrum tiles.