A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

According to one embodiment, an information collection system comprises a transmitter, a receiver, and a processor. The transmitter emits a signal. The receiver receives the signal. The processor calculates a distance between the transmitter and the receiver from a strength of the signal received by the receiver. The processor calculating the distance between the transmitter and the receiver from the strength of the signal for each of the signals received during a first interval, and using an average distance as the distance between the transmitter and the receiver, the average distance being obtained by averaging the plurality of calculated distances.

Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

Disclosed are techniques for training a position estimation module. In an aspect, a first network entity obtains a plurality of positioning measurements, obtains a plurality of positions of one or more user equipments (UEs), the plurality of positions determined based on the plurality of positioning measurements, stores the plurality of positioning measurements as a plurality of features and the plurality of positions as a plurality of labels corresponding to the plurality of features, and trains the position estimation module with the plurality of features and the plurality of labels to determine a position of a UE from positioning measurements taken by the UE.

A networked radio frequency identification system includes a plurality of radio frequency identification (RFID) tag readers, a computer in signal communication with the RFID tag readers over a network, and a software module for storage on and operable by the computer that localizes RFID tags based on information received from the RFID tag readers using a network model having endpoints and oriented links. In an additional example, at least one of the RFID tag readers includes an adjustable configuration setting selected from RF signal strength, antenna gain, antenna polarization, and antenna orientation. In a further aspect, the system localizes RFID tags based on hierarchical threshold limit calculations. In an additional aspect, the system controls a locking device associated with an access point based on localization of an authorized RFID tag at the access point and reception of additional authorizing information from an input device.

The present disclosure provides radio-frequency (RF) systems that can detect the presence of RF signals received by the system, as well as determine characteristics such as the operating frequency of RF signals, the type of RF source that transmitted each RF signal, and/or the location of each RF source with high precision and sensitivity while using low cost, scalable electronics that are versatile enough for deployment in a variety of environments. Such systems can employ a network of RF sensors that can coordinate in response to communication with a computer to perform any such detection and/or determination using trained models executed onboard the RF sensors and/or the computer. RF signals may have unique characteristics when received at one or more RF sensors that may be detected using trained models described herein, even in high noise or non-line of sight (LOS) environments and with low cost, low resolution RF receiver hardware.

Disclosed are techniques for wireless positioning. In an aspect, a first user equipment (UE) obtains one or more first radio frequency fingerprint (RFFP) measurements of one or more first downlink channels received at the first UE, one or more first sidelink channels received at the first UE, or both, and determines one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

Disclosed are techniques for positioning. In an aspect, a network entity receives, from at least one network node, a measurement report including one or more radio frequency fingerprint (RFFP) measurements, wherein the one or more RFFP measurements include at least one RFFP measurement of at least one sidelink channel between a first user equipment (UE) and a second UE, determines one or more locations of a target UE based on the one or more RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

A method of monitoring a kinematic state of an unmanned aerial vehicle (UAV) is provided. The method comprises obtaining one or more predicted pathlosses between a UAV and one or more base stations at a first time instance, wherein the predicted pathlosses are determined using an estimate of a kinematic state of the UAV at the first time instance and one or more pathloss models developed using a machine-learning process. The method further comprises obtaining one or more measurements of a pathloss between each of the one or more base stations and the UAV at the first time instance, and re-determining the estimate of the kinematic state of the UAV at the first time instance based on the one or more predicted pathlosses and the one or more measurements of the pathloss.

It is provided a method for determining when a portable key device is located on a front side or on a back side in relation to a barrier secured by an electronic lock. The method is performed in a location determiner and comprises the steps of: obtaining a channel impulse response, CIR, based on an impulse signal transmitted from the portable key device, the CIR being based on a plurality of samples of the impulse signal as received by an antenna being fixedly mounted in relation to the electronic lock; and determining, based on the CIR, whether the portable key device is located on the front side or on the back side.