Methods, apparatus and systems for correlation-based wireless monitoring are described. For example, a described method comprises: detecting and monitoring motion of a first object in a first sensing task based on a first motion information (MI) computed based on a first time series of channel information (TSCI) associated with a first device pair; detecting and monitoring motion of a second object in a second sensing task based on a second MI computed based on a second TSCI associated with a second device pair; computing a correlation score based at least partially on: the first TSCI, the second TSCI, the first MI and the second MI; detecting the first object and the second object as a same object when the correlation score is greater than a first threshold; and detecting the first object and the second object as two different objects when the correlation score is less than a second threshold.

Disclosed are techniques for wireless sensing. In an aspect, a user equipment (UE) receives, from a network entity, volume-related information associated with at least one target object, the volume-related information including an estimated position of the at least one target object, an estimated size of the at least one target object, an estimated volume of the at least one target object, an estimated orientation of the at least one target object, an estimated shape of the at least one target object, or any combination thereof, and performs a sensing procedure with the network entity to determine one or more characteristics of the at least one target object based on at least the volume-related information.

Methods and apparatus for predicting wireless measurements based on virtual access points is described. In some embodiments, a location of a user equipment (UE) may be obtained, and given the location of the UE, an output may be generated using a machine learning model, the output including one or more predicted wireless measurements. The output may be indicative of a wireless channel in a multipath environment. In some variants, the machine learning model may have been trained by obtaining a training dataset comprising multipath components data and ground truth locations of a wireless device, and performing an optimization with respect to the multipath components data and the ground truth locations. In some implementations, a training output comprising a predicted multipath component may be produced during the training, and the optimization may include an iterative minimization of an error between at least the predicted multipath component and a labeled multipath component.

Embodiments provide an unmanned aerial vehicle comprising a receiver and a position determiner. The receiver is configured to receive two periodic wideband signals transmitted from two spaced apart base stations of a navigation system for unmanned aerial vehicles, wherein the two periodic wideband signals are time-synchronized. The position determiner is configured to determine a position of the unmanned aerial vehicle relative to the two base stations based on a difference between reception times of the two periodic wideband signals and based on reception intensities of the two periodic wideband signals.

Methods, systems, and devices for wireless communications are described. A position of a user equipment (UE) may be determined when communications between the UE and a base station are routed through a relay node. For example, the UE 115 may determine whether communications are received from the base station or the relay node based on positioning assistance data that contains positioning-related information about different base stations and relay nodes in the system. The UE may then transmit a position metric based on this determination, where a location server uses this position metric for determining the location of the UE. Additionally or alternatively, the location server or base station may use the positioning assistance data to determine whether an uplink transmission is received directly from the UE or via the relay node and may generate a position metric that the location server uses for determining the location of the UE.

Systems, methods, apparatuses, and computer program products for positioning measurements are provided. One method may include receiving, from a user equipment, a number of supported receive and transmit panels that can be simultaneously activated and/or a panel switch time. The method may then include computing or otherwise determining a UE-based latency lag using the number of supported receiver and transmitter panels that can be simultaneously activated and the panel switch time.

Wireless communication between two electronic devices may be used to determine a distance between the two devices, even in the presence of an otherwise-disruptive attacker. A wireless receiver system of one device may receive a true wireless ranging signal from a first transmitting device and a false wireless ranging signal from an attacker. The wireless receiver system may correlate the wireless signals with a known preamble sequence and perform channel estimation using the result, obtaining a channel impulse response for the wireless signals. The wireless receiver system may filter the channel impulse response for the plurality of wireless signals by removing at least part of the channel impulse response due to the false wireless ranging signal while not removing at least part of the channel impulse response due to the true wireless ranging signal. The receiver system may perform a wireless ranging operation using the filtered channel impulse response.

In one aspect, a method of determining a location of a user within an indoor space, includes emitting a radiofrequency signal into the indoor space, receiving backscattered training radiofrequency signals, including multipath, for at least one location within the indoor space, converting the received training signals into a point cloud for each location of the at least one location, assigning a signature for each location based on the point cloud for each location, receiving additional radiofrequency signals, including multipath, converting the additional radiofrequency signals into an additional point cloud, and determining a location of the user by comparing the additional point cloud to the assigned signatures.

The present invention relates to a method for estimating a distance between nodes of an LPWA network. First and second nodes, respectively, successively transmit a narrow band signal at a plurality of carrier frequencies to each other. The second and first nodes, respectively, receive the transmitted signals and demodulate them into baseband signals. Complex values representative of a forward-backward function of the transmission channel between two nodes are obtained from the baseband demodulated signals. These complex values are then provided to a previously supervisingly trained neural network, the neural network giving an estimation of the distance separating the first node and the second node. The present invention also relates to a method for estimating the position of a node in an LPWA network using the above-noted distance estimation method.

A positioning system according to an aspect of the present invention includes a wireless base station device that communicates with a wireless terminal; one or more relay devices that relay a signal between the wireless terminal and the wireless base station device; and a positioning device that estimates a position of the wireless terminal on the basis of a first signal received by the wireless base station device from the wireless terminal via a first relay device, which is one of the one or more relay devices, a position of the one or more relay devices, and a position of the wireless base station device.