The present disclosure is to perform beamforming corresponding to the influence of a dynamic environment in which a user moves. The present disclosure relates to a beamforming prediction device that includes: a storage unit that stores a dictionary D obtained by learning fingerprints based on trajectories, and a fingerprint database based on trajectories; a trajectory prediction unit that calculates a trajectory of a mobile terminal, using location information about the mobile terminal; a fingerprint estimation unit that applies the trajectory of the mobile terminal to an input of the dictionary D, and calculates the sparse coefficient X corresponding to the trajectory of the mobile terminal; and a beamforming calculation unit that calculates beamforming of the mobile terminal, using the sparse coefficient X calculated by the fingerprint estimation unit and the fingerprint database.

A system and a method for positioning by a user equipment (UE) may include receiving, by the UE, a radio resource control (RCC) signal comprising a measurement request, receiving, by the UE, at least two positioning reference signals (PRSs), each of the at least two PRSs corresponding to different fractions of an operating frequency range, determining that the at least PRSs are coherently combinable, aggregating, by the UE, the at least two PRSs in one measurement gap, and reporting, by the UE, measurements based on the aggregating the at least two PRSs.

This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.

There is provided a technique of operating a real-time locating system (RTLS) in an indoor environment. The technique comprises: obtaining from a UD a timestamped dataset informative of ID of a tagged object registered by the UD in its proximity, of ID of the UD and of ambiguous sample electromagnetic pattern (SEP) registered by the UD; and defining a location of the tagged object at a point-in-time in accordance with the location of the UD, the point-in-time corresponding to a timestamp of the dataset. The location of the UD is defined using a SEP map informative of associations, at point-in-time, between the SEPs and locations thereof. The SEP map is continuously generated by processing timestamped “UD reports” continuously received from a plurality of UDs, each “UD report” comprising data informative of a respectively registered SEP and a unique identification of a reported UD (IDUD).

An apparatus or an add-on module for a, in particular mobile, device, is disclosed. In an embodiment, the device to be localized or the add-on module uses a measuring unit to measure, via suitable sensors, a local electromagnetic field distribution generated by a given infrastructure. An instantaneous position of the add-on module or of the device equipped therewith is then determined by comparing the measured field distribution with a specified map. In order to facilitate tracking of the add-on module or of the device, the measured field distribution and/or the determined position can be sent via a wireless data connection to a server unit.

An exemplary radio fingerprint-based indoor localization method and system is disclosed that is resistant to spoofing or jamming attacks (e.g., at nearby radios, e.g., access points), among other types of interference. The exemplary method and system may be applied in the configuring of a secured convolutional neural network (S-CNNLOC) or secured deep neural network configured for attack-resistant fingerprint-based indoor localization.

A method of a base station in a mobile communication system is provided, which includes receiving position information of at least one UE; determining an initial position of a UAV based on the position information; transmitting, to the UAV, control information related to the initial position and association information between the at least one UE and the UAV; receiving, from the UAV, first feature information related to a communication state between the at least one UE and the UAV; and transmitting control information related to a movement position of the UAV based on an output of a reinforced learning network to which the first feature information is input.

Systems and methods for WiFi mapping an industrial facility are disclosed. The system comprises a self-driving vehicle having a WiFi transceiver. The self-driving vehicle communicates with a fleet-management using the WiFi transceiver, via a WiFi access point. The self-driving vehicle receives a mission from the fleet-management system, and moves to a destination location based on the mission, using autonomous navigation. While executing the mission, the self-driving vehicle simultaneously measures the received signal strength indication of the WiFi access point and other WiFi access points in the facility, and stores the received signal strength indication in association with the location at which the received signal strength indication was measured.

An edge device includes a first antenna array that includes a first portion and one or more second portions. The edge device includes control circuitry that senses a surrounding area of the edge device by use of the first portion of the first antenna array. The control circuitry executes beamforming to direct a first beam of radio frequency (RF) signal having a signal strength greater than a threshold to a first user equipment (UE), by use of the one or more second portions of the first antenna array.

Disclosed is a beacon-based positioning system. A beacon position in which a beacon is installable is defined in a target space, and a path loss model of radio frequency (RF) signals between all beacon positions and all observation positions of a scanner is determined. Among all possible installation plans for the beacon positions, an installation plan in which different beacon signals, whose RSSIs calculated using the path loss model have significant values, are received in a number greater than or equal to a minimum reference number and a total number of the beacons installed is minimum is determined as an optimal installation plan. The optimization problem of determining the optimal installation plan may be expressed by binary linear programming.