Systems, methods, and apparatus are provided for automated identification of open space in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the open space based upon the measured and analyzed data in near real-time.

Techniques for calculating a location of a position consumer device is disclosed. In one example, a network server may create a fingerprint map from reference data points. Each of the reference data points may include a recorded geo-location of a position source device and signal measurements taken at that recorded geo-location. By initially estimating an initial position of the position consumer device, the network server may apply one or more threshold values to filter reference data pointscandidates for interpolation. The network server may then perform an interpolation on one or more pairs of reference data points to find a pair of reference data points that is collinear with the estimated position of the position consumer device. The location of the position consumer device may then be calculated based upon geo-locations of position source devices that are associated with collinear reference data points.

Embodiments of the present disclosure relate to fingerprint positioning in a wireless communication network. A method for establishing a positioning model comprises: obtaining, at a first device, a set of training data comprising historical measurements and historical regions, a historical measurement being associated with a second device, the second device being within one of the historical regions when the historical measurement is obtained; and training the positioning model by using the historical measurements as input of the positioning model and the historical regions as output of the positioning model to obtain parameter sets of Gaussian distributions of the positioning model, the Gaussian distributions indicating probabilities that a third device is positioned in the historical regions, and the parameter sets corresponding to respective non-light of sight levels of the historical regions. In this way, the positioning accuracy of fingerprint positioning method can be greatly improved.

Systems and methods are described for automated detection of border conflicts in physical radiofrequency (RF) communication network infrastructures. For example, proposed sector antennas in a greenfield physical network deployment may be licensed to radiate in certain spectrum blocks in the mapped licensed geographic regions (mLGR) where they are located, but the licenses may not permit radiated power in those spectrum blocks to cross into adjacent mLGRs. Embodiments compute radiation contours for the sector antennas indicating estimated local power levels computed based on antenna characteristics of the sector antennas and propagation model data that defines geographic morphologies for the mLGRs. The radiation contours are analyzed to detect any border conflict conditions where the estimated local power levels exceed defined threshold radiation levels in unlicensed regions. A culprit set of the sector antennas can be output to indicate those responsible for detected border conflict conditions.

An edge device includes a first antenna array and a control circuitry that senses a surrounding of the edge device to recognize a user equipment (UE) in motion as valid to receive services from the edge device. A position of the UE in motion from the edge device is tracked to execute beamforming for directing a first beam of radio frequency (RF) signal having a signal strength greater than a threshold to the UE in motion. A first portion of the first antenna array is used to sense the surrounding area and one or more second portions of the first antenna array is used for the beamforming to direct the first beam of RF signal in a defined radiation pattern. Further, the defined radiation pattern of the first beam of RF signal is dynamically updated based on a change in a position of the UE in motion.

A positioning device includes a processor configured to calculate a first neighboring time at which the mobile device becomes closest to a first base station in the plurality of base stations based on the received signal strength data and a second neighboring time at which the mobile device becomes closest to a second base station of the plurality of base stations, and convert the relative movement data in the second storage device into position coordinates data specifying an absolute position of the mobile device in an absolute coordinate system using the first base station as a reference point from the first neighboring time to the second neighboring time.

Each of a plurality of transmitters, which are distributed at fixed locations of a site, regularly transmits radio signals. A mesh node performs measurements on radio signals transmitted by at least one transmitter and transmits messages including results of the measurements. The mesh node belongs to a plurality of mesh nodes, each configured to monitor at least one environmental parameter at the site. A gateway node receives messages transmitted by the mesh node directly and/or via at least one other mesh node of the plurality of mesh nodes, wherein each of the plurality of mesh nodes is configured to receive messages from other mesh nodes of the plurality of mesh nodes and to forward received messages. The gateway node transmits received messages to a server that is configured to monitor mesh nodes at the site based on results of measurements.

Device-free localization for smart indoor environments within an indoor area covered by wireless networks is detected using active off-the-shelf-devices would be beneficial in a wide range of applications. By exploiting existing wireless communication signals and machine learning techniques in order to automatically detect entrance into the area, and track the location of a moving subject within the sensing area a low cost robust long-term tracking system can be established. A machine learning component is established to minimize the need for user annotation and overcome temporal instabilities via a semi-supervised framework. After establishing a robust base learner mapping wireless signals to different physical locations from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncertainty level rises significantly. Additionally, an automatic change-point detection process is employed setting a query for updating the outdated model and the decision boundaries.

Methods, systems, and products determine electromagnetic reflective characteristics of ambient environments. A wireless communications device sends a cellular impulse and receives reflections of the cellular impulse. The cellular impulse and the reflections of the cellular impulse may be compared to determine the electromagnetic reflective characteristics of an ambient environment.

A sensing system including multiple sensor node devices and an analysis device, wherein: each of the multiple sensor node devices has a sensor that measures a measurement target and acquires data values, a learning unit that, based on the data values, learns a model used to estimate the data values at an installation position of the sensor, and a communication unit that transmits learning result data indicating a learning result from the learning unit; and the analysis device has a spatial analysis unit that estimates a spatial distribution of the data values based on the learning result data from each of the multiple sensor node devices and the installation positions of the respective sensor node devices.