A method for evaluating a buildings wireless performance, the method comprising: constructing an environment model, the environment model comprising a layout of the building, a power density of transmitters P.sub.T[W/m.sup.2], and a considered frequency band f.sub.c; computing a signal to interference plus noise ratio (SINR) for the building in dependence on the environment model; computing a SINR for an open space in dependence on the environment model, the computing size for the open space being the same as the computing size of the building, and wherein the open space is an ideal space in absence of the building or other surroundings; comparing the SINR for the building with the SINR for the open space; and in dependence on the comparison, evaluating the building wireless performance by calculating an interference power gain and an intended power gain for the building.

Systems and methods for localizing individuals in a region using wireless signals in accordance with embodiments are illustrated. One embodiment includes a method for localizing individuals in a region between wireless devices of a system. The method receives wireless signal strength data for signals transmitted along signal paths between several wireless playback devices transmitting on a wireless channel during synchronous playback of media content by the several wireless playback devices and determines a first signal strength for each of several portions of the wireless channel. The method calculates, for each signal path between each of the several wireless playback devices, a difference in the determined first signal strength from a second signal strength for each of the several subcarriers, and determines, based on the calculated differences, a state for a set of one or more individuals in the region.

A service qualification platform may receive, from a user device, a service request to qualify a unit of a multi-unit building to receive a service. The service qualification platform may obtain a service coverage mapping associated with the multi-unit building and may provide, to the user device and via a user interface that is associated with a geographic information system, a unit location request for an indication of a location of the unit within the multi-unit building. The service qualification platform may receive unit location information that is associated with the indication and may determine a service qualification metric based on the service coverage mapping and the unit location information, wherein the service qualification metric is associated with a capability of receiving the service within the unit. The service qualification platform may perform an action associated with the service qualification metric.

Systems and methods to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment. As a result, a telecommunications service provider is able to efficiently and economically identify targeted solutions and locations to expand capacity of cell sites and improve customer experiences.

Provided are a deep learning-based beamforming communication system and method, wherein in an indoor environment using millimeter wave communication, in response to reference signals transmitted from a base station to at least one user terminal, reference signal received power and location information for each user terminal location are received from each user terminal and a fingerprint DB is constructed, and from the constructed fingerprint data, a user model is constructed on the basis of reference signal received power for each user terminal location and a blockage model is constructed on the basis of reference signal received power according to each blockage located between the base station and the user terminal. Location information and data traffic are received from the at least one user terminal, a beam index of the user terminal corresponding to the received data traffic is derived from a deep neural network, and a communication channel between the base station and a user is formed with the derived beam index, whereby reliability and a data transfer rate are improved in an indoor communication environment.

Examples disclosed herein relate to a sensor fusion scanning system for wireless network planning. The system includes a sensor scanning mobile platform comprising a beam steering radar sensor and one or more auxiliary sensors, the sensor scanning mobile platform configured to scan a wireless environment, a reflectivity engine configured to generate a reflectivity representation of the wireless environment based on radar data from the beam steering radar sensor, a sensor fusion processing engine configured to generate a Three-Dimensional (“3D”) representation of the wireless environment based on the radar data and sensor data from the one or more auxiliary sensors, and a reflectarray planning engine configured to design a plurality of reflectarrays and determine locations for the plurality of reflectarrays in the wireless environment based on the reflectivity representation and the 3D representation.

The present technology is directed to providing a 3-D visualization of a Wi-Fi signal propagation pattern based on telemetry data. The present technology can receive telemetry data for a Wi-Fi access point located at a location of a building plan in a Wi-Fi visualization system, store the telemetry data with a timestamp, determine a change in a Wi-Fi coverage for the Wi-Fi access point based on the telemetry data, and present a visualization illustrating the change in the Wi-Fi coverage for the Wi-Fi access point. The present technology can further present an animation of the change in the Wi-Fi coverage for the Wi-Fi access point based on the stored telemetry data.

Methods of auto-calibrating antennas in a target environment are provided including obtaining a map and associated scale of the target environment; displaying the map to a user on a user display device; discovering a plurality of antennas present in the target environment; illustrating the discovered plurality of antennas on the map; repositioning at least some of the plurality antennas on the map; locking selected ones of plurality of antennas into a current position to provide a plurality of locked antennas, wherein the plurality of locked antennas is equal to zero or greater; and auto-calibrating a remaining plurality of unlocked antennas, wherein auto-calibrating includes moving each of the remaining plurality of unlocked antennas from a first position to a second position.

The disclosure provides a method for estimating channel quality in a wireless network. The method comprises: obtaining information relating to a channel quality for a first time period, as measured by a wireless node located in an environment comprising one or more machines having mechanical parts which are movable in a periodic pattern; and providing the information as an input to a predictive model, developed using a machine-learning algorithm, to obtain a predicted channel quality in the environment for a second, subsequent time period.

Methods of auto-calibrating antennas in a target environment are provided including obtaining a map and associated scale of the target environment; displaying the map to a user on a user display device; discovering a plurality of antennas present in the target environment; illustrating the discovered plurality of antennas on the map; repositioning at least some of the plurality antennas on the map; locking selected ones of plurality of antennas into a current position to provide a plurality of locked antennas, wherein the plurality of locked antennas is equal to zero or greater; and auto-calibrating a remaining plurality of unlocked antennas, wherein auto-calibrating includes moving each of the remaining plurality of unlocked antennas from a first position to a second position.