A method includes selecting a first machine learning model from a plurality of machine learning models that are trained for use in performing geolocation, wherein the first machine learning model is selected to perform geolocation within a first cell of a plurality of cells of a wireless network, acquiring event data from a plurality of wireless devices within the first cell, grouping the event data into a plurality of records, wherein each record of the plurality of records contains event data that indicates a common wireless device of the plurality of wireless devices, a common cell of the plurality of cells, and a common timestamp, and generating a predicted location of a first wireless device of the plurality of wireless devices, using the first machine learning model, wherein the first machine learning model outputs the predicted location in response to an input of a record of the plurality of records.

A method includes selecting a first machine learning model from a plurality of machine learning models that are trained for use in performing geolocation, wherein the first machine learning model is selected to perform geolocation within a first cell of a plurality of cells of a wireless network, acquiring event data from a plurality of wireless devices within the first cell, grouping the event data into a plurality of records, wherein each record of the plurality of records contains event data that indicates a common wireless device of the plurality of wireless devices, a common cell of the plurality of cells, and a common timestamp, and generating a predicted location of a first wireless device of the plurality of wireless devices, using the first machine learning model, wherein the first machine learning model outputs the predicted location in response to an input of a record of the plurality of records.

One or more systems and methods for wireless equipment deployment are provided herein. Imagery of locations depicting structures within a list of structures is analyzed to identify features of the structures within the locations. Ranks may be calculated for the structures based upon structure scores and installation scores calculated from the features. In response to a rank for a structure exceeding a threshold, wireless equipment deployment of a communication device may be triggered so that the communication device is controlled to exchange communication signals with devices proximate the structure.

Aspects described herein provide a computer implemented radio frequency propagation simulation tool to allow the simulation of radio frequency propagation across a topographic area which has been very finely mapped in three dimensions to include possible obstructions to high frequency radio waves. A computer implemented RF propagation simulation tool may identify any possible obstructions one edge of which may lie in a simulated RF propagation path between two points, and apply an edge based RF diffraction model (a so-called “knife edge diffraction” model) thereto to simulate the RF propagation around the obstruction. In some aspects, a computer implemented RF propagation simulation tool may identify possible obstructions which in their entirety lie within the width of a simulated RF propagation path, and apply a further diffraction model (a so-called “shield diffraction” model) thereto to simulate the RF propagation around the obstruction. The results of the simulations of RF propagation can be graphically overlaid onto a map or other topographic image for display to a user.

Aspects described herein provide a computer implemented radio frequency propagation simulation tool to allow the simulation of radio frequency propagation across a topographic area which has been very finely mapped in three dimensions to include possible obstructions to high frequency radio waves. A computer implemented RF propagation simulation tool may identify any possible obstructions one edge of which may lie in a simulated RF propagation path between two points, and apply an edge based RF diffraction model (a so-called “knife edge diffraction” model) thereto to simulate the RF propagation around the obstruction. In some aspects, a computer implemented RF propagation simulation tool may identify possible obstructions which in their entirety lie within the width of a simulated RF propagation path, and apply a further diffraction model (a so-called “shield diffraction” model) thereto to simulate the RF propagation around the obstruction. The results of the simulations of RF propagation can be graphically overlaid onto a map or other topographic image for display to a user.

To provide a communication control device capable of appropriately accommodating a surplus interference margin that is not distributed to a communication device depending on a situation. There is provided a communication control device including an acquisition unit that acquires a parameter for calculating a coverage of one or more second wireless systems that share a part or a whole of a frequency allocated to a first wireless system, and a control unit that calculates the coverage of the second wireless system on the basis of the parameter acquired by the acquisition unit and a predetermined reception power reference value, generates information indicating whether or not a partitioned geographical range is included in the coverage, and records a reception power level from the second wireless system in the geographical range in which the information satisfies a predetermined condition.

Described are examples for providing radio access network (RAN) analytics for a virtualized base station. An analytics engine includes a memory storing one or more parameters or instructions for operating the virtualized RAN and at least one processor coupled to the memory. The analytics engine is configured to perform multiple protocol layers of RAN processing for at least one cell at the virtualized base station. The analytics engine is configured to determine a time series of real-time metrics at two or more layers of the multiple protocol layers for the at least one cell or a user equipment (UE) connected to the at least one cell. The analytics engine is configured to correlate a time series for each of the two or more layers to detect a network condition. The analytics engine is configured to modify a configuration of the at least one cell based on the detected network condition.

Described are examples for providing radio access network (RAN) analytics for a virtualized base station. An analytics engine includes a memory storing one or more parameters or instructions for operating the virtualized RAN and at least one processor coupled to the memory. The analytics engine is configured to perform multiple protocol layers of RAN processing for at least one cell at the virtualized base station. The analytics engine is configured to determine a time series of real-time metrics at two or more layers of the multiple protocol layers for the at least one cell or a user equipment (UE) connected to the at least one cell. The analytics engine is configured to correlate a time series for each of the two or more layers to detect a network condition. The analytics engine is configured to modify a configuration of the at least one cell based on the detected network condition.

A method for using legacy Wi-Fi and Wi-Fi Peer-to-Peer (P2P) simultaneously is provided. The method includes entering a device discovery process of Wi-Fi P2P, if use of a Wi-Fi P2P function is requested while using a legacy Wi-Fi function, acquiring a Group Owner (GO) right of Wi-Fi P2P in the device discovery process, performing a listen state over the same channel as a channel where the legacy Wi-Fi function is in use, through the acquisition of the GO right, and performing a search state over a social channel of Wi-Fi P2P, and repeating the listen state and the search state until the device discovery process is ended.

The technologies described herein are generally directed to facilitate allocating resources to zones for IOT equipment in a fifth generation (5G) network or other next generation networks. An example method discussed herein includes identifying, by carrier allocation equipment, carrier transmission information corresponding to transmission of a first carrier signal configured to support Internet of things equipment. The method can further comprise analyzing, by the carrier allocation equipment, the carrier transmission information to determine coverage information corresponding to a potential for coverage, by the first carrier signal, of an Internet of things equipment support zone corresponding to a geographic area. The method can further include, based on the coverage information, facilitating configuring transmission parameter information, representative of a transmission parameter applicable to the coverage of the Internet of things equipment support zone by the first carrier signal.