A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Examples relate to access point signal estimation. In one example, a computing device may: receive a first pathloss value in a first frequency, the first pathloss value indicating a difference in transmit power of a particular access point and a received signal strength observed by a first access point; receive a second pathloss value in the first frequency, the second pathloss value indicating a difference in transmit power of the particular access point and a received signal strength observed by a second access point; receive a third pathloss value in a second frequency, the third pathloss value indicating a difference in transmit power of the particular access point and a received signal strength observed by the second access point in the second frequency; and generate, using the first, second, and third pathloss values, an estimated pathloss between the first access point and the particular access point in the second frequency.

The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., tessellate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

A Machine-to-machine (M2M) terminal (11) is configured to receive a first notification from a base station (13) and to transmit a second notification to the base station (13) when establishing a radio connection with the base station (13) after reception of the first notification or while performing a procedure for establishing a bearer between the M2M terminal (11) and a core network (14) after reception of the first notification. The first notification indicates whether specific coverage enhancement processing is supported in a cell (130) of the base station (13) in which the M2M terminal (11) is located. The second notification indicates that the specific coverage enhancement processing is required or being executed by the M2M terminal (11). It is thus possible to provide an improvement to allow the M2M terminal to determine necessity of special coverage enhancement processing for M2M terminals.

A Machine-to-machine (M2M) terminal (11) is configured to receive a first notification from a base station (13) and to transmit a second notification to the base station (13) when establishing a radio connection with the base station (13) after reception of the first notification or while performing a procedure for establishing a bearer between the M2M terminal (11) and a core network (14) after reception of the first notification. The first notification indicates whether specific coverage enhancement processing is supported in a cell (130) of the base station (13) in which the M2M terminal (11) is located. The second notification indicates that the specific coverage enhancement processing is required or being executed by the M2M terminal (11). It is thus possible to provide an improvement to allow the M2M terminal to determine necessity of special coverage enhancement processing for M2M terminals.

A computer implemented method of cell overlap analysis of a communication network. The method includes determining coverage area of a first and a second cell of the communication network, wherein determination of the coverage area of a cell is based on user distribution in the respective cell; determining intersecting area as an area where the determined coverage area of the first cell and the determined coverage area of the second cell overlap; and determining a first impact value reflecting impact of the overlap on the first cell as a ratio of the determined intersecting area and the determined coverage area of the first cell.

An intelligent wireless broadband network and content delivery management within a network includes at least one datacenter, at least one network tower and a plurality of smart nodes may be provided. Each of the plurality of smart nodes may be deployed as a micro point of presence (micro POP) at the at least one datacenter the at least one tower and at each of a plurality of hub-homes within the network. An artificial intelligence (AI) capable compute unit may be configured to provide customization of the plurality of smart nodes based on usage pattern of the plurality of homes at a neighborhood level, and thereby facilitating a dynamic edge network distribution solution for better Internet experience to the end-users.

A method for detecting coverage problems is provided. The method includes receiving, at data processing hardware, from at least one user equipment (UE), observations. Each observation includes a signal measurement of a signal emitted from a base station and a corresponding location of the signal measurement. The method also includes generating, by the data processing hardware, a coverage map for the base station based on the received observations, the coverage map indicating a signal characteristic of the emitted signal about the base station. The method further includes determining, by the data processing hardware, an estimated characteristic of the base station by feeding the coverage map into a neural network configured to output the estimated characteristic of the base station.

A method for detecting coverage problems is provided. The method includes receiving, at data processing hardware, from at least one user equipment (UE), observations. Each observation includes a signal measurement of a signal emitted from a base station and a corresponding location of the signal measurement. The method also includes generating, by the data processing hardware, a coverage map for the base station based on the received observations, the coverage map indicating a signal characteristic of the emitted signal about the base station. The method further includes determining, by the data processing hardware, an estimated characteristic of the base station by feeding the coverage map into a neural network configured to output the estimated characteristic of the base station.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.