A method and apparatus for efficient deployment of nodes in a network includes obtaining first data that indicates first locations of nodes in a network and terrain data that indicates height of terrain at terrain locations. The method further includes determining an exploration region for a first node and dividing the exploration region into subregions. The method further includes determining a proxy location for each subregion that is a location corresponding to a characteristic of the terrain data in the subregion. The method further includes determining a value of a parameter that indicates a contribution of the first node at each proxy location to network fitness. The method further includes assigning a second location to the first node based on the determined parameter value at each proxy location. The method further includes relocating the first node from the first location to the second location.

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.

Implementations described and claimed herein provide systems and methods for intelligent node type selection in a telecommunications network. In one implementation, a customer set is obtained for a communications node in the telecommunications network. The customer set includes an existing customer set and a new customer set. A set of customer events is generated for a node type of the communications node using a simulator. The set of customer events is generated by simulating the customer set over time through a discrete event simulation. An impact of the customer events is modeled for the node type of the communications node. The node type is identified from a plurality of node types for a telecommunications build based on the impact of the customer events for the node type.

Implementations described and claimed herein provide systems and methods for intelligent node type selection in a telecommunications network. In one implementation, a customer set is obtained for a communications node in the telecommunications network. The customer set includes an existing customer set and a new customer set. A set of customer events is generated for a node type of the communications node using a simulator. The set of customer events is generated by simulating the customer set over time through a discrete event simulation. An impact of the customer events is modeled for the node type of the communications node. The node type is identified from a plurality of node types for a telecommunications build based on the impact of the customer events for the node type.

A system configured to track network slicing operations within a 5G communication network includes processing circuitry configured to determine a network slice instance (NSI) associated with a QoS flow of a UE. The NSI communicates data for a network function virtualization (NFV) instance of a Multi-Access Edge Computing (MEC) system within the 5G communication network. Latency information for a plurality of communication links used by the NSI is retrieved. The plurality of communication links includes a first set of non-MEC communication links associated with a radio access network (RAN) of the 5G communication network and a second set of MEC communication links associated with the MEC system. A slice configuration policy is generated based on the retrieved latency information and slice-specific attributes of the NSI. Network resources of the 5G communication network used by the NSI are reconfigured based on the generated slice configuration policy.

A respective preferred installation height above ground level is determined for each of a plurality of locations for a subscriber module for receiving a radio link from an access point in a wireless network, the access point having a given height above ground level and a specified location, and the subscriber module being situated within a given geographical area including the location of the access point. The method comprises accessing elevation data for the given geographical area, processing the elevation data to generate a preferred height data file representing a preferred height for a subscriber module to be wirelessly visible by the access point at each of the plurality of locations and processing the required height data file to provide output data indicating the preferred height of the subscriber module as a function of location.

A respective preferred installation height above ground level is determined for each of a plurality of locations for a subscriber module for receiving a radio link from an access point in a wireless network, the access point having a given height above ground level and a specified location, and the subscriber module being situated within a given geographical area including the location of the access point. The method comprises accessing elevation data for the given geographical area, processing the elevation data to generate a preferred height data file representing a preferred height for a subscriber module to be wirelessly visible by the access point at each of the plurality of locations and processing the required height data file to provide output data indicating the preferred height of the subscriber module as a function of location.

This document discloses a solution for estimating network traffic capacity demand in an area of interest. According to an aspect, a computer-implemented method comprises: forming, by using one or more social media applications, a social media layer storing records of a plurality of places in the area of interest; forming, by using at least one source storing real geolocations of the places, a geolocation layer mapping the places to geolocations; classifying the places into a plurality of classes and assigning to each place a weight indicative of a traffic capacity demand dependent on a class of said place; building a capacity layer for the area of interest on the basis of the real geolocations of the places provided by the geolocation layer and the traffic capacity demand per place indicated by the weights, the capacity layer indicating spatial distribution of network traffic capacity demand in a plurality of sub-areas of the area of interest, the plurality of sub-areas comprising sub-areas having at least one of the places and sub-areas between the places

This document discloses a solution for estimating network traffic capacity demand in an area of interest. According to an aspect, a computer-implemented method comprises: forming, by using one or more social media applications, a social media layer storing records of a plurality of places in the area of interest; forming, by using at least one source storing real geolocations of the places, a geolocation layer mapping the places to geolocations; classifying the places into a plurality of classes and assigning to each place a weight indicative of a traffic capacity demand dependent on a class of said place; building a capacity layer for the area of interest on the basis of the real geolocations of the places provided by the geolocation layer and the traffic capacity demand per place indicated by the weights, the capacity layer indicating spatial distribution of network traffic capacity demand in a plurality of sub-areas of the area of interest, the plurality of sub-areas comprising sub-areas having at least one of the places and sub-areas between the places