Abstract: Aspects of the subject disclosure may include, for example, network deployment or radio-propagation computation based on a combination of photon mapping and machine learning including supporting near-real-time computation of the radio transmissions for different layouts of antennas and allowing examination of a large variety of antenna locations and layouts, changing configuration details, e.g., tilting antennas or optimally selecting the sector that each antenna covers, and so on. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying geographic clusters that are similar based on metrics and geo-spatial association. Embodiments of the disclosure are directed to operations that include obtaining data corresponding to a communication network, applying a first algorithm to the data to generate a plurality of bins, generating a respective score for each bin of the plurality of bins, applying a second algorithm, based on the respective scores, to generate a plurality of clusters, and generating a graph for each cluster of the plurality of clusters, wherein vertices of each graph are represented by the bins of the cluster, and wherein edges of each graph connect the bins of the cluster to adjacent bins of the cluster. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining user input identifying a first user-identified network feature of a training image of a geographical region. The training image and the user-identified feature are provided to a neural network adapted to train itself according to the user-identified features to obtain a first trained result that classifies objects within the image according to the user-identified feature. The training image and the first trained result are displayed, and user-initiated feedback is obtained to determine whether a training requirement has been satisfied. If not satisfied, the user-initiated feedback is provided to the neural network, which retrains itself according to the feedback to obtain a second trained result that identifies an updated machine-recognized feature of the training image. The process is repeated until a training requirement has been satisfied, after which a map is annotated according to the machine-recognized feature.

Abstract: Aspects of the subject disclosure may include, for example, calculating a Fresnel zone about a line of sight (LoS) between a pair of antennas positioned on antenna mounts. The Fresnel zone is projected onto a geospatial grid of 3D cells, each having a height above a horizontal plane. Cells that intersect the Fresnel zone projection are selected to obtain a subset cells with constraint heights determined according to heights of the subset cells that are adjusted according to the Fresnel zone. The LoS is revised according to an algorithm to obtain a set of updated LoS. Those updated LoS that do not intersect the set of adjusted constraint heights are identified as solutions that include an optimal solution, should one exist. Other embodiments are disclosed.

Abstract: Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

Abstract: Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. A security management component (SMC) can determine a neural network (NN) of NNs that can be representative of the cell network based on analysis of first signal measurement data associated with the cells. The NN can cluster respective cells into respective clusters based on analysis of second signal measurement data associated with the cells. The NN can determine whether an abnormal signaling condition associated with a cell is occurring based on analysis of third signal measurement data associated with the cells, information relating to the cluster to which the cell is assigned, and a defined network security criterion. SMC can perform feature reforming on the first, second, and/or third signal measurement data to reduce dimensionality of such data to facilitate processing by the NN.

Abstract: A device includes a processor and a memory. The processor effectuates operations including monitoring enterprise network traffic associated with one or more user equipment (UE). The processor further effectuates operations including comparing the enterprise network traffic to a UE profile associated with each of the one or more UE. The processor further effectuates operations including determining whether the comparison indicates that a predetermined threshold has been exceeded. The processor further effectuates operations including in response to the indication that the predetermined threshold has been exceeded, generating an alert, wherein exceeding the predetermined threshold is indicative of a denial of service attack on an enterprise network or an attempt to remove enterprise data via the one or more UE.

Abstract: A processing system may obtain usage volume information for endpoint devices for at least one cell site of a cellular network, determine at least one earning value of the at least one cell site based upon a summation of an earning metric of each of the endpoint devices for the at least one cell site, the earning metric comprising for each of the endpoint devices in a given time period: a total earning for the cellular network from the endpoint device times a ratio of the usage volume via the at least one cell site divided by the total usage volume via the cellular network, train a prediction model to predict an earning value of a new cell site, based upon geospatial features of the at least one cell site as predictor factors, and determine a predicted earning value of the new cell site via the prediction model.

Abstract: Aspects of the subject disclosure may include, for example, obtaining user input identifying a first user-identified network feature of a training image of a geographical region. The training image and the user-identified feature are provided to a neural network adapted to train itself according to the user-identified features to obtain a first trained result that classifies objects within the image according to the user-identified feature. The training image and the first trained result are displayed, and user-initiated feedback is obtained to determine whether a training requirement has been satisfied. If not satisfied, the user-initiated feedback is provided to the neural network, which retrains itself according to the feedback to obtain a second trained result that identifies an updated machine-recognized feature of the training image. The process is repeated until a training requirement has been satisfied, after which a map is annotated according to the machine-recognized feature.

Abstract: A device includes a processor and a memory. The processor effectuates operations including monitoring enterprise network traffic associated with one or more user equipment (UE). The processor further effectuates operations including comparing the enterprise network traffic to a UE profile associated with each of the one or more UE. The processor further effectuates operations including determining whether the comparison indicates that a predetermined threshold has been exceeded. The processor further effectuates operations including in response to the indication that the predetermined threshold has been exceeded, generating an alert, wherein exceeding the predetermined threshold is indicative of a denial of service attack on an enterprise network or an attempt to remove enterprise data via the one or more UE.

Abstract: Architectures and techniques are presented that improve or enhance a network planning procedure such as by selecting a more efficient test buffer that is used to identify objects that might intersect a Fresnel zone between two transceivers. An improved test buffer (e.g., buffer region) can be one that is constructed from a plurality of rectangles situated along a line of sight of the two transceivers and that are oriented according to cardinal directions.

Abstract: A processing system may obtain a map comprising a plurality of geographical objects, identify a first label of a first geographical object that identifies a first geographical object type, identify a second label of a second geographical, identify a geospatial relationship between the first and second geographical objects, and apply an input data set comprising geospatial relationship information of the first geographical object to a geospatial relationship model to obtain an output comprising a confidence factor of the first label of the first geographical object. The geospatial relationship model may be associated with the first geographical object type and is to output the confidence factor based upon the input data set. The input data set may include the geospatial relationship between the first and second geographical objects. The processing system may then apply at least one modification to the map based upon the confidence factor.

Abstract: A processing system may obtain usage volume information for endpoint devices for at least one cell site of a cellular network, determine at least one earning value of the at least one cell site based upon a summation of an earning metric of each of the endpoint devices for the at least one cell site, the earning metric comprising for each of the endpoint devices in a given time period: a total earning for the cellular network from the endpoint device times a ratio of the usage volume via the at least one cell site divided by the total usage volume via the cellular network, train a prediction model to predict an earning value of a new cell site, based upon geospatial features of the at least one cell site as predictor factors, and determine a predicted earning value of the new cell site via the prediction model.

Abstract: Aspects of the subject disclosure may include, for example, collecting and storing data about a communication environment including one or more communication networks wherein respective communication networks of the one or more communication networks employ respective communication technologies for communication with remote user devices in the communication environment, providing traffic forecasts of communication traffic, creating importance layers for potential build locations for equipment of the one or more communication networks, identifying desirable build locations among the potential build locations of the one or more communication networks based on the traffic forecasts of communication traffic and the importance layers, receiving user input defining planning parameters for the communication environment and determining respective target goal values for respective build options for the one or more communication networks based on the planning parameters, the identified desirable build locations and the

Abstract: Aspects of the subject disclosure may include, for example, specifying changes to a three-dimensional model of the environment to simulate changes in the environment over time and identifying impacts on a communication system. Simulated changes may include increased building density, increased building heights, changes in vegetation, or the like. Other embodiments are disclosed.

Abstract: Aspects of the disclosure include determining that a first instance of a first object is present in a first image in accordance with an execution of a first image processing algorithm, generating a first bounding region that at least partially surrounds the first instance of the first object in the first image, determining that the first instance of the first object in the first image has a first attribute in accordance with an execution of a second image processing algorithm, wherein the second image processing algorithm is operative on the first image in accordance with the first bounding region, and selecting the first instance of the first object and/or a second instance of the first object to receive a deployment of a network resource in accordance with the determining that the first instance of the first object in the first image has the first attribute. Other aspects are disclosed.

Abstract: Architectures and techniques are presented that improve or enhance a network planning procedure such as by selecting a more efficient test buffer that is used to identify objects that might intersect a Fresnel zone between two transceivers. An improved test buffer (e.g., buffer region) can be one that is constructed from a plurality of rectangles situated along a line of sight of the two transceivers and that are oriented according to cardinal directions.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first object included in at least one image in accordance with an execution of an image processing algorithm, analyzing a plurality of parameters in accordance with at least one model responsive to the identifying of the first object included in the at least one image, wherein each parameter of the plurality of parameters is associated with the first object or a second object, selecting one of the first object or the second object for receiving at least one communication network resource responsive to the analyzing of the plurality of parameters, wherein the selecting results in a selected object, and presenting the selected object on a presentation device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining user input identifying a first user-identified network feature of a training image of a geographical region. The training image and the user-identified feature are provided to a neural network adapted to train itself according to the user-identified features to obtain a first trained result that classifies objects within the image according to the user-identified feature. The training image and the first trained result are displayed, and user-initiated feedback is obtained to determine whether a training requirement has been satisfied. If not satisfied, the user-initiated feedback is provided to the neural network, which retrains itself according to the feedback to obtain a second trained result that identifies an updated machine-recognized feature of the training image. The process is repeated until a training requirement has been satisfied, after which a map is annotated according to the machine-recognized feature.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first object included in at least one image in accordance with an execution of an image processing algorithm, analyzing a plurality of parameters in accordance with at least one model responsive to the identifying of the first object included in the at least one image, wherein each parameter of the plurality of parameters is associated with the first object or a second object, selecting one of the first object or the second object for receiving at least one communication network resource responsive to the analyzing of the plurality of parameters, wherein the selecting results in a selected object, and presenting the selected object on a presentation device. Other embodiments are disclosed.