Abstract: Aspects of the subject disclosure may include, for example, a process for selecting equipment locations such as of cellular antennas, based on a combination of a geospatial grid representation of a planning area and optimization algorithms (which can be combined with propagation models and a 3D model of the world) where the optimization algorithm can select a deployment from a large space of options and would make RAN planning much more efficient. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including receiving a call; selecting a next carrier to handoff the call; generating a call data record (CDR) for the handoff to the next carrier; encrypting the CDR using a call encryption key, thereby creating an encrypted CDR; encrypting the encrypted CDR using a committee encryption key, thereby creating a double encrypted CDR; recording the double encrypted CDR to a blockchain; and sending the call encryption key to the next carrier. 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, responsive to a viewing request associated with a media content item, for each original content segment of a plurality of content segments of the media content item determining whether content characteristic information associated with the original content segment is compatible with viewing context information associated with the viewing request, and, if not compatible, then replacing the original content segment with an alternative content segment of the plurality of content segments, and streaming the alternative content segment to a user device, and, if compatible, then responsive to determining the content characteristic information is compatible with the viewing context information, streaming the original content segment to the user device. Other embodiments are disclosed.

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: 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: 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 including at least one processor may obtain a time series of measurement values from a communication network and train a prediction model in accordance with the time series of measurement values to predict future instances of an event of interest, where the time series of measurement values is labeled with one or more indicators of instances of the event of interest. The processing system may then generate a deterministic finite automaton based upon the prediction model, convert the deterministic finite automaton into a rule set, and deploy the rule set to at least one network component of the communication network.

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, 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: 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, 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, responsive to a viewing request associated with a media content item, for each original content segment of a plurality of content segments of the media content item determining whether content characteristic information associated with the original content segment is compatible with viewing context information associated with the viewing request, and, if not compatible, then replacing the original content segment with an alternative content segment of the plurality of content segments, and streaming the alternative content segment to a user device, and, if compatible, then responsive to determining the content characteristic information is compatible with the viewing context information, streaming the original content segment to the user device. Other embodiments are disclosed.

Abstract: Architectures and techniques are presented that improve or enhance a network planning procedure such as interactively planning suitable locations for transceiver sites that communicate with one another. Map data indicative of a 3D depiction of a physical space can be presented to a user interface device. Input indicative of a first transceiver site and a second transceiver site can be received. A Fresnel zone between the first transceiver site and the second transceiver site can be determined based on the map data. An interactive representation of the Fresnel zone can be presented to the user interface device.

Abstract: Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving a first record of sending a file and a hash for the file from a sender of the file; recording the first record on a blockchain; providing the first record to a receiver of the file; and monitoring a predetermined time period in which to receive a second record of receiving the file and the hash for the file. Other embodiments are disclosed.

Abstract: Concepts and technologies are disclosed herein for creating and using network coverage models. A request for a predicted coverage model that represents a first signal propagation in a first portion of a network that covers a first area associated with a first geographic location can be received. An aerial image that depicts the first area can be obtained. The aerial image can be provided to an existing coverage model. The existing coverage model can include a neural network, and the existing coverage model can be based on a second signal propagation in a second portion of the network that covers a second area associated with a second location. The predicted coverage model for the first area can be obtained from the existing coverage model.

Abstract: Architectures and techniques are presented that improve or enhance a network planning procedure such as interactively planning suitable locations for transceiver sites that communicate with one another. Map data indicative of a 3D depiction of a physical space can be presented to a user interface device. Input indicative of a first transceiver site and a second transceiver site can be received. A Fresnel zone between the first transceiver site and the second transceiver site can be determined based on the map data. An interactive representation of the Fresnel zone can be presented to the user interface device.

Abstract: Concepts and technologies disclosed herein are directed to location-based blockchain. A localized corroborator system can receive, from a user device, an initiation message including a public key, and in response, can generate and send the random session identifier to the user device. The system can receive, from the user device, a signed session identifier including the random session identifier signed by the user device using a private key. The system can check a time that elapsed between sending the random session identifier to the user device and receiving the signed session identifier from the user device. The system can verify authenticity of the signed session identifier using the public key. In response to the time being less than a pre-defined threshold, the system can issue a location certificate to the user device. The user device can utilize the location certificate to conduct a certified blockchain transaction in a blockchain.