Abstract: Facilitating analysis and resource planning for advanced heterogeneous networks (e.g., 5G, 6G, and beyond) is provided herein. A system is provided that includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include determining that a resource is to be added to existing resources at a grid level of a heterogeneous network. Further, the operations can include selecting candidate locations for placement of the resource based on a coverage-driven objective and a capacity-driven objective defined for the heterogeneous network. The coverage-driven objective can be associated with a demand for services within the grid level of the heterogeneous network. The capacity-driven objective can be associated with demand growth within the grid level of the heterogeneous network. The resource can be a fifth generation millimeter wave node or a cloud radio access network node.

Abstract: Locations and azimuths of cells of a communication network can be estimated, determined, and validated. Cell attribute management component (CAMC) can estimate, determine, and/or validate cell locations based on analysis of timing advance (TA) measurement data and/or location data associated with devices associated with base stations associated with cells. CAMC can estimate azimuth of a cell associated with a base station based on analysis of a validated cell location of the cell and location data associated with devices associated with the cell. CAMC can determine whether a recorded azimuth of the cell is validated based on analysis of the estimated azimuth of the cell and the recorded azimuth of the cell. CAMC can tag the recorded azimuth of the cell as validated if applicable azimuth accuracy criteria is met, inaccurate if applicable azimuth criteria is not met, or omni if the cell is an omni cell.

Abstract: Facilitating implementation of communication network deployment through network planning in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a system can include, configuring a first deployment scenario for first network equipment and a second deployment scenario for second network equipment. The first deployment scenario is selected from a group of first deployment scenarios and can include a first parameter. The second deployment scenario is selected from a group of second deployment scenarios and can include a second parameter. The configuring can include determining that a sum of the first parameter and the second parameter satisfies a function of a defined parameter level. The operations also can include facilitating a first enactment of the first deployment scenario for the first network equipment and a second enactment of the second deployment scenario for the second network equipment.

Abstract: A processing system including at least one processor may obtain operational data from a radio access network (RAN), format the operational data into state information and reward information for a reinforcement learning agent (RLA), processing the state information and the reward information via the RLA, where the RLA comprises a plurality of sub-agents, each comprising a respective neural network, each of the neural networks encoding a respective policy for selecting at least one setting of at least one parameter of the RAN to increase a respective predicted reward in accordance with the state information, and where each neural network is updated in accordance with the reward information. The processing system may further determine settings for parameters of the RAN via the RLA, where the RLA determines the settings in accordance with selections for the settings via the plurality of sub-agents, and apply the plurality of settings to the RAN.

Abstract: Predicting small cell capacity and coverage to facilitate offloading of macrocell capacity is presented herein. A system selects a group of candidate locations for placement of respective small cells to facilitate offloading, via the respective small cells, of traffic from respective macrocells corresponding to the candidate locations—the respective small cells including first transmission powers that are less than second transmission powers of the respective macrocells. Further, for each candidate location of the group of candidate locations, the system determines an estimated amount of traffic capacity of a small cell of the respective small cells that has been presumed to have been placed at the candidate location, and determines estimated signal strengths of respective signals that have been predicted to have been received from the small cell at respective portions of a grid of a defined signal coverage area corresponding to the candidate location.

Abstract: Facilitating analysis and resource planning for advanced heterogeneous networks (e.g., 5G, 6G, and beyond) is provided herein. A system is provided that includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include determining that a resource is to be added to existing resources at a grid level of a heterogeneous network. Further, the operations can include selecting candidate locations for placement of the resource based on a coverage-driven objective and a capacity-driven objective defined for the heterogeneous network. The coverage-driven objective can be associated with a demand for services within the grid level of the heterogeneous network. The capacity-driven objective can be associated with demand growth within the grid level of the heterogeneous network. The resource can be a fifth generation millimeter wave node or a cloud radio access network node.

Abstract: A system includes a first network edge data collector, a first network edge key performance indicator (KPI) engine configured to operate on first data collected by the first network edge data collector, a KPI metrics manager in communication with the first network edge KPI engine, the KPI metrics manager controlling a KPI metric catalog and wherein the first network edge KPI engine determines first edge KPI metric using a metric algorithm from the KPI metric catalog on the first data.

Abstract: Predicting small cell capacity and coverage to facilitate offloading of macrocell capacity is presented herein. A system selects a group of candidate locations for placement of respective small cells to facilitate offloading, via the respective small cells, of traffic from respective macrocells corresponding to the candidate locations—the respective small cells including first transmission powers that are less than second transmission powers of the respective macrocells. Further, for each candidate location of the group of candidate locations, the system determines an estimated amount of traffic capacity of a small cell of the respective small cells that has been presumed to have been placed at the candidate location, and determines estimated signal strengths of respective signals that have been predicted to have been received from the small cell at respective portions of a grid of a defined signal coverage area corresponding to the candidate location.

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: The described technology is generally directed towards automated cell parameter updates in cellular networks. A network automation platform architecture is disclosed which includes elements configured to automatically analyze, recalculate, and deploy cell parameters such as PCI and RSI parameters, for cells of a cellular communication network.

Abstract: Locations of cells of a communication network can be estimated, determined, and validated. Cell location component (CLC) can analyze timing advance (TA) measurement data and/or location data associated with devices associated with a base station associated with one or more cells. CLC can estimate a first location of the base station, based on the TA measurement data and/or location data, to facilitate estimating the location of an associated cell. CLC can validate the estimated cell location or recorded cell location of the cell (recorded in a cell location pool) based on analysis of estimated cell location, recorded cell location, TA measurement data, and/or location data, and, based on the validation, can tag the cell location determination as accurate, acceptable, bad, or uncertain. CLC can request additional monitoring of a cell location determination tagged as uncertain, or investigation of a cell location determination tagged as bad.

Abstract: Provided is a tailstock type vertical take-off and landing unmanned aerial vehicle and a control method thereof. The unmanned aerial vehicle is mainly composed of a fuselage, wings, ailerons, empennages, an elevator, a rudder, an engine, an attitude adjustment nozzle, a landing gear, and the like. The wings are symmetrically arranged on both sides of the middle of the fuselage; the ailerons are hinged to the trailing edges of the wings on the both sides; the empennages are located at the tail of the fuselage, and a form of vertical empennages+horizontal empennages or V-shaped empennages can be used; the elevator and rudder are hinged to the trailing edges of the empennages; the engine is arranged at the tail of the fuselage for producing main thrust.

Abstract: Facilitating analysis and resource planning for advanced heterogeneous networks (e.g., 5G, 6G, and beyond) is provided herein. A system is provided that includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include determining that a resource is to be added to existing resources at a grid level of a heterogeneous network. Further, the operations can include selecting candidate locations for placement of the resource based on a coverage-driven objective and a capacity-driven objective defined for the heterogeneous network. The coverage-driven objective can be associated with a demand for services within the grid level of the heterogeneous network. The capacity-driven objective can be associated with demand growth within the grid level of the heterogeneous network. The resource can be a fifth generation millimeter wave node or a cloud radio access network node.

Abstract: The described technology is generally directed towards automated cell parameter updates in cellular networks. A network automation platform architecture is disclosed which includes elements configured to automatically analyze, recalculate, and deploy cell parameters such as PCI and RSI parameters, for cells of a cellular communication network.

Abstract: Predicting small cell capacity and coverage to facilitate offloading of macrocell capacity is presented herein. A system selects a group of candidate locations for placement of respective small cells to facilitate offloading, via the respective small cells, of traffic from respective macrocells corresponding to the candidate locations—the respective small cells including first transmission powers that are less than second transmission powers of the respective macrocells. Further, for each candidate location of the group of candidate locations, the system determines an estimated amount of traffic capacity of a small cell of the respective small cells that has been presumed to have been placed at the candidate location, and determines estimated signal strengths of respective signals that have been predicted to have been received from the small cell at respective portions of a grid of a defined signal coverage area corresponding to the candidate location.

Abstract: The technologies described herein are generally directed to facilitating operation of a system for implementing fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include identifying, by a device comprising a processor, predicted resource usage of a first antenna covering a geographic zone. Further, the method can include selecting, by the device, a group of geographic siting locations within the geographic zone for potentially siting ones of a group of second antennas. In addition, selecting, by the device, a spatial arrangement in relation to the first antenna, of a subset of the group of geographic siting locations can occur, with a selected spatial arrangement including an arrangement to maintain the predicted resource usage in the condition.

Abstract: A device can receive, from a network node device, call trace event data relating to characteristics of a wireless communication session between the network node device and a user equipment. The device can sequence and combine the call trace event data for a period of the wireless communication session. The device can analyze the call trace event data to determine a category of network communication traffic transmitted via a communication channel between the network node device and the user equipment. In response to a determination that the network communication traffic comprises streaming video packets, the device can facilitate directing of network resources to be allocated to support the wireless communication session.

Abstract: Facilitating model-driven automated cell allocation in advanced networks (e.g., 5G and beyond) is provided herein. Operations of a method can comprise determining, by a system comprising a processor, a solution to an integer programming problem based on input data associated with a network inventory and configuration data for network devices of a group of network devices included in a communications network. Also, the method can comprise determining, by the system, respective cell identities and respective root sequence index assignments for the network devices. Further, the method can comprise implementing, by the system, a deployment of the respective cell identities and respective root sequence index assignments at the network devices.

Abstract: Locations of cells of a communication network can be estimated, determined, and validated. Cell location component (CLC) can analyze timing advance (TA) measurement data and/or location data associated with devices associated with a base station associated with one or more cells. CLC can estimate a first location of the base station, based on the TA measurement data and/or location data, to facilitate estimating the location of an associated cell. CLC can validate the estimated cell location or recorded cell location of the cell (recorded in a cell location pool) based on analysis of estimated cell location, recorded cell location, TA measurement data, and/or location data, and, based on the validation, can tag the cell location determination as accurate, acceptable, bad, or uncertain. CLC can request additional monitoring of a cell location determination tagged as uncertain, or investigation of a cell location determination tagged as bad.

Abstract: The technologies described herein are generally directed to facilitating operation of a system for implementing fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include identifying, by a device comprising a processor, predicted resource usage of a first antenna covering a geographic zone. Further, the method can include selecting, by the device, a group of geographic siting locations within the geographic zone for potentially siting ones of a group of second antennas. In addition, selecting, by the device, a spatial arrangement in relation to the first antenna, of a subset of the group of geographic siting locations can occur, with a selected spatial arrangement including an arrangement to maintain the predicted resource usage in the condition.