Abstract:
A method and apparatus for providing planning of a plurality of mobile switching centers in a wireless network are disclosed. For example, the method obtains input data, and determines a limit for at least one mobile switching center parameter in accordance with the input data. The method determines if the limit for the at least one mobile switching center parameter is exceeded and determines an optimal output for an objective function, wherein the objective function is based on a plurality of penalty factors, if the limit for the at least one mobile switching center parameter is exceeded.
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:
The described technology is generally directed towards user equipment geolocation. Network measurement data associated with user equipment can be separated into static periods in which the user equipment was not moving, and moving periods in which the user equipment was moving. Static location processing can be applied to determine static locations from the static period network measurements, and moving location processing can be applied to determine moving locations from the moving period network measurements. Resulting static location information and moving location information can then be merged in order to improve the accuracy of both the static and the moving location information. The enhanced accuracy location information can be stored and used for any desired application.
Abstract:
The described technology is generally directed towards user equipment (UE) geolocation using a long history of network information. In some examples, a long history of network information associated with a UE can be processed to identify frequently repeated serving cell and correlated timing advance values. The frequently repeated serving cell and correlated timing advance values are indicative of frequently visited places. Next, the long history can be leveraged to determine locations of the frequently visited places with enhanced accuracy, and the resulting enhanced accuracy locations can be identified in a location lookup table for the UE. When the UE subsequently connects to the frequently repeated serving cell and the correlated timing advance value is observed, the location lookup table can be used to quickly assign an enhanced accuracy location to the UE.
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:
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:
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:
The described technology is generally directed towards user equipment (UE) geolocation. A machine learning model can be trained to estimate UE locations based on historical network communication data associated with the UEs. In order to train the machine learning model, known previous UE locations and corresponding historical network communication data can be provided to the machine learning model. A variety of other information, such as topographical information, can also be provided to the machine learning model. The machine learning model can be trained to predict the known previous UE locations based on the corresponding historical network communication data and any other provided information. Once it is trained, the machine learning model can be deployed to estimate real-time UE locations based on historical network communication data associated with the UEs.
Abstract:
Concepts and technologies disclosed herein are directed to the optimization of over-the-air (“OTA”) file distribution for connected cars based upon a heuristic scheduling algorithm. A schedule provided by the heuristic scheduling algorithm is designed to distribute OTA data flow to connected cars over the network (geographically) and over a scheduling time horizon (timely), and is capable of reducing the negative impact of OTA file updates on overall wireless network performance. This schedule is created based upon historical statistics associated with connected car driving patterns and simulations of connected car-specific OTA traffic over the network. By leveraging connected cars that connect to different cells at different times based upon driving patterns, the heuristic scheduling algorithm is effective in reducing OTA impact on the network.