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 to 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.
Abstract:
Techniques for locating a mobile device using a time distance of arrival (TDOA) method with disturbance scrutiny are provided. In an aspect, for respective combinations of three base station devices of a number of base station devices greater than or equal to three, intersections in hyperbolic curves, generated using a closed form function with input values based on differences of distances from the device to pairs of base station devices of the respective combinations of three base station devices, are determined. The intersection points are then tested for robustness against measurement errors associated with the input values and a subset of the intersection points that are associated with a degree of resistance to the measurement errors are selected to estimate a location of the device.
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:
Intelligent, adaptive scheduling weight adjustment is enabled, e.g., to improve network performance. For instance, A non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising based on key performance indicators corresponding to data traffic flows via a network, determining quality of service data representative of respective qualities of service for the data traffic flows, using a scheduling weight data traffic model generated using machine learning and trained using past quality of service data representative of past qualities of service of past data traffic flows via the network, from prior to the data traffic flows, and past scheduling weight settings applied to the past data traffic flows, determining a scheduling weight setting to be applied to a data traffic flow of the data traffic flows, and applying the scheduling weight setting to the data traffic flow.
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 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:
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 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:
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:
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.