Abstract:
The disclosed technology describes on-demand adjusting of the quality of service (QoS) class identifier (QCI) relative weight settings for different QCI classes associated with user devices. A QoS controller, which can be implemented in a standalone or a cloud configuration, updates the QCI weight settings as requested or needed to deal with changing network environments, such as growing traffic, different RF conditions, new devices, ratio of different service classes of user devices, to improve the overall performance of one or more cell sites. In one implementation, the QoS controller collects actual network statistics, and runs simulations based on those statistics with different groups of candidate QCI weight settings to generate multiple result sets. The QoS controller evaluates the result sets to determine which group of QCI weight settings meets desired performance objectives, and then applies those QCI weight settings to one or more NodeBs for use with actual data traffic.
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:
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:
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:
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:
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:
A method and apparatus for selecting a bandwidth option for a cell in a network are disclosed. For example, the method obtains, for the cell, network traffic data for a geographical area, and a physical characteristic of an antenna in the geographical area, determines busy time data traffic from the network traffic data, determines, for the cell, a cell range from the physical characteristic of the antenna, selects a bandwidth option from a plurality of bandwidth options, and determines an average throughput in accordance with the bandwidth option that is selected and the cell range.
Abstract:
Aspects of the subject disclosure may include, for example, receiving information about a data flow for radio communication between the radio access network and user equipment, classifying the data flow as one of a large data flow and a small data flow, adjusting priority of the data flow by reducing relative priority of the data flow responsive to classifying the data flow as a large data flow, and communicating data including the data flow between the radio access network and the user equipment according to the adjusted priority. Other embodiments are disclosed.
Abstract:
Aspects of the subject disclosure may include, for example, receiving information about a data flow for radio communication between the radio access network and user equipment, classifying the data flow as one of a large data flow and a small data flow, adjusting priority of the data flow by reducing relative priority of the data flow responsive to classifying the data flow as a large data flow, and communicating data including the data flow between the radio access network and the user equipment according to the adjusted priority. Other embodiments are disclosed.
Abstract:
Aspects of the subject disclosure may include, for example, receiving information about a data flow for radio communication between the radio access network and user equipment, classifying the data flow as one of a large data flow and a small data flow, adjusting priority of the data flow by reducing relative priority of the data flow responsive to classifying the data flow as a large data flow, and communicating data including the data flow between the radio access network and the user equipment according to the adjusted priority. Other embodiments are disclosed.