At&t Intellectual Property I, LP

At&t Mobility Ii LLC

Huahui Wang

Ravi Raina

Gopalakrishnan Meempat

Wenjie Zhao

Sarat Puthenpura

Sam Houston Parker

Jie Chen

David Gerald Belanger

Henry Kafka

Laurie Bigler

Huijing Yang

Tae Won Cho

Xiaoyu Wang

Raghvendra Savoor

Stuart Blackburn

Vishwanath Ramamurthi

Dipak Ghosal

Ke Zhang

Slawomir Mikolaj Stawiarski

Weihua Ye

Yuning Yang

Hyun Ok Lee

Ganesh Krishnamurthi

Ye Ge

Dongho Kim

Senthil Raj Dhandapani

Kemal Kara

Yu Chen

Tony Thayil

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.

Enhanced network with data flow differentiation

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.

Facilitating implementation of communication network deployment through network planning in advanced networks

Intelligent, automated, fixed wireless internet planning (e.g., using a computerized tool) is enabled. For instance, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, for a user equipment determined to be within a defined coverage area, a signal to interference and noise ratio, based on the signal to interference and noise ratio, determining a spectral efficiency value corresponding to the user equipment, based on the spectral efficiency value and a total available bandwidth of a network via which the defined coverage area is enabled, determining an available throughput corresponding to the user equipment, and in response to a determination that the available throughput exceeds a threshold throughput, designating the user equipment, in a data store, as being covered within the defined coverage area by the total available bandwidth of the network.

Planning of fixed wireless internet

Distribution of traffic to cells in a communication network can be controlled. A distribution management component (DMC) can determine overall device traffic throughput for cells of a sector that satisfy a defined traffic throughput criterion relating to a harmonic mean of the device traffic throughput for the cells to desirably enhance or maximize the harmonic mean of the overall device traffic throughput. Based on the overall device traffic throughput for the cells, the DMC can determine whether to adjust a characteristic associated with a cell of the cells to facilitate adjusting distribution of device traffic among the cells of the sector to achieve desirable load balancing of traffic by the sector and in the network. Load balancing can be achieved by controlling respective parameters with regard to communication devices that are in idle mode or connected mode to facilitate directing communication devices and associated traffic to desired cells.

Load balancing in wireless networks for improved user equipment throughput

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.

Radio access network control with deep reinforcement learning

Aspects of the subject disclosure may include, for example, calculating a respective first quality metric for each cell of a plurality of cells included in a network, calculating a respective second quality metric for each cell of the plurality of cells, calculating a capacity of each cell of the plurality of cells in accordance with the first quality metric for the cell and the second quality metric for the cell, and allocating traffic of the network amongst the plurality of cells in accordance with the respective capacity of each cell of the plurality of cells. Other embodiments are disclosed.

Apparatuses and methods to facilitate load-aware radio access network resource allocations

The disclosed technology is directed towards load balancing in an adaptive and automated way for wireless mobility networks to improve the overall harmonic-average UE throughput within each controlled group of cells (e.g., different frequency carriers serving a sector of a base station). A load balancer (e.g., analytics component) obtains various device traffic data including throughput data for cells of a group. Pairs of cells in a group (sharing a site and face) can be selected based on satisfying various criteria, with estimated throughput gain achieved by changing the handoff rates between the cell pairs. The technology iteratively repeats the overall process, driving a system to an optimal equilibrium.

Adaptive automated load balancing for mobility networks including fifth generation networks and beyond

Aspects of the subject disclosure may include, for example, calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput. Other embodiments are disclosed.

Apparatuses and methods for estimating throughput in accordance with quality of service prioritization and carrier aggregation to facilitate network resource dimensioning

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.

Controlling quality of service class identifier weights for improved performance in 5G or other next generation networks

Aspects include determining whether a utilization of wireless spectrum associated with a guaranteed class of traffic in a network is greater than a first threshold, responsive to the determining indicating that the utilization of the wireless spectrum associated with the guaranteed class of traffic is greater than the first threshold, causing an upgrade of a capacity in the network, and responsive to the determining indicating that the utilization of the wireless spectrum associated with the guaranteed class of traffic is not greater than the first threshold: determining a throughput for a non-guaranteed class of traffic for each cell of a plurality of cells of the network, and responsive to determining that the throughput for the non-guaranteed class of traffic for at least one cell of the plurality of cells is less than a second threshold, causing the upgrade of the capacity in the network. Other embodiments are disclosed.

Apparatuses and methods for network resource dimensioning in accordance with differentiated quality of service

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.

Real-time user traffic classification in wireless networks

Distribution of traffic to cells in a communication network can be controlled. User equipment (UE) can perform measurements regarding signal quality with cells and communicate measurement information and a connection request to a source cell. The source cell can establish an initial connection with the UE. Meanwhile, the UE can perform additional measurements and communicate additional measurement information to the source cell. A distribution management component (DMC) can analyze the measurement information and cell-related information and determine whether to redirect the UE from the source cell to a target cell based on the analysis results. If the DMC determines that the UE is to be redirected to the target cell, the DMC can release the connection to the source cell and communicate a redirect message that includes target cell information to the UE, and the UE can send a connection request to the target cell.

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Bridgewater, NJ, United States of America

Huahui Wang