Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.

Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.

Systems and methods are provided for dynamically determining optimal 5G New Radio (NR) configuration for dual Radio Access Technology (RAT) technology capable user equipment (UE). After receiving an indication a UE has connected to a particular sector, an eNodeB having a nonstandalone (NSA) 5G node and a standalone (SA) 5G node requests historical information or geotagged data corresponding to the UE. The eNodeB determines a NSA signal quality for a 5G node of the eNodeB and a SA signal quality of the 5G SA node. Based on a delta of the NSA signal quality and the SA signal quality being below a predetermined threshold, the eNodeB uses the historical information or the geotagged data to dynamically assign the UE to the NSA 5G node or the SA 5G node.

A system described herein may provide a technique for the assignment of Centralized Units (“CUs”) to Distributed Units (“DUs”) in a radio access network (“RAN”) that includes a distributed or hierarchical arrangement of network infrastructure equipment. Different groups of DUs may be modeled based on usage or traffic patterns, and complementary groups of DUs may be identified based on measures of usage that may vary with time. For example, one model associated with one group of DUs may experience relatively heavy usage during morning hours and light usage during evening hours, and another model associated with a complementary group of DUs may experience relatively light usage during morning hours and heavy usage during evening hours.

A graph of devices is constructed, each network device serving an amount of bandwidth over the network, each vertex of the graph corresponding to a respective one of the network devices, each edge of the graph connecting network devices by interference weight, such that edges between connected network devices using different channels have an interference weight of zero, and edges between connected network devices using the same channel have an interference weight denoting an amount of interference between the connected network devices. A cell utilization of each of the network devices is determined according to an amount of traffic served by the respective network device compared to a total of the amounts of bandwidth for all network devices. A vehicle requesting bandwidth is assigned to the one of the network devices having a smallest product of cell utilization and maximum interference weight edge.

According to certain embodiments, a method for use in a network node comprises receiving a request to connect a session of a wireless device. The wireless device is located in a service area of a Local Area Data Network (LADN) and a subscription associated with the wireless device permits access to the LADN. The method further comprises determining whether to select the LADN for the session. The determining is based on one or more factors associated with the LADN. The one or more factors comprise at least one of the following: loading conditions, service quality, historic data, and subscriber priority. The method further comprises sending, to another network node, a message indicating whether the LADN has been selected for the session.

The present disclosure relates to capacity planning methods and apparatus. One example method includes matching a distribution model based on a quantity of service packets in each transmission time interval within specified duration to obtain a matched first distribution model, matching a distribution model based on a length of the service packets to obtain a second distribution model, and performing bandwidth control based on the first distribution model, the second distribution model, a distribution parameter of the first distribution model, and a distribution parameter of the second distribution model.

A traffic demand to be experienced by a base station (BS) at a target time instant is forecasted by obtaining a long-term component of the traffic demand and predicting a short-term component thereof. To predict the short-term component, a service area of the BS is imaged at an observation-making time instant earlier than the target time instant. The number of people captured in the image and located in the service area is determined. The BS collects identifiers of user equipments (UEs), such as media access control (MAC) addresses, to determine the number of UEs present at the observation-making time instant. The short-term component is computed from the number of people and the number of UEs via a mathematical function determined by a genetic algorithm or a machine-learning algorithm according to historical records. The forecasted traffic demand facilitates channel allocation for the BS.

A method for performing mobility load balancing includes receiving, at a server, current load data for a plurality of cells of a wireless communication network, selecting, from the plurality of cells, a target cell (s), wherein a value of the current load data for the target cell exceeds a first predefined threshold, and selecting, from a neighbor cell list corresponding to the target cell, a set of neighboring cells for the target cell. The method further includes calculating, a value of at least one utilization parameter for the target cell, determining, a CIO value and an E-tilt value for the target cell based on the value of the at least one utilization parameter for the target cell and configuring one or more physical layer parameters of the target cell based on the determined CIO and E-tilt values for the target cell.

A method for connecting to a network and a related product are provided. The method includes: determining a motion section and a motion rate of an electronic device in response to the electronic device accessing to a mobile data network; estimating a target network rate of the mobile data network based on the motion rate and the motion section; and connecting to a target wireless network when the target network rate is less than a preset threshold.