H04L47/824

Radio network node, user equipment (UE), system and methods performed therein for handling communication in a wireless communication network

According to an embodiment herein a method performed by a radio network node for handling communication of data for a user equipment, UE, served in a service area associated with the radio network node in a wireless communication network is herein provided. The radio network node receives an indication from the UE, wherein the indication indicates a priority level for bandwidth allocation compared to other UEs. The radio network node further allocates a first bandwidth out of a total bandwidth to the UE for communication, wherein the first bandwidth is allocated in size based on the indication.

PROVIDING QUALITY OF SERVICE BASED ON BANDWIDTH

A method for determining a Quality of Service (QoS) policy can be based on requested bandwidth. The method may initially receive a connection request which includes a requested bandwidth that corresponds to an application. The method may then determine a policy for an application data flow associated with the application based on the connection request. A bandwidth designation, which is based on the requested bandwidth, may be assigned to the application data flow based on the determined policy. Finally, the policy and the bandwidth designation may be provided so that a bearer can be assigned.

Methods of and devices for implementing and executing policy rules on a per application basis in a telecommunications system

Implementation of an application rule for an application to be accessed by a User Equipment, UE, in a user session in a Service Based Architecture, SBA, domain in a core network of a telecommunications system is disclosed. The SBA, among others, comprises a Policy Control Function, PCF (6), an Application Function, AF (5), and a Session Management Function, SMF (9). The method comprising the steps of receiving, by the PCF (6), an application rule comprising an AF Identifier, AF-ID, identifying the application rule, an Application Identifier, App-ID, identifying the application, and at least one service requirement for processing the application in the SBA domain. The PCF (6) instructing the SMF (9) to execute the at least one service requirement to all present and future user sessions pertaining to the respective application. Complementary methods of supporting the execution of the application rule and devices are also presented.

ALLOCATING RESOURCES FOR COMMUNICATION AND SENSING SERVICES
20230232273 · 2023-07-20 ·

An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processor, cause the apparatus at least to perform determining, per a communication service to which resources are to be allocated, a metric value using a first predefined set of rules, determining, per a sensing service to which resources are to be allocated, a metric value using a second predefined set of rules, sorting communication services to which resources are to be allocated and sensing services to which resources are to be allocated based on the metric values to a sorted order using a third rule, and allocating resources for the communication services and the sensing services based on the sorted order.

Resource management apparatus, resource management system, and resource management method
11695705 · 2023-07-04 · ·

A resource management apparatus, a resource management system, and a resource management method. The resource management apparatus stores in one or more memories, positional relation of a resource and reservation information related to a reservation of the resource, in response to receiving a usage start request for starting a use of the resource from a communication terminal, determines whether one or more resources in surroundings are secured based on the positional relation and the reservation information, identifies the resource as an available resource in response to a determination that the one or more resources in the surroundings are not secured, and transmits usage information regarding the use of the available resource to the communication terminal.

Systems and methods to utilize edge computing to respond to latency in connected vehicles

Systems and methods for responding to latency in connected vehicles. Vehicles operating on or near the edge of networks are identified. Vehicle dependent nodes are identified. Latency is calculated between the vehicle and the dependent nodes. The latency is compared to one or more threshold values and a response is determined and/or executed.

Device group partitions and settlement platform
11533642 · 2022-12-20 · ·

Device group partitions and a settlement platform are provided. In some embodiments, device group partitions (e.g., partitions of devices based on associated device groups) are provided. In some embodiments, a settlement platform service is provided. In some embodiments, a settlement platform service is provided for partitioned devices. In some embodiments, collecting device generated service usage information for one or more devices in wireless communication on a wireless network; and aggregating the device generated service usage information for a settlement platform for the one or more devices in wireless communication on the wireless network is provided. In some embodiments, a settlement platform implements a service billing allocation and/or a service/transactional revenue share among one or more partners. In some embodiments, service usage information includes micro-CDRs, which are used for CDR mediation or reconciliation that provides for service usage accounting on any device activity that is desired. In some embodiments, each device activity that is desired to be associated with a billing event is assigned a micro-CDR transaction code, and a service processor of the device is programmed to account for that activity associated with that transaction code. In some embodiments, a service processor executing on a wireless communications device periodically reports (e.g., during each heartbeat or based on any other periodic, push, and/or pull communication technique(s)) micro-CDR usage measures to, for example, a service controller or some other network element for CDR mediation or reconciliation.

Enhanced real-time linking methods and systems

Systems and methods for enabling links between various devices is provided. The systems and methods may include a platform that enables different devices to access spatial models of a resource. The platform may enable the different devices to define and/or modify assignment conditions for access rights to resources. Further, the platform may enable definition of assignment conditions before or after the access rights are available for assignment.

Master station device, base station, and communication control method
11510282 · 2022-11-22 · ·

A base station determines, based on transmission quality information of a fronthaul and channel quality information of a terminal, a resource and a transmission scheme of the fronthaul assigned to the terminal, and controls, based on determined information, the transmission scheme of a signal to be transmitted to the fronthaul using the determined resource.

METHOD OF LOAD FORECASTING VIA ATTENTIVE KNOWLEDGE TRANSFER, AND AN APPARATUS FOR THE SAME

A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.