18 results about "Qos management" patented technology

A method for quality of service (QoS) flow control dynamically optimizes network resource allocation based on terminal power consumption status. An access network function receives terminal status information from an application function (AF) and determines a QoS profile for associated QoS flows from alternative profiles. The selected QoS profile is transmitted to a core network function or terminal for QoS updates. The terminal status information, which may include battery level, CPU load, or overheating status, informs the selection process to balance performance and efficiency. Core network functions manage QoS updates by interacting with the AF, access network, and terminals. The method, apparatus and computer readable medium ensure adaptive and energy-efficient QoS management for improved user experience and resource utilization in diverse network environments.

The application provides a quality of service (QoS) management method, which comprises the following steps: a first device sends first information, the first information is used for requesting a QoS for a first service; the first device receives second information, the second information is used for indicating a QoS control mode and a QoS parameter of a first QoS flow, wherein the first QoS flow is used for transmitting a data flow of the first service, and the QoS control mode comprises a first control mode based on packet group control; and the first device performs QoS control on the data flow of the first service according to the second information. According to the application, the first device can perform QoS control on the data flow of the first service with packet group as granularity, the flexibility of QoS management is improved, the requirements of different services are met, the user experience is improved, and the waste of network resources is reduced.

Embodiments of the present application provide a QoS parameter configuration method, device, apparatus and storage medium, wherein the method is applied to a QoS management node, comprising: determining a QoS parameter used by a sidelink interface of a direct communication link in a target transmission direction between a relay terminal and a target communication peer of the QoS management node; and sending the QoS parameter to the relay terminal and / or the target communication peer. Through the QoS parameter configuration method, device, apparatus and storage medium provided by the embodiments of the present application, the QoS of an end-to-end bearer / DRB in an L2 sidelink relay scenario is ensured.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.

A quality of service QoS management method includes the steps of: a first device sending first information, where the first information is for requesting QoS of a first service; a first device receiving second information, where the second information indicates a QoS control scheme and QoS parameters of a first QoS flow, where the first QoS flow is for transmitting a data flow of the first service, where the QoS control scheme includes a first control scheme for controlling based on a data packet group; and a first device performing QoS control on the data flow of the first service based on the second information.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.

Embodiments of the present disclosure provide a communication method and apparatus, a communication device, a communication system, and a storage medium. The communication method comprises: receiving first information, wherein the first information is used for indicating QoS parameters related to computing power openness of AI. By means of the embodiments of the present disclosure, QoS management for computing power sharing can be achieved.

A quality of service QoS management method includes: a first device sends first information, where the first information is for requesting QoS for a first service; the first device receives second information, where the second information indicates a QoS control manner and a QoS parameter of a first QoS flow, the first QoS flow is for transmitting a data flow of the first service, and the QoS control manner includes a first control manner for controlling based on a data packet group; and the first device performs QoS control on the data flow of the first service based on the second information.

Disclosed are computerized systems and methods for a decision intelligence (Dl)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.

Systems, methods, and computer software are disclosed for providing an Open Radio Access Network (RAN) networking infrastructure. In one embodiment a method is disclosed, comprising: providing real-time OpenRAN controller responsible for radio connection management, mobility management, QoS management, edge services, and interference management for the quality of end user experience; and providing a non-real-time controller in communication with the real-time OpenRAN controller, the non-real-time controller providing functionality such as configuration management, device management, fault management, performance management, and lifecycle management for all network elements in a network.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.

A station (STA) in a wireless network is configured to transfer a stream classification service (SCS) protocol established with an access point (AP) to a different AP while roaming, including sending a frame to the AP causing the transfer, receiving a response from the AP regarding whether the transfer to the different AP has been accepted, rejected, or accepted conditionally and a set of alternative parameters for the SCS protocol, and seamlessly roaming to the different AP while maintaining the SCS protocol established for network traffic without establishing a new SCS protocol.

Provided in the embodiments of the present application are a communication method, a communication apparatus, a transmitting end and a receiving end. The method comprises: configuring first information, wherein the first information includes one or more AMSDUs, and each AMSDU corresponds to a respective SCS stream identifier. The present application can improve the degree of differentiation of QoS management provided for data of different SCS streams, thereby effectively filling gaps in existing communication protocols.

The invention discloses an automobile assembly line communication coordination system and method based on an industrial network, and relates to the technical field of communication coordination, and the method comprises the steps: calculating a single welding quality score Q in real time for an edge calculation node located at each welding station, generating a welding event fingerprint, and carrying out annular caching; determining whether to upload the high-resolution data and the original waveform to a rear end based on a threshold value and the calculated joint anomaly score; qoS management is carried out on the control flow and the quality return flow; welding event fingerprints reported by the edge are received, backtracking data are stored, a network-quality joint causal analysis model is executed, and model updating is issued; and according to a causal analysis result and a historical model, implementing automatic welding parameter fine tuning, repair welding triggering, local redundancy switching and rollback strategies. According to the method, the problems that the welding process is extremely sensitive to delay jitter and intermittent welding defects and traceability data missing are caused by high-resolution quality data backtracking in an existing automobile assembly production line industrial network are solved.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.

The present application relates to communication methods and communication apparatuses. An example network function includes a quality of service (QoS) management (QoM) component and a data processing (DaP) component. The QoM component is configured to send, to the DaP component, a service data packet identified by a quality of service (QoS) flow identifier (QFI) or one or more fields in the QFI. The DaP component is configured to process the service data packet based on the QFI or the one or more fields in the QFI to generate a processing result.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.