16720results about "Network traffic/resource management" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

Embodiments of the subject application relate to methods and apparatuses for a delay status report associated with a protocol data unit (PDU) set. According to an embodiment of the subject application, a user equipment (UE) includes a processor and a transceiver coupled to the processor, and the processor is configured to: determine delay information associated with one or more protocol data unit (PDU) sets received from an upper layer of the UE, wherein the delay information includes at least one of: a remaining PDU set delay budget; a PDU set buffer delay; a PDU set delay budget offset; or first timing when receiving a PDU within the one or more PDU sets from the upper layer; and transmit a report including the delay information, wherein the report is at least one of: a delay budget report (DBR); a delay report (DR); an arriving timing report (ATR); or an enhanced buffer status report (EBSR).

The invention overcomes the constraints imposed by conventional wireless communication standards by introducing a physical-layer optimized architecture that decouples transmission control from any single protocol. Rather than being confined by the predefined behaviors of LTE, 5G NR, Wi-Fi, or NB-IoT, the system implements a unified control system that dynamically manages radio parameters—such as modulation, coding, and power—based on real-time link quality and network context. This cross-standard, multimode capability effectively supersedes traditional standard-driven implementations, enabling adaptive, low-latency, and spectrum-efficient communication in complex heterogeneous environments.The system dynamically controls radio access network (RAN) and physical layer parameters, including carrier aggregation, dynamic spectrum allocation, modulation and coding scheme (MCS) adaptation, beamforming configuration, channel coding, transmit power control, and frequency selection. The system architecture includes multi-mode base stations, relay nodes, and edge access points that support inter-RAT handover, fast radio link recovery, and seamless mobility across diverse wireless technologies.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

Embodiments include methods, electronic device, storage medium, and computer program to implement a dedicated queue based on a user request. In one embodiment, a method comprises: receiving a first message to optimize one or more data flows based on a quality-of-service request for an end user; enabling a first queue dedicated to the one or more data flows sourced for the end user in the network node based on the first message; and upon a determination of congestion in the network, marking packets in the one or more data flows stored in the first queue as candidates to drop based on a queue size of the first queue, wherein the marking is to set explicit congestion notification bits of the packets.

The invention provides a distributed collaboration-based computing network intelligent agent system and a resource dynamic scheduling method thereof, and belongs to the technical field of computing network integration. The system comprises an edge agent used for sensing local computing power, network bandwidth and task load in real time, predicting task demand fluctuation by using a lightweight neural network, adjusting resource allocation weight in real time in combination with network topology change, and executing a preliminary task scheduling decision; the regional collaborative agent is used for aggregating multiple edge node states based on federated learning, generating a cross-node resource scheduling strategy, verifying the credibility of a computing power transaction smart contract and determining a cross-domain resource allocation scheme; and the cloud management agent is used for constructing a global resource portrait model according to the information provided by the edge agent and the regional collaborative agent, performing long-term strategy optimization, issuing global strategy information, and constructing and updating a computing power transaction smart contract based on a preset computing power transaction smart contract template. According to the invention, multi-level refined scheduling of computing network resources is realized.

The invention discloses an air-space-ground caching and resource optimization method and system based on a hierarchical intelligent agent, and aims to construct a deep reinforcement learning architecture in which a high-layer DQN and a low-layer DDPG are coordinated for high dynamics and information uncertainty of an air-space-ground integrated network. The high-level intelligent agent generates a period-level content cache and access control strategy based on global states including a cache state, a task request, a node resource and the like, and the low-level intelligent agent executes time slot-level resource allocation, task unloading rate control and UAV deployment optimization under the constraint of the high-level strategy. The system triggers low-level optimization through a double-stage reward mechanism and constraint verification, evaluates a strategy effect based on time slot income and full-period income, and combines state perception and experience playback technologies to realize collaborative optimization of high and low-level decisions. A simulation result shows that compared with a traditional method, the method has remarkable advantages in the aspects of reducing content acquisition delay, reducing return communication overhead, improving task processing success rate and the like, and the space-air-ground MEC network resource utilization rate and user experience are effectively improved.

This disclosure describes systems, methods, and devices for quality of service (QOS)-level security configuration in a packet data unit (PDU) session. A device may identify a first user plane security indication received from an application function of a wireless network; identify a second user plane security indication received from the application function; generate, based on the first user plane security indication, a first security configuration for a first QoS flow of a PDU session; generate, based on the second user plane security indication, a second security configuration for a second QoS flow of the PDU session, the first security configuration different than the second security configuration; decode a first packet received, from the UE, in the first QoS flow using the first security configuration; and decode a second packet received, from the UE, in the second QoS flow using the second security configuration.

The invention discloses an Internet of Vehicles network selection and switching decision system supporting access of multiple operators. According to the method and the device, the timeliness and the continuity of network switching are remarkably improved through prediction and planning in advance. The module depends on a trajectory-network matching prediction sub-module, combines a vehicle navigation trajectory and a global digital twinborn model, identifies a base station coverage area in a future driving path and the network service quality of each operator in advance, and avoids the lag problem that switching is triggered only after signals are weakened in traditional passive switching. The pre-switching resource reservation sub-module further sends a resource request to a target operator base station and confirms reservation before the vehicle enters a new road section, ensures that resources such as bandwidth and time slot required during switching are in place, reduces switching failure or interruption caused by resource competition, avoids common communication interruption after signal sudden drop in traditional switching, and improves the switching efficiency. And continuous transmission of key businesses such as automatic driving control instructions and real-time road conditions is ensured.

The invention discloses a hospital Internet of Things equipment intelligent monitoring and fault early warning system based on edge computing, relates to the technical field of equipment monitoring and early warning, and aims to solve the technical problem that fault discovery lags in a high-real-time scene of an existing intelligent monitoring and fault early warning system. The system is used for collecting various data of hospital Internet of Things equipment and sending the data to a preset storage position, and comprises a local database, a data processing module and an abnormal data judgment unit; the Internet of Things base station is used for being connected with Internet of Things equipment, collecting equipment data and achieving intelligent monitoring and fault early warning through an algorithm model, and the algorithm model is constructed based on a core algorithm and rules; and the hospital Internet of Things edge computing platform is used for carrying out edge computing management on the data sent by the Internet of Things base station, feeding back an analysis result and storing the data. The method has the advantage of improving the fault discovery speed of the Internet of Things equipment.

The invention provides a production line scheduling method and equipment fusing edge computing and machine learning, and belongs to the technical field of production line scheduling, and the method comprises the steps: deploying edge nodes in an industrial production line, and carrying out the life cycle management and control of state monitoring, task issuing, resource configuration and version management on the edge nodes by a cloud; the edge nodes collect production line data in real time; constructing a scheduling model at the cloud, and issuing the scheduling model to the edge node; outputting execution task priority judgment and path and resource optimization in combination with the scheduling model, generating a scheduling instruction, and issuing the scheduling instruction to a field control system to execute task scheduling; recording all scheduling processes and state changes, and synchronizing to the cloud after the network is recovered; according to the invention, the analysis rule and the model parameters can be adjusted according to the actual working condition and the monitoring scene of the equipment, accurate description and abnormal early warning of the operation state of the equipment are realized, and the efficiency, the accuracy and the adaptability of equipment monitoring in the industrial Internet of Things environment are effectively improved.

Systems, methods, and tools may schedule protocol data unit (PDU) sets. A wireless transmit receive (WTRU) may receive configuration information, including a delay threshold and a configured grant (CG) arrangement, from a network node. The WTRU may receive a first PDU subset and related information from an application. The WTRU may estimate the payload size and arrival time of a second PDU subset. Based on the estimates and the payload size of the first subset, the WTRU may determine the first and second groups of CG PUSCH occasions. If the estimated arrival time difference between the two subsets is larger than the delay threshold, the WTRU may identify an available CG PUSCH occasion. The information may be sent to the network node.

The invention relates to the technical field of wireless communication, in particular to a production line wireless communication system and method. The system comprises a multi-dimensional feature sensing module, an interference intelligent cognition and decision-making module, a communication opportunity decision-making module and a channel resource arrangement module. An expected action sequence, transient current features and spatial position topology of an interference source are collected through a multi-dimensional feature sensing module, and an interference source multi-dimensional feature set is generated through fusion; the interference intelligent cognition and decision-making module performs space-time spectrum joint modeling of an interference field based on the feature set, generates a workshop electromagnetic environment situation map, constructs a whole network link toughness map in parallel, and generates a conflict-free communication scheduling table through fusion analysis and multi-dimensional cost optimization; the communication opportunity decision module converts the dispatch table into an executable instruction; and the channel resource arrangement module executes the instruction and realizes closed-loop feedback. According to the method, the problem of communication uncertainty caused by industrial electromagnetic interference is solved, and reliable transmission of key services such as automatic guided vehicle scheduling and man-machine interaction is ensured.

A first base station node receives, from a second base station node, a request to setup a context of a wireless device, wherein the request indicates that a packet flow is associated with a PDU set.

Techniques are described herein for handling multi-modal synchronization. An example method can include processing first information relating to multi-modal synchronization between a first quality of service (QoS) flow and a second QoS flow. The method can further include processing second information indicating whether a packet data unit (PDU) discard is allowed based on a failure of the multi-modal synchronization. The method can further include generating, based on the first information and the second information, third information to configure a PDU discard behavior. The method can further include outputting the third information for transmission to a user equipment (UE).

A communication method is performed by a non-Access Point Station Multi-Link Device (non-AP STA MLD). The method includes: determining a first message frame, wherein the first message frame includes identification information for indicating an operation mode to be maintained, and the identification information corresponds to link information for identifying a link to which the operation mode is applicable; and sending the first message frame.

A wireless transmit / receive unit (WTRU) may receive predictive buffer status report (BSR) configuration information. The predictive BSR configuration information may include information indicating an associated time frame and at least one triggering condition. The WTRU may predict an amount of uplink (UL) traffic corresponding to the associated time frame. The WTRU may predict BSR configuration information. The WTRU may determine that at least one triggering conditions has been met. The WTRU may send a predictive BSR, the predictive BSR comprising an indication of the predicted amount of UL traffic.

The invention relates to the technical field of telecommunication, in particular to a dual-mode communication link transmission method based on dynamic channel perception, which comprises the following steps of: firstly, detecting channel states of two links in parallel in a data transmission gap or according to a preset period to obtain a signal-to-noise ratio and a channel load rate; determining a service quality grade according to a service type of a transmission data packet, selecting an adaptive signal-to-noise ratio and a channel load rate weight coefficient, and further calculating a comprehensive quality score of the two links; dynamically calculating a power line link switching threshold based on a network average time delay requirement or a power line link historical quality stability parameter; comparing the power line link score with a threshold value and a wireless link score, and determining whether to switch or not; and finally, synchronizing the data transmission serial number and the protocol context through signaling to realize seamless switching. According to the invention, the real-time performance of channel sensing and the accuracy of quality evaluation are improved, the reasonable switching decision and the coherent data transmission are ensured, and the reliability of a communication system is enhanced.

A communication method and a related device relate to the technical field of communication. The method comprises the following steps: the terminal equipment determines whether first data in a logic channel group has delay data or not, and further dynamically associates a target threshold value based on whether the delay data exists or not. The terminal equipment sends a resource request message, the resource request message is used for requesting to configure resources for the first data, and the resource request message comprises a target threshold value associated with the first data. The target threshold value is used for a receiver of the resource request message, such as a network device, to determine the sending opportunity of the DSR report. According to the method, the sending opportunity of the delay state report can be dynamically adjusted, and redundant signaling is reduced.

The invention discloses a reliable data transmission method for a narrowband satellite internet of things. The method comprises the following steps: defining a reverse data frame format; link state monitoring and quantification: continuously or periodically monitoring a quality index representing a link of the satellite Internet of Things terminal; continuously or periodically monitoring the uplink state of the satellite Internet of Things terminal by using a terminal link state evaluation module of the satellite master station baseband system; link state grading: dividing the current link state into a plurality of predefined state grades; identifying the transmission rate of the satellite Internet of Things terminal; constructing a self-adaptive confirmation window strategy: a sending end of the satellite Internet of Things terminal uses a confirmation window with a dynamic window size; the number of data packets confirmed by the transmitted ACK / NACK message, namely the window size, is dynamically adjusted according to the currently sensed link state and the identified terminal transmission rate; the performance of the whole system is improved.

The application discloses a 5G network slice access control method based on service type, which comprises the following steps: 1) in a 5G network supporting multiple network slices, the type of a newly arrived service is judged, and the service is divided into a time delay sensitive service and a non-time delay sensitive service; if the service is the time delay sensitive service, step 2) is executed; if the service is the non-time delay sensitive service, step 3) is executed; 2) a bearing slice of the time delay sensitive service is selected; 3) a bearing slice of the non-time delay sensitive service is selected; 4) the next service needing transmission is waited for, and step 1) is executed. The application can minimize the time delay of the time delay sensitive service, realize the relative fairness of the time delay of various services, minimize the transmission waiting time of the non-time delay sensitive service, and improve user perception.

The invention belongs to the technical field of intelligent electric power internet of things, and particularly relates to an unmanned aerial vehicle heterogeneous terminal self-adaptive security access method for electric power edge computing, which comprises the following specific steps: terminal registration and credible anchoring: binding a hardware-level identity, collecting a multi-mode fingerprint and constructing a security behavior portrait; self-adaptive access authentication is carried out; dynamic behavior monitoring and baseline adjustment: establishing an identity authentication and management system, synchronously carrying out multi-dimensional real-time monitoring, carrying out linkage treatment on abnormal behaviors, and adopting a baseline evolution mechanism to avoid misjudgment; the multi-dimensional real-time monitoring comprises terminal service trend monitoring and flow situation analysis; and performing safe closed loop and cooperative response. According to the invention, unified risk detection of the unmanned aerial vehicle can be realized, real-time monitoring of vulnerabilities, access authority and operation states of the unmanned aerial vehicle is carried out, a dynamic behavior monitoring model is constructed in combination with unmanned aerial vehicle terminal information, business data, environment data and the like, and real-time monitoring and early warning of the unmanned aerial vehicle terminal are realized.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

The invention relates to the technical field of edge computing, in particular to an operator edge computing node optimization method and system, and the method comprises the steps: obtaining local load features and cross-node association features of edge nodes in real time, and constructing a dynamic load vector and an association vector respectively; based on a preset time sequence prediction model, taking the load vector and the association vector as input, and obtaining a predicted load trend in a future preset time period; generating an optimal service migration strategy through a preset reinforcement learning model according to the predicted load trend in combination with the dynamic load vector, the association vector and the task attribute; wherein the migration strategy comprises a migration target node and a migration resource allocation scheme; wherein the task attributes comprise task priorities; and on the basis of the migration strategy, a resource pre-allocation scheme of the target node is generated in combination with a preset resource constraint condition, and the method has the advantages of improving service response, resource utilization rate, task success rate and the like in a high dynamic scene.

One or more computer-readable media (CRM) storing instructions for monitoring quality of-service (QoS) flows in a wireless cellular network are disclosed. The instructions, upon execution by one or more processors, cause the one or more processors to: obtain a signal in the wireless cellular network; determine, based on the signal, a QoS flow event for a session management function (SMF); determine performance measurements related to the QoS flow event; and modify at least one of the QoS flows in the wireless cellular network based on the performance measurements. Example QoS flow events include a request to create or modify a QoS flow, a successfully created or modified Qos flow, a QoS flow that failed to be created or modified, etc.

The invention relates to the technical field of network slice resource allocation, in particular to a 6G intelligent load balancing and fault self-healing method based on AI and network slices, which comprises the step of building an AI decision-making layer model architecture, a self-adaptive optimization mechanism, a resource dynamic scheduling mechanism and a strategy execution guarantee architecture. According to the method, the network load trend is predicted in real time through the AI technology, the resource allocation between the slices is dynamically adjusted, and the problem of low resource utilization rate caused by static allocation and periodic adjustment is solved. Meanwhile, an automatic fault detection and recovery mechanism is designed, when a node fault is detected, standby resource takeover can be quickly triggered, session continuity can be kept, the service interruption time is remarkably shortened, and the requirements of a 6G network for high reliability and low time delay are met.

The invention discloses a multi-target computing power unloading system oriented to an unmanned aerial vehicle cooperative cluster, and aims to realize comprehensive optimization of task scheduling efficiency, energy balance and unloading intelligence on the premise of satisfying service real-time performance. The system firstly senses the operation state of each unmanned aerial vehicle in a cluster, extracts multi-dimensional information such as task data volume, residual electric quantity, local computing power and adjacent cooperative node resources, constructs a system state space, and defines action spaces such as local processing, edge unloading and cooperative unloading. And then, designing a multi-target fusion reward function, carrying out weighted integration on key indexes such as task time delay, electric quantity balance and node residual energy, dynamically adjusting reward feedback in combination with service delay constraint, and guiding a reinforcement learning agent to converge to a better unloading strategy.

The invention relates to the technical field of terminal monitoring, in particular to an intelligent security terminal collaborative monitoring system with edge computing capability. Comprising a multi-mode sensing unit; the edge calculation processing unit is used for distributing CPU and FPGA operation tasks by using a dynamic characteristic distillation technology and a hardware load feedback technology on the basis of multi-mode perception data; a distributed cooperative communication unit; and an intelligent decision-making unit. Visual, voiceprint and environmental physical signals of a security area are collected through the multi-modal sensing unit, unified time stamps and spatial coordinate association are added for multi-dimensional signals by means of the data synchronization module, and cooperation of the feature distillation module and the model optimization module in the edge calculation processing unit is combined. According to the method, multi-modal features are mapped to a unified feature space, high-value features are screened, and redundant convolution kernels are identified and removed, so that effective cooperative processing of multi-modal security and protection data can be realized, and the processing efficiency of edge-side sensing data is improved.

The invention relates to the technical field of construction intelligent monitoring, in particular to a building construction remote intelligent monitoring system based on 5G telephone communication and edge computing. Comprising an on-site sensing unit; the edge processing unit is used for realizing accurate integration of heterogeneous data through an improved self-adaptive weighted fusion algorithm and generating a real-time control strategy in combination with a working condition dynamic model; the 5G communication unit is combined with predictive slice scheduling and a multi-mode anti-interference protocol; and a remote monitoring unit. The industrial-grade sensor array is adopted to collect related data, a monitoring data stream with a timestamp and marked with an abnormal identifier is generated, meanwhile, an edge processing unit complements abnormal data through a data preprocessing module, heterogeneous data integration is achieved through an improved self-adaptive weighted fusion algorithm, and the data processing efficiency is improved. According to the method, the problems that the data processing architecture and the construction multi-source data characteristics are insufficient in adaptation and data validity tracing is missing are effectively solved, and effective tracing and accurate integration of the construction scene multi-dimensional monitoring data are achieved.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.