1455 results about "Cellular network" patented technology

A wireless communication method and system enabling dynamic multi-path data transmission and reception across heterogeneous radio access networks. The method includes selecting between sequential, concurrent, or redundant multipath transmission strategies based on real-time measurements of link quality parameters, including signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), delay, and spectrum availability. The system further enables adaptive transmission power control, modulation scheme selection, and carrier configuration based on physical layer feedback, minimizing interference and optimizing spectral efficiency. Peer-to-peer communication between terminals is supported without base station mediation, reducing RAN load. Multi-base station connectivity is enabled, allowing terminals to simultaneously transmit through multiple nodes and aggregate at the core network. The architecture supports real-time topology control, dynamic link adaptation, and transmission parameter adjustment based on physical-layer constraints.

Disclosed are methods, apparatuses, and systems for using Artificial Intelligence (AI) and Machine Learning (ML) in cellular networks. In one aspect, a method is performed by a first node. The method includes sending, to a second node, a first message that indicates a request to update or reconfigure a functionality in the first node related to an ML-model or another functionality in which the ML-model is a part. The method further includes receiving, from the second node, a second message responsive to the first message, and performing an update of the functionality related to the ML-model based on the second message.

The invention provides a cellular network flow space-time prediction method and system based on a hybrid neural network, and belongs to the technical field of intelligent communication. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a feature fusion layer and an output layer. The data embedding layer maps a historical traffic sequence, cross-domain external data and metadata into high-dimensional features; the space-time coding layer is used for respectively fusing one-dimensional causal convolution and a Mama neural network to extract multi-scale time features and densely connecting convolution and a multi-head attention mechanism to capture multi-scale space features through time and space modeling branches; the feature fusion layer realizes adaptive weighted fusion of spatial-temporal features, cross-domain features and metadata features by using a gating fusion mechanism; and the output layer performs linear transformation on the fusion features to generate a final prediction result. According to the method, the spatial-temporal dynamic capture of the service traffic is accurate, the prediction curve is highly fit with the true value, and the accurate prediction of the multi-service traffic of the cellular network is realized.

Techniques, methods, and solutions for supporting accurate and resilient positioning, navigation, and timing (PNT) services. In some examples, a cellular PNT (Cell-PNT) network may provide end-to-end monitoring and control of PNT capabilities. For example, a server may coordinate resource allocation between devices. The server may receive a service request and requested performance indicators according to a service level agreement. The server may additionally receive determined performance indicators associated with the service. Based on differences between determined performance indicators and requested performance indicators, the server may determine how to adjust resources to satisfy the requested performance indicators. Indication of the adjusted resources may be transmitted to a device associated with the determined performance indicators. The device may adjust data indications and signals based on the adjusted resources.

Techniques for cluster failure management in telecommunications systems are provided. In one example, a cellular network includes: a first radio unit (RU) that supports a first cell of the network, a second RU that supports a second cell, and a server system in communication with both RUs. The server system comprises a first server and a second server. A first pod acting as a distributed unit for the first RU is active on the first server and instantiated on the second server. A second pod acting as a distributed unit for the second RU is instantiated on the first server and active on the second server. A control plane executing on the first server manages execution of both pods, in response to determining that a pod is no longer active on a server, activates the pod on the other server.

The invention relates to the technical field of Internet content distribution, and discloses a distributed edge cache acceleration method based on PCDN, comprising the following steps: step I: terminal node access and resource initialization, which includes node registration and authentication, resource information reporting and node classification and label management; step II, hierarchical caching strategy and content storage, wherein the hierarchical caching strategy and content storage comprises content popularity grading, cache writing strategy and cache replacement and elimination; and step III, dynamic content scheduling and distribution. According to the distributed edge cache acceleration method and device based on the PCDN, the content popularity and regional distribution are pre-judged in advance through a deep learning model, and bandwidths and resources of different networks are integrated through a network fusion technology, so that the purposes of reducing the number of times of source returning requests, breaking through the transmission limitation of a traditional single network (for example, only depending on a cellular network) and improving the transmission efficiency can be achieved. The effects of node contribution traceability and automatic execution excitation are achieved.

A method for streaming dynamically changing user interface data from a server to a client, including resizing user interface data in accordance with a client display, repeatedly transmitting in the form of IP packets, the user interface data in a lossy compression format, for each transmission, if an acknowledgement is received that all packets have been received, then increase the number of IP packets in the next transmission, and if the increased number of IP packets is sufficient to transmit the user interface data in a lossless compression format, then retransmit previously transmitted user interface data in the lossless compression format, including partitioning previously transmitted user interface data into display strips, and for each strip, if the user interface data is unchanged, transmit the strip in the lossless compression format, and if the user interface data has changed, return to the repeatedly transmitting for the changed user interface data.

Technologies for providing energy efficiency technology in extreme mMIMO systems in a cellular network cellular network (e.g., 5G wireless network, 6G wireless network) are described. The method collects data representing conditions for potential energy saving (ES) modes, comprising morphology data and traffic pattern data. The method determines, using the collected data, one or more energy saving (ES) modes for one or more components of a cellular network in at least one of a time (T) domain, a frequency (F) domain, or a space (S) domain, wherein the one or more ES modes cause at least one adjustment to the one or more components in the at least one of the time (T) domain, the frequency (F) domain, or the space (S) domain.

A device is configured to perform positioning sessions and radio frequency (RF) sensing sessions in a cellular network. The positioning sessions may be previously defined positioning sessions, in which a device uses reference signals (RSs) to generate positioning measurements. RF sensing sessions may be defined sessions using the existing infrastructure for positioning sessions in order to generate RF sensing measurements. With the device to report both the positioning measurements and the RF sensing measurements, the device may adjust a previously defined measurement report for positioning measurements to also include RF sensing measurements, or the device may generate newly defined measurement reports to include the RF sensing measurements. The device may also prioritize reporting the positioning measurements and the RF sensing measurements, which may be based on a latency requirement of the different measurements, a quality of the measurements, or a use case of the device.

The invention discloses an Internet of Things equipment automatic identification method and device based on a cellular network environment and a storage medium, and the method comprises the following steps: S1, collecting IP flow information of Internet of Things equipment, exporting record data, and storing the record data to a flow data storage module; s2, extracting equipment traffic features, generating feature vectors, and storing the feature vectors in a feature database; s3, training an equipment identification model by using an improved structure sparse variational automatic encoder in combination with a space-time diagram convolutional network, and extracting an equipment traffic mode and behavior topological characteristics; s4, optimizing model training by adopting a category balance loss function; s5, collecting real-time flow data of to-be-identified equipment, converting the real-time flow data into feature vectors, inputting the feature vectors into the identification model, and calculating a matching result through cosine similarity; and S6, setting a similarity threshold value, outputting the type and the model of the equipment, or marking and storing unknown equipment. According to the method, the identification accuracy and the real-time performance are improved, and the identification capability of the shared IP equipment and the small sample equipment is enhanced.

Systems and methods for improving cellular handover performance are provided. A user individual offset parameter (UIO) is provided which may modify a measured signal strength parameter for a particular user equipment, improving handover performance. The UIO may be calculated and optimized such that one or more key performance indicators including signal to interference and noise ratio, handover success rate, and signaling overhead are maximized. A base station may compute or retrieve the value of a UIO for a particular user equipment, and adjust a measured signal strength and quality metric using the UIO. The UIO may be calculated either by the user equipment, the base station, or the core network.

The invention discloses a collision sensing intelligent guardrail system and method based on Beidou positioning, and the method comprises the following steps: S1, collecting sensor data along a guardrail, and carrying out the preprocessing; s2, transmitting the preprocessed sensor data to an edge server, and forwarding the preprocessed sensor data to a cloud server through a cellular network; s3, constructing a gated convolutional network based on two channels, and inputting the state vector sequence into the gated convolutional network; s4, performing multi-layer nested coding and decoding operation on the collision feature vector, constructing a collision judgment function, and judging whether collision abnormity is triggered or not; s5, generating a collision early warning data packet when the collision level label is in an activated state; and S6, transmitting the collision alarm data packet to a road traffic command center for road maintenance personnel to quickly respond and deal with the collision alarm data packet. According to the invention, high-precision real-time perception and intelligent early warning of the collision state of the road guardrail are realized, and the automation level and response efficiency of traffic safety monitoring are obviously improved.

The invention provides a communication method, a communication device, electronic equipment and a readable storage medium. The electronic equipment establishes satellite communication connection with a satellite; the electronic device receives a cellular signal, the cellular signal comprising a first cellular signal type; the electronic equipment judges whether the first cellular signal type is the same as a second cellular signal type in the electronic equipment or not; if the first cellular signal type is the same as a second cellular signal type in the electronic equipment, the electronic equipment prompts a user whether to switch to a first cellular network; and the electronic equipment receives and responds to the first operation of the user, disconnects the satellite communication connection with the satellite, and switches to the first cellular network. After the communication connection with the satellite is established, the signal type of the received cellular signal is detected, and a user is prompted whether to switch to the cellular network or not.

According to an aspect, there is provided a comput-er-implemented method for training a performance estimator model (22) for estimating the performance of a cellular network. The performance estimator model (22) comprises an encoder stage (24) comprisesing a plurality of encoders and a decoder stage (26) comprising a plurality of decoders. The network comprises a plurality of cells, and the cellular network has a plurality of configuration parameters and a configuration parameter of interest that are each configurable per cell. The method comprises: (I) obtaining (901) a training data set, the training data set comprising measurements of a plurality of performance parameters for the plurality of cells, wherein different values of the configuration parameters are being used among the plurality of cells, wherein the training data set further comprises respective values of the plurality of configuration parameters for the plurality of cells; (II) training (903) the encoders to encode the training data set into a respective representation for each cell, wherein each encoder receives, for a respective cell, the measurements of the plurality of performance parameters and corresponding values of the plurality of configuration parameters for that cell, and wherein a layer of each of the plurality of encoders are interconnected as a neural network representing the cellular network such that information on relationships between different pairs of cells in the cellular network is taken into account in the encoding; (ill) training (905) the decoders to decode a respective representation to determine a subset of the plurality of performance parameters for the respective cell associated with the configuration parameter of interest, wherein each decoder receives the respective representation and a current value of the configuration parameter of interest, wherein a layer of each of the plurality of decoders are interconnected as a neural network representing the cellular network such that information on relationships between different pairs of cells in the cellular network is taken into account in the decoding; (iv) determining (907) a value of a loss metric that is based on a difference between the input training data set and the output of the decoders; and (v) repeating (909) steps (II), (ill) and (iv) to retrain the encoders and decoders to obtain an improved value of the loss metric.

The embodiment of the invention provides a wireless network state dynamic optimization method and related equipment, and belongs to the technical field of wireless communication. According to the method, a Monte Carlo search tree is constructed by taking a current cellular network state as a root node, nodes of the search tree represent a cellular network state, and edges between the nodes represent base station control actions; the cellular network state comprises a signal intensity distribution characteristic and an interference risk characteristic; determining a node sequence from a root node to a leaf node in the search tree according to the value network of each node in the search tree; the value network is determined according to a reward function, and the reward function is used for calculating a coverage integrity reward and an interference suppression reward according to the cellular network state; and determining and executing a base station control decision according to the base station control action corresponding to each node contained in the node sequence. According to the invention, the state of the wireless network can be adaptively optimized, the network coverage rate is timely improved, and the adjacent cell interference is inhibited.

Some wireless devices communicate with multiple networks. For example, a user equipment (UE) may communicate with a cellular network and a wireless local area network (WLAN). In some cases, licensed use and unlicensed use may occur in the same portion of radio frequency (RF) spectrum or in overlapping portions of RF spectrum. In some examples of the techniques described herein, a UE may relay information between networks to coordinate spectrum usage in the same band. For instance, a cellular network may provide channel sharing information to a UE. The channel sharing information may include a request targeted to another network (e.g., a WLAN) to vacate one or more channels in the band, to reduce transmit signal power for one or more channels, or other channel sharing information. The UE may serve to relay the channel sharing information with the other network.

The invention relates to the technical field of wireless communication, and particularly discloses a spectrum sensing method and device for a cooperative frequency switching network and a communication system, and the method comprises the steps: generating a service network air interface frequency utilization situation table according to a network-level cooperative frequency control instruction transmitted by a guard network; a frequency spectrum sensing control instruction is generated according to the service network air interface frequency use situation table, the frequency spectrum sensing control instruction is diffused through a guard network, and the service terminal guard module can sense and monitor a service network air interface according to the frequency spectrum sensing control instruction and a to-be-sensed frequency set; and sensing and monitoring a service network air interface according to the to-be-sensed frequency set. The spectrum sensing method of the cooperative frequency switching network provided by the invention can assist in improving the anti-interference capability of the cellular network.

The invention relates to the technical field of Internet of Things, and discloses an Internet of Things terminal intelligent connection system and method based on a global operator network, and the method comprises the steps: when equipment is powered on or restarted, automatically executing network environment scanning, detecting a currently available cellular network, and collecting signal intensity, network coverage condition, delay and bandwidth; the equipment identifies an available operator network based on a global network configuration file, and comprehensively evaluates signal strength, network coverage, delay and bandwidth by adopting an optimization algorithm; the equipment continuously monitors a current connection state, collects network quality parameters, analyzes a network fluctuation trend in combination with a machine learning algorithm, and judges whether to switch to other operator networks when monitoring that the network quality is lower than a dynamic threshold value; and when detecting that the current network quality is continuously reduced, the signal is weak or the connection is lost, automatically triggering a network switching mechanism. The method has the advantage of improving the connection efficiency and stability of the Internet of Things terminal.

The invention relates to a cellular network switching method and device, equipment and a storage medium. The method comprises the following steps: detecting whether current equipment meets a switching condition or not; the switching condition is a condition for switching a wireless network used by the current equipment to a cellular network; if the switching condition is met, identifying the operating system type of the current equipment; according to the method, the Socket object is created in different modes for different operating systems, cellular network communication is carried out based on the Socket object, the mode is not limited by a system kernel, the network transmission effect is improved, meanwhile, the wide applicability is achieved, and through the mode, when the wireless network quality is poor and communication fails, the communication efficiency is improved. The cellular network is automatically switched for communication, so that the negative influence on data transmission when a single network goes wrong is greatly relieved, and the user experience is improved.

The invention relates to a multi-network convergence gateway system supporting a satellite, a cellular and a wide-narrow band ad hoc network, and the system comprises a satellite communication module which is used for carrying out the communication connection with a satellite, and receiving and transmitting a satellite signal; the cellular communication module is used for communicating with a cellular network base station; the wide-narrow band ad hoc network module is used for constructing an ad hoc network in a local area to realize communication between nodes; the data processing module is used for uniformly processing data from the satellite communication module, the cellular communication module and the wide-narrow band ad hoc network module; and the power management module is used for managing and controlling a power supply of the whole system. The method has the beneficial effects that fusion of various communication networks can be realized, communication requirements in different scenes are met, and the power consumption of the system is reduced.

Methods and systems for improving connectivity between a network node and a base station during data communication over a cellular network are described. The network node comprises a first plurality of WCMs. The method includes identifying, by the network node, a first base station connected to a second plurality of WCMs. The second plurality of WCMs include all or part of the first plurality of WCMs. After that, a WCM is selected from the second plurality of WCMs to maintain a connection with the first base station. The rest of the second plurality WCMs are disconnected from the first base station. The disconnected WCMs are reconnected in a manner so that each of the second plurality of WCMs becomes connected to a different base station or to the same base station over different frequency bands.

A gene regulation inference method based on causal diagram embedding and conditional cellular network relates to the technical field of gene regulation network prediction, and comprises the following steps: 1, obtaining a gene expression matrix from scRNA-seq, and generating a causal diagram; 2, generating a local feature embedding matrix of a gene by using a graph neural network model based on a causal graph and a known gene regulation and control network graph; 3, constructing a CCSN based on scRNA-seq, converting the CCSN into gene connectivity vectors, and integrating the gene connectivity vectors of all cells to form a CNDM as a global feature matrix; 4, integrating the local feature embedding matrix and the global feature matrix to form a final gene feature matrix; 5, screening a core gene from the gene feature matrix, and constructing a regulation edge matrix; and 6, inputting the regulatory edge matrix into a gene link prediction module to realize inference of the gene regulatory network. By applying the method, the causal relationship and the cell specificity can be integrated, the core gene is effectively screened, the feature fusion is optimized, and the accuracy and the biological interpretation of network inference are improved.

In one embodiment, a device receives a request from a client to remotely access an endpoint in a local network. The device instantiates a network slice having a remote access function in a cellular network. The device causes the endpoint to communicate a particular type of traffic via the network slice and the remote access function. The device configures a virtual private network tunnel between the client and the remote access function. The client and endpoint communicate with one another via a connection that comprises the network slice and the virtual private network tunnel.

This invention belongs to the field of wireless communications technology and discloses a large-scale multiple access method based on adaptive matching pursuit. First, some active users in a large-scale user terminal send spread-spectrum signals to a base station to perform uplink access transmission. Next, the base station receives the uplink signals of multiple users and models them as block-sparse vectors, using these vectors to reconstruct a new uplink receive signal equation. The base station then uses an adaptive matching pursuit algorithm based on the block-sparse model to detect active users and reconstruct the user-sent data. This invention is suitable for scenarios where large-scale terminals have sporadic burst uplink transmissions. It can achieve unauthorized random access for large-scale terminals while reducing user terminal overhead, thereby improving the uplink throughput of cellular networks.

A user equipment (UE) includes a first subscriber identification module (SIM) and a second SIM. The UE is configured to establish a first cellular network connection based on, at least, the first SIM and a second cellular network connection based on, at least, the second SIM. The UE detects a collision between i) multiple paging occasions or ii) multiple paging frames. The UE then initiates a procedure for updating a paging occasion for the first cellular network connection. The UE monitors for a page over the first cellular network connection during the paging occasion.

Causal graph generation The invention provides a method carried out by a processing device, where, in a first phase, a plurality of candidate causal graphs is generated based on training data sets, each of the candidate causal graphs providing a causal relationship relating a corresponding first network related parameter of a cellular network to a second network related parameter of the cellular network. In a second phase, based on actual data sets of the cellular network, a first actual value of the first network related parameter and a second actual value of the second network related parameter is determined, and in the second phase, a predicted second value of the second network related parameter is determined for each of the candidate causal graphs based on the first actual value and the plurality of candidate causal graphs generated in the first phase. The predicted second values determined with each of the candidate causal graphs, is compared to the second actual value of the second network related parameter, in order to determine, for each candidate causal graph, a difference between the actual and the predicted second value, and at least one final causal graph is determined based on the comparing, and is provided for further use.

Various arrangements for decreasing cellular network dead spots using microcells are detailed. A geographic map indicative of cellular network performance may be created. Using the geographic map, an area that has a cellular network dead spot can be identified. A subscriber database can be used to identify a subscriber address that is in a location to extend the cellular network within the dead spot. Based on analyzing the subscriber database to identify the subscriber address that is in the location to extend the cellular network, a microcell can be caused to be deployed at the subscriber address. Cellular services can then be provided using the microcell to various pieces of user equipment.

A user equipment (UE) may attempt to access an edge data network. The UE generates a first credential based on a second credential, the second credential generated for a procedure between the UE and a cellular network, generating an identifier corresponding to the first credential, and generates a multi-access edge computing (MEC) authorization parameter. The UE then transmits an application registration request message to a server associated with an edge data network, the application registration request message including an indication of the first credential, the identifier corresponding to the first credential and the first authorization parameter. The UE then receives an authentication accept message or an authentication reject message from the server associated with the edge data network.

The invention relates to the field of communication, in particular to a method for delivering cache content of a mobile edge computing network for energy consumption optimization. For a cache-based MEC cellular network, the method reasonably utilizes user movement information, and calculates corresponding path fading, channel gain and shadow fading by utilizing future position information of a user. Determining the minimum transmission power by using the calculated channel gain and path fading, then setting a time slot, carrying out content delivery decision on data packets requested by different users, and allocating wireless communication resources; and with minimization of the total emission energy consumption of the base station as a target, modeling a cache content delivery problem into a mixed integer programming problem to carry out optimization solution so as to obtain an energy consumption minimization decision for the base station to deliver all data packets.

A method for determining energy-savings states of cellular network components comprises retrieving run-time performance metric data for cells in a predetermined area corresponding to an event location; feeding the run-time performance metric data to a trained machine-learning (ML) model; determining, using the trained ML model and the run-time performance metric data, predicted cell utilization levels over a predicted time period; comparing one or more predicted cell utilization levels for each cell to a respective threshold; and building an inclusion list that identifies each cell for which at least one of the respective predicted cell utilization level(s) is / are lower than or equal to the respective threshold(s) and a respective one or more predicted time increments in which the at least one of the respective predicted cell utilization level(s) of a respective cell is / are lower than or equal to the respective threshold(s).