1406 results about "Telecom network" patented technology

A method performed by a terminal in a wireless communication system is provided. The method includes receiving, from a base station, a radio resource control (RRC) message including layer 1 (L1) / layer 2 (L2) triggered mobility (LTM) configuration information, the LTM configuration information including at least one LTM candidate configuration for at least one candidate cell, transmitting, to the base station, a report for an L1 measurement on the at least one candidate cell, receiving, from the base station via a medium access control (MAC) control element (CE), an LTM cell switch command based on the report, identifying whether a value of a timing advance command (TAC) in the MAC CE is set to a specific value, and in case that the value of the TAC is not set to the specific value, processing the TAC and performing a random access channel (RACH)-less LTM cell switch for a target cell identified by the MAC CE.

The present disclosure describes a method performed by a user equipment (UE) for reporting the performance of at least one machine-learning (ML) model to a cellular telecommunications network. Some exemplary embodiments include the UE utilizing at least one ML model, generating one or more reports or reportable information of a performance of the at least one ML model, and reporting the one or more reports or reportable information to a network. Associated devices and systems are also provided herein.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Disclosed is a method of performing L1 / L2 triggered mobility (LTM) in a user equipment (UE), communicatively coupled to a telecommunication network, wherein if the UE receives RRCReconfiguration containing LTM target cell information, RRCReconfigurationComplete is sent to a source cell, and upon receipt of a MAC control element (CE) from the source cell, the target cell information is applied, a handover is performed, and a further RRCReconfigurationComplete is generated and sent to the target cell upon completion of the handover.

System and method for implementing advanced sleep modes in an O-RAN, the method includes collecting measurement data for training an AI / ML model; based on the collected measurement data, training an A / ML model and deploying the AI / ML model in the nRT-RIC; activating the trained AI / ML model in the nRT-RIC; monitoring energy optimization data for AI / ML model inference from an open radio unit (O-RU) via an E2 node; activating at least one advanced sleep mode (ASM) in the nRT-RIC; based on the activation of the ASM, collecting data to temporarily deactivate O-RU components; based on the activated AI / ML model and the ASM, evaluating, the collected data to temporarily deactivate O-RU components; based on the evaluating, requesting to initiate the ASM to the O-RU; based on the ASM initiation request, implementing the ASM, by the E2 node and the O-RU.

A system for performing simulations on a digital twin (1) of a telecommunication network is configured to determine a assessment accuracy with respect to one or more performance indicators and / or determine a maximum assessment duration, select a simulation model (13-15,23-25,33-35,43-45) for each of a plurality of components (3-8) of the digital twin based on at least one of the minimum assessment accuracy and the maximum assessment duration, assemble the selected simulation models (13,14,23,33,43) into the digital twin, and perform the simulations on the digital twin.

This disclosure provides a method of operating a management node in a cellular telecommunications network, the cellular telecommunications network including a first base station and a User Equipment (UE), the management node storing a first set of tracking area codes, wherein the first base station is configured to transmit a first tracking area code and the UE is configured to send a tracking area update request message to the first base station identifying the first tracking area code, the method including receiving a request message from the first base station, the request including the first tracking area code identified in the tracking area update request message; comparing the received first tracking area code to the first set of tracking area codes; and sending a response message to the first base station, the response message accepting or rejecting the tracking area request based on the comparison.

A computer-implemented method for managing a telecommunications network based on a large language model is disclosed, the large language model being fine-tuned with technical documentation for the telecommunications network and historical data originating from the telecommunications network. The method comprises receiving one or more key-performance indicators and determining whether the one or more key-performance indicators indicate an anomaly. The method further comprises, in response to detecting an anomaly, determining contextual data associated with the real-time data and feeding a prompt to identify a root cause to the large language model, the prompt containing the contextual data and a task description for root cause analysis for the anomaly. The method also comprises performing one or more responses to address the root cause.

The invention discloses a fraud phone recognition method and system based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a plurality of fraud numbers from historical fraud data; constructing a multi-modal fraud data graph, taking each piece of multi-modal data of the fraud number as a node, and constructing a connection edge between the nodes according to an association relationship between every two pieces of multi-modal data; constructing and finely adjusting a large language model; multi-modal data of a to-be-recognized fraud number is obtained, a plurality of nodes are selected from the multi-modal fraud data graph, then the multi-modal data of the to-be-recognized fraud number and the multi-modal data of the selected nodes form cue words, the cue words are input into the large language model, and whether the obtained to-be-recognized fraud number relates to fraud or not and reason analysis for judging that the to-be-recognized fraud number is fraud are output. The invention relates to the field of communication, can improve the fraud recognition accuracy and system adaptability through deep fusion and calculation of multi-mode data such as call audio, short message text and call ticket data, and provides powerful technical support for fighting against novel telecommunication network fraud.

Conditions are identified in a telecommunications network. Data is collected that comprises live data indicative of conditions in the telecommunications network. Skills are configured to monitor key performance indicators (KPIs) and detect anomalies in the telecommunications network and localizations of the anomalies based on the KPIs. Based on metadata indicative of the KPIs and the localization, a data store is searched to identify associated incident reports and documentation related to design and requirements knowledge of the telecommunications network or portions thereof. Based on the skills, identified incident reports, and troubleshooting knowledge base, a prompt is generated for input to a large language model (LLM).

A system for classifying customer communications based on customer intent receives a communication from a customer during a communication session. The communication can include natural language. The system can create an embedding vector based on the natural language of the communication. The first embedding vector can include a numerical representation of natural language extracted from the communication. The system can compare the embedding vector to multiple embedding vectors associated with intent classifications corresponding to a prediction of a type of assistance available for customers. The multiple embedding vectors can be created based on a model that is configured to identify customer intent. The system can identify which intent classification of the multiple intent classifications is associated with the embedding vector based on the comparison. The system can redirect the communication session to a sub-unit of the telecommunications network service provider based on the identified intent classification.

Embodiments are directed towards systems and methods for managing user experiences during cellular telecommunication outages within a wireless telecommunication network, such as a wireless 5G network. Example embodiments include systems and methods managing user experiences during a cellular telecommunication network outage utilizing a backup data center. For example, in response to the distributed unit (DU) detecting there exists the failure in communication between the DU and the corresponding primary centralized unit control plane (CU-CP), the system causes the DU to switch from using the corresponding primary CU-CP to using the secondary CU-CP of a secondary CU hosted on the backup cloud-native virtualized compute instance based on utilizing a secondary ID pre-configured on the DU.

A method is provided for identifying and / or referring to user equipment route selection policy information when operating a user equipment connected to a telecommunications network. The telecommunications network comprises a core network comprising a policy and charging function. The policy and charging function is configured to transmit user equipment route selection policy information to the user equipment. A piece of user equipment route selection policy information or a part thereof is able to be identified via a user equipment route selection policy identifier. The method comprises: the piece of user equipment route selection policy information or the part thereof is assigned to the user equipment route selection policy identifier; and a further or operatively used piece of user equipment route selection policy information or a part thereof refers to the user equipment route selection policy identifier.

The disclosure relates to a 5th generation (5G) communication system or a 6th generation (6G) communication system for supporting higher data rates beyond a 4th generation (4G) communication system, such as long term evolution (LTE). A method of managing execution failure of L1 / L2 triggered mobility (LTM), in a user equipment (UE), communicatively coupled to a telecommunication network is provided. The method includes if an initial LTM execution attempt fails, performing, by the UE, cell selection, and if the selected cell is an LTM candidate cell and if the UE has been configured by the telecommunication network to attempt LTM after an LTM execution failure, the attempting, by UE, a further LTM execution.

A method for implementing a carrier and / or cell switch off / on optimization in an O-RAN based on an A1 policy, the method includes: deploying, by the NRT-RIC framework, a AI / ML model to the nRT-RIC; providing, by the rApp, an A1 policy to prepare and execute the cell and / or carrier switch off / on optimization through the NRT-RIC framework to the nRT-RIC; collecting, by the nRT-RIC, data to perform the cell and / or carrier switch off / on via the E2-interface from the O-RU via the E2 node; evaluating, by nRT-RIC, the collected data; generating, by the nRT-RIC, an E2 message and send, by the nRT-RIC, the E2 message to the O-RU via the E2 node; implementing, by the E2 node and the O-RU, the cell and / or carrier switch off / on optimization within the O-RAN; receiving, by the rApp, an A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC.

Systems and methods for implementing writing configuration changes in a non-real-time radio access network intelligence controller (NRT-RIC) architecture including a Service Management and Orchestration (SMO) framework and a NRT-RIC framework in an open radio access network (O-RAN) are provided. The method includes: receiving at least one request to write a configuration change to one or more O-RAN Operations and Maintenance (OAM) related functions; authorizing the at least one request to write a configuration change to the one or more O-RAN OAM-related functions from the rApp; validating information provided by the at least one request to write a configuration change to one or more O-RAN OAM-related functions; creating a job for writing the configuration changes based on the information provided by the at least one request to write a configuration change to one or more O-RAN OAM-related functions; and sending a job identifier comprising information about the job to the rApp.

A method of distributed training of a machine learning model is provided. The method includes inputting a first set of observations and a first set of actions for a primary agent to generate a first Q function for the primary agent, inputting a second set of observations and a second set of actions for a set of secondary agents to generate a set of Q functions for the set of secondary agents, and generating a Qtot function from the first Q function and the set of Q functions by a mixing network for the primary agent, the Qtot function to generate actions or predictions to configure a first node to operate in a telecommunication network.

Techniques for optimizing messages in a telecommunications network are discussed herein. More particularly, an SMSF can be configured to refrain from sending deregistration and / or unsubscribe messages to a UDM in response to a handover from 5G to 4G, which can thereby reduce signaling in the network. Further, because of the suppressed messages, the network maintains SMSF session information, which indicates the delivery location for a SMS message. However, when a SMSC receives a SMS addressed to the UE, the HSS may query a UDR for SMSF session information, SMSF registration status, and / or AMF registration status. The HSS may determine a location for the SMS based on the active location of the UE, as determined by the AMF registration status. Thus, the techniques discussed herein represent network optimizations while allowing SMS messages to be correctly routed in a network.

The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.

Embodiments relate to analyzing network packets in a telecommunication networks using machine learning models. The network packets are correlated and then labeled to indicate successes or failures in a subtask of communication flow. Features are extracted based on the labels and correlated network packets. The extracted features are applied to a machine learning model to predict or infer success or failure of the entire communication flow. The result from the machine learning model may again be applied to subsequent machine learning models to predict root cause of a failure or to predict or infer the type of success. In this way, more accurate diagnosis of network issues in the telecommunication networks may be made in a more expedient manner.

A network assessment system for generating subjective quality ratings for Internet protocol multimedia subsystem (IMS) data sessions in real-time. The system measures real-time telecommunications network data during an IMS data session, processes the data using a trained model, and receives a subjective quality rating usable to modify the performance of a node in instances where the node does not typically have an associated subjective quality rating or in instances where the node is not associated with a standardized subjective quality rating.

The embodiments herein relate to a method performed by a UE (800) capable for sidelink communications, for congestion control in a wireless telecommunications network, wherein the UE is allocated one or more resource pools, the method comprising: performing (701) CBR / CR measurements on said at least one resource pool to generate CBR / CR value(s); wherein the measurements are performed over a sensing window which is either pre-configured or adapted based on the QoS of at least one data packet or at least one service, and selecting (702) a resource pool or at least one resource within the resource pool based on a CBR / value and QoS requirements of the data packet or the service for transmission.

Errors in network service provisioning are upstream causes to service disruptions interruptions, and failures experienced by a telecommunications network’s subscribers. These challenges are overcome by the disclosed systems for diagnosing and auditing service provisioning errors in telecommunications networks. By addressing discrepancies between Customer Facing Services (CFS) as selected by network subscribers (e.g., voice calling, messaging, roaming, unlimited or limited broadband access) and the current states of network elements in the telecommunications network, the system ensures accurate service delivery. The system performs real-time diagnosis by comparing expected network service configurations, derived from queried subscriber CFS, with the actual network states or configurations through a reverse mapping process. The system's auditing process reduces operational costs and obviates existing techniques of manual inspection.

Disclosed herein is a method performed by a Control Plane function 800, in a telecommunication network 1302 to manage a restart of a User Plane, UP, function in a user plane path for Protocol Data Unit, PDU, sessions that the CP function 800 is managing, the method comprises: receiving 900 a Packet Forwarding Control Protocol, PFCP, message from the UP function 802 comprising a report related to the UP-2 (804) has restarted; acknowledging 902 the report; and performing 904 an action based on an identity of the other UP-2 804.

The disclosure relates to a 5th generation (5G) communication system or a 6th generation (6G) communication system for supporting higher data rates beyond a 4th generation (4G) communication system, such as long term evolution (LTE). A method of managing execution failure of L1 / L2 triggered mobility (LTM), in a user equipment (UE), communicatively coupled to a telecommunication network is provided. The method includes if an initial LTM execution attempt fails, performing, by the UE, cell selection, and if the selected cell is an LTM candidate cell and if the UE has been configured by the telecommunication network to attempt LTM after an LTM execution failure, the attempting, by UE, a further LTM execution.

This disclosure describes systems, computer readable media, and methods for establishing a user call on a telecommunications network, the call including a user phone number, and establishing a bidirectional communication connection with the user. Audio data is received from the user and sent to a STT service. Text data is received from the STT service, the text data representing the audio data. The AI prompt can be sent to an AI model, from which a text response is received. The text response can be parsed into one or more response statements, which are sent to a TTS service. A stream of speech data can be received from the TTS services and converted to a format suitable for the telecommunications network. A target bitrate for the converted speech can be determined, and the converted speech can be sent to the user at the target bitrate.

Example embodiments of the present disclosure are related to provisioning network energy management in a telecommunication system. According to embodiments, an apparatus may include an energy management function (EMF) for a telecommunication network. The apparatus may be configured to execute instructions for implementing the EMF to: receive, from at least one network entity, one or more energy data associated with the at least one network entity; process the one or more energy data to produce a level of energy efficiency associated with the at least one network entity; and perform, based on the level of energy efficiency, one or more operations for managing energy usage of the at least one network entity.

A system for deploying network functions into target cloud environments in a cellular telecommunication network is cloud computing service provider agnostic. The system may use target environment agnostic GUIs to collect network function and IP address information for the deployment of the network function. The system populates a template for deploying the network function based on the network function and IP address information as well as the identity of the target environment. The system builds and deploys the network functions in the target environment based on the template.

A computer-implemented method for managing a telecommunications network based on a large language model is disclosed, the large language model being fine-tuned with technical documentation for the telecommunications network and historical data originating from the telecommunications network. The method comprises receiving one or more key-performance indicators and determining whether the one or more key-performance indicators indicate an anomaly. The method further comprises, in response to detecting an anomaly, determining contextual data associated with the real-time data and feeding a prompt to identify a root cause to the large language model, the prompt containing the contextual data and a task description for root cause analysis for the anomaly. The method also comprises performing one or more responses to address the root cause.

Method for implementing a dynamical antenna array reconfiguration through a C-Plane within an O-RAN, the method includes: receiving, by a distributed unit (O-DU) from a radio unit (O-RU), configuration capability information via management plane (M-Plane) messaging from the O-RU; receiving, by the O-DU from a higher-layer network function, a request to reconfigure an antenna array; based on the configuration capability information from the O-RU and based on the request to reconfigure the antenna array from the higher-layer network function, determining, by the O-DU, an antenna array configuration supported by the O-RU; and applying, by the O-DU, the supported antenna array configuration via C-Plane messaging or M-Plane messaging to the O-RU.

Methods and systems are described for AI and / or ML model selection in a telecommunications network. One example embodiment includes obtaining a first set of one or more criteria for selecting between at least two AI / ML models; performing at least one measurement procedure to obtain one or more measurements; and using the obtained one or more measurements and the first set of one or more criteria to select between the at least two AI / ML models. A measurement procedure can comprise e.g. CSI, radio link procedure (RLP), positioning measurement, measurement related to cell change procedure etc. Certain described embodiments enhance measurement performance of the measurement (e.g., CQI) and correspondingly improve the outcome / performance of the procedure (e.g., data scheduling) using the measurement. This in turn reduces the overall processing in the UE, frees up at least part of the memory resources and reduces the UE power consumption.