966 results about "Telecom network" patented technology

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

There is provided a computer-implemented method for processing a sequence of data items. The data items correspond to one or more features of a telecommunications network. The sequence of data items is segmented (102) into a plurality of segments. For each segment of the plurality of segments, the data items in the segment are analysed (104) using a machine learning model to identify a relationship between the data items in the segment and output a representation of the data items in the segment based on the identified relationship.

Systems and methods are provided for mitigating atmospheric ducting within a wireless telecommunication network. The disclosure includes determining the configuration of user equipment (UE) within the coverage area of a victim base station, which employs a primary cell operating with time domain duplexing (TDD) and a secondary cell operating with frequency domain duplexing (FDD). Subsequently, a scheduler associated with the victim base station directs uplink communications to be transmitted via the secondary cell, thereby isolating the primary cell during periods of interference.

Conditions are identified in a telecommunications network. Based on the condition, a data store is accessed to identify an associated executable plan for responding to the detected anomaly. The executable plan is generated by an AI agent based on a structured operator-readable document comprising operator-executable procedures. The executable plan comprises a series of operations that are executable by an execution component.

Described herein are methods and systems for determining a user equipment (UE) location of a UE, wherein a UE type of the UE is fixed wireless access. The methods and systems further allow or deny a connection of the UE to a telecommunications network based on geofencing logic applied to at least one of the UE location, the UE type, and a UE identifier of the UE. In some implementations, the methods and systems may allow or deny the connection regardless of the UE location, instead using the UE type and an allow list of UEs permitted to connect to a base station.

Conditions are identified in a telecommunications network based on data collected from the telecommunications network. The data comprises time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and raw error logs collected alongside the time series telemetry data for the telecommunications systems. Outputs from a time series insight generator and a sentiment analyzer are combined to generate an output report indicative of anomalous metrics in the telecommunications network.

A system for controlling the use of screens by a user, comprising a set of software modules each embedded in an apparatus of a set of apparatuses of said user, comprising screens, and a server connected to said apparatuses by a telecommunication network, wherein:said apparatuses are initially restricted to a first set of functions, and include means of determining behavioral information of said user and transmitting same to said server,said server is adapted for granting an additional function according to said behavioral information, and for transmitting, to the software module of a selected apparatus, a message to enable said user to access said additional function.

A method is disclosed for managing access in a telecommunications network by utilizing a resource classification layer. The method involves receiving a request at a resource classification layer from a sender to obtain an access token for a receiver service. The sender is associated with a user role that has specific permissions and access rights corresponding to a data sensitivity threshold. The request includes a data payload. A classification value for the data payload is assigned using one or more resource classification models, which are trained on a log of past data payloads. A data sensitivity score is generated by comparing the classification value to a scale of classification values. The method then indicates whether the access token can be granted to the sender by comparing the data sensitivity score against the data sensitivity threshold to verify authorization.

The system receives multiple alarms indicating the issue associated with the network and obtains multiple categories associated with the multiple alarms. The category indicates a component associated with the network. Based on the multiple categories, the system creates a correlation signature associated with the multiple alarms. The system obtains historical data including a historical correlation signature that is the same as the correlation signature, a cause associated with the historical correlation signature, and an indication of accuracy associated with the cause. The system determines whether the indication of accuracy satisfies a first criterion. Upon determining that the indication of accuracy satisfies the first criterion, the system makes a prediction that the cause associated with the multiple alarms indicating the issue is the same as the cause associated with the historical correlation signature.

The disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as long term evolution (LTE). Disclosed is a method of determining a User Equipment, UE, location wherein the UE is in communication with at least two base stations (gNB) of a telecommunication network, comprising the steps of: determining at least one of: a) Angle of Arrival, AoA, of a signal from the UE at each of the at least two gNBs; b) Angle of Departure, AoD, of a signal from each of the at least two gNBs; c) AoA of a signal from each of the at least two gNBs at the UE; and d) AoD of a signal from the UE at each of the at least two gNBs; and determining the UE position on the basis thereof.

A computer implemented method in a communications network for determining location information about an actual location of a drone comprises obtaining (302) a reported location of the drone at a first time point and obtaining (304) a measurement of radio conditions between the drone and a node in the telecommunications network, at the first time point. The method then comprises predicting (306) radio conditions at one or more locations related to the reported location of the drone, and determining 308 the location information about the actual location of the drone based on the measured radio conditions and the predicted radio conditions.

The present invention relates to various signaling methods associated with a multi-transmission reception point (TRP) system. A method of indicating transmission configuration indicator (TCI) state(s) of operation for a user equipment (UE) in a telecommunication network is proposed. The method comprises receiving a media access control-control element (MAC-CE) structure. The MAC-CE structure comprises a code-point mapped to a TCI state. The TCI state is at least one of joint downlink (DL) TCI state and uplink (UL) state and at least one of joint / DL TCI state, and UL state. A downlink control information (DCI) is received. The DCI comprises a TCI field indicating a code-point of the MAC-CE structure and a flag for selection of one of joint TCI state of operation for DL and UL and separate TCI states of operation one for DL and one for UL. The TCI state is activated, based on the flag and the TCI field. The UE is operated in the at least one TCI state. Further, a method of indicating a TCI state using a group common DCI is proposed. Furthermore, a method as transmitting a hybrid automatic repeat request (HARQ) from the group of UEs using a group common physical uplink control channel (PUCCH) is proposed. Furthermore, methods for handling collisions between difference channels and signals in a M-TRP system are proposed. In the event of a collision, a signal / channel is chosen based on preconfigured rules. Additionally, methods of activating and updating TCI states for different channels in a M-TRP system are proposed. Further, methods of associating a TCI state are proposed.

The invention discloses a communication network fault diagnosis method and related equipment based on multi-modal vector fusion, and the method comprises the steps: carrying out the vector structuring processing of historical heterogeneous telecommunication operation and maintenance data through a multi-modal vectorization engine, and obtaining a historical multi-modal vector set; performing vector fusion processing on the historical multi-modal vector set through a vector fusion center to obtain a historical multi-modal fusion vector; an alarm message queue in a telecommunication network is monitored through a diagnosis decision module, and if an unknown composite fault is detected from the alarm message queue, cosine similarity calculation is carried out on a current fault fusion vector corresponding to the unknown composite fault and a historical multi-mode fusion vector in a hierarchical storage module; and outputting a network fault diagnosis decision result. According to the method, the accuracy and comprehensiveness of network fault diagnosis can be improved, efficient data storage, retrieval and diagnosis decision are realized, the operation efficiency and diagnosis capability of the system are improved, and the method can be widely applied to the technical field of communication network operation and maintenance.

A method (100) of allocating discrete radio resources of an access node of a telecommunication network to wireless devices The method is performed by a scheduler node of the telecommunication network. The method comprises, sequentially, for each radio resource solving (S101) a lower-bound optimization problem, where is the number of wireless devices. The lower-bound optimization problem is dependent on a respective mean reward and a respective value, for each wireless device, to obtain an estimated optimal resource allocation. The respective parameters are updated, for each wireless device, by sampling from a probability function defined as a convex combination of the estimated optimal resource allocation and a fairness constraint. The method further comprises initiating (S103) allocation of the radio resource to the wireless device having the largest mean reward.