Abstract: The system obtains KPIs of a cell of a telecommunication network. The system preprocesses the multiple KPIs by reducing noise of the multiple KPIs and normalizing an amplitude of a KPI among the multiple KPIs. The system identifies a performance issue of the network by executing multiple ML models based on the KPIs, where a first ML model is configured to identify a sudden performance degradation, where a second ML model is configured to identify an event leading to a change in a performance trend, where a third ML model is configured to identify a gradual change in the performance trend, and where a fourth ML model is configured to identify whether the cell of the network is nearing a capacity limitation of the cell. The system provides a report based on the identified performance issue.

Abstract: In some implementations, an operational architecture receives a natural language input for a wireless communication network. In response, the operational architecture identifies a task in the wireless communication network based on the natural language input. Based on the natural language input, the operational architecture identifies a context to constrain a Large Language Model (LLM) to a domain of the wireless communication network. The operational architecture generates an LLM prompt that identifies the task and the context. The operational architecture submits the prompt to the LLM, and in response, receives steps to perform the task in the domain of the wireless communications network.

Abstract: Various embodiments comprise a wireless communication network configured to anonymize network Key Performance Indicators (KPIs) to train a machine learning model. In some examples, the wireless communication network comprises a network analytics system and a KPI store. The network analytics system retrieves the network KPIs generated by network KPI sources. The analytics system filters the network KPIs based on an intended function of the machine learning model. The analytics system identifies correlated ones of the filtered KPIs and groups the correlated ones of the filtered KPIs into KPI groups based on a network condition. For each KPI group, the analytics system sorts the filtered KPIs into KPI ranges. For each KPI range, the analytics system tokenizes the KPI range by converting the filtered KPIs that compose the KPI range into strings. The KPI store stores the tokenized KPIs in a KPI database accessible by the machine learning model for training.

Abstract: Systems, methods, and devices are disclosed herein for an application service that utilizes a large language model (LLM) to assist with performing management tasks associated with a wireless communications network. In an implementation, the application service receives natural language input indicative of a management task to complete with respect to the network. The application service identifies a context of the management task, which helps constrain the LLM to the appropriate domain(s). The application service generates a prompt that incorporates the task and the context and submits the prompt to the LLM. The application service receives from the LLM a reply that includes a plan that details a series of steps and associated resources of the domain for completing the management task. Upon receiving the LLM's reply, the application service performs the management task based on the provided plan.

Abstract: Systems, methods, and software are disclosed herein for localizing an atmospheric duct based on remote interference. In an implementation, a method of operating a computing device for localizing an atmospheric duct includes detecting cross-cell interference during an uplink transmission at a network cell, identifying a source of the cross-cell interference based on a RIM reference signal embedded in the cross-cell interference, and generating localization information of an atmospheric duct based on position information of the network cell relative to the source and a propagation delay of the cross-cell interference.

Abstract: A wireless communication device is disclosed that includes a non-transitory memory, a processor, and a local agent. The local agent transmits context information to an AI engine and receives and stores sign language data sets selected based on the context information from the AI engine in the non-transitory memory. The local agent receives content in need of language assistance. In response to a determination to process the content locally, the local agent converts the content into first sign language gestures based on the data sets. In response to a determination to process the content remotely, the local agent sends at least some of the content to the AI engine, receives keys from the AI engine, and determines second sign language gestures based on the keys and the data sets. The local agent sends the first or second sign language gestures to a display of the wireless communication device for presentation.

Abstract: In some examples, a wireless communication system comprises a user device and network elements. The user device generates user data. The user device wirelessly receives a satellite signal from a satellite. The user device determines satellite signal metrics for the satellite signal. The user device wirelessly transfers the user data and the satellite signal metrics to the network elements. The network elements determine known satellite metrics for the satellite. The network elements compare the satellite signal metrics to the known satellite metrics. The network elements to attest to the user data based on the comparison.

Abstract: A wireless communication network attests to a geographic location of a wireless user device. An access node receives signaling transferred by the wireless user device that indicates the wireless user device, the geographic location, satellite signal characteristics for the geographic location, and terrestrial signal characteristics for the geographic location. The network access node transfers the signaling to a location attestation node. The location attestation node determines known signal characteristics for the geographic location. The location attestation node compares the known signal characteristics to the satellite signal characteristics and the terrestrial signal characteristics. The location attestation node generates a location-attestation score based on the comparison. The location attestation node transfers information that indicates the wireless user device, the geographic location, and the location-attestation score. The location attestation node may comprise a distributed ledge node.

Abstract: The system obtains KPIs of a cell of a telecommunication network. The system preprocesses the multiple KPIs by reducing noise of the multiple KPIs and normalizing an amplitude of a KPI among the multiple KPIs. The system identifies a performance issue of the network by executing multiple ML models based on the KPIs, where a first ML model is configured to identify a sudden performance degradation, where a second ML model is configured to identify an event leading to a change in a performance trend, where a third ML model is configured to identify a gradual change in the performance trend, and where a fourth ML model is configured to identify whether the cell of the network is nearing a capacity limitation of the cell. The system provides a report based on the identified performance issue.

Abstract: Various embodiments comprise a wireless communication network configured to anonymize network Key Performance Indicators (KPIs) to train a machine learning model. In some examples, the wireless communication network comprises a network analytics system and a KPI store. The network analytics system retrieves the network KPIs generated by network KPI sources. The analytics system filters the network KPIs based on an intended function of the machine learning model. The analytics system identifies correlated ones of the filtered KPIs and groups the correlated ones of the filtered KPIs into KPI groups based on a network condition. For each KPI group, the analytics system sorts the filtered KPIs into KPI ranges. For each KPI range, the analytics system tokenizes the KPI range by converting the filtered KPIs that compose the KPI range into strings. The KPI store stores the tokenized KPIs in a KPI database accessible by the machine learning model for training.

Abstract: A wireless communication system attests to user data from a wireless user device. The wireless user device receives satellite signals from satellites and determines satellite signal metrics for the satellite signals. The wireless user device determines its geographic location based on the satellite signals. The wireless user device executes an operating system in trusted processing circuitry in response to device power-up. The wireless user device executes a network application in the trusted processing circuitry. The network application transfers the user data, geographic location, and signal metrics to network elements. The network elements determine known satellite metrics for the geographic location. The network elements compare the satellite signal metrics to the known satellite metrics and generate an attestation score for the user data based on the satellite comparison. The network elements store the user data, the geographic location, and the attestation score.

Abstract: A wireless access node wirelessly transfers an identifier and location to a communication satellite. The communication satellite transfers the identifier and location to a data communication system. The data communication system determines a node configuration for the wireless access node based on the identifier and location and transfers the node configuration for the wireless access node to the communication satellite. The communication satellite wirelessly transfers the node configuration to the wireless access node. The wireless access node wirelessly exchanges user data with user communication devices based on the node configuration. The wireless access node exchanges the user data with the data communication system based on the node configuration.

Abstract: The system obtains KPIs of a cell of a telecommunication network. The system preprocesses the multiple KPIs by reducing noise of the multiple KPIs and normalizing an amplitude of a KPI among the multiple KPIs. The system identifies a performance issue of the network by executing multiple ML models based on the KPIs, where a first ML model is configured to identify a sudden performance degradation, where a second ML model is configured to identify an event leading to a change in a performance trend, where a third ML model is configured to identify a gradual change in the performance trend, and where a fourth ML model is configured to identify whether the cell of the network is nearing a capacity limitation of the cell. The system provides a report based on the identified performance issue.

Abstract: A wireless communication system wirelessly transfers a network integration request to a distributed ledger over a communication satellite. The network integration request indicates an access node identifier, ledger credentials, and geographic location. The distributed ledger validates the ledger credentials, and in response, translates the access node identifier and the geographic location into an access node configuration. The distributed ledger transfers the access node configuration to the wireless access node over the communication satellite. Based on the access node configuration, the wireless access node wirelessly registers with a wireless communication network, wirelessly exchanges user data between User Equipment (UEs) and the wireless communication network, and hands-over some of the UEs with neighbor access nodes.

Abstract: Systems, methods, and devices are disclosed herein for an application service that utilizes a large language model (LLM) to assist with performing management tasks associated with a wireless communications network. In an implementation, the application service receives natural language input indicative of a management task to complete with respect to the network. The application service identifies a context of the management task, which helps constrain the LLM to the appropriate domain(s). The application service generates a prompt that incorporates the task and the context and submits the prompt to the LLM. The application service receives from the LLM a reply that includes a plan that details a series of steps and associated resources of the domain for completing the management task. Upon receiving the LLM's reply, the application service performs the management task based on the provided plan.

Abstract: The system obtains KPIs of a cell of a telecommunication network. The system preprocesses the multiple KPIs by reducing noise of the multiple KPIs and normalizing an amplitude of a KPI among the multiple KPIs. The system identifies a performance issue of the network by executing multiple ML models based on the KPIs, where a first ML model is configured to identify a sudden performance degradation, where a second ML model is configured to identify an event leading to a change in a performance trend, where a third ML model is configured to identify a gradual change in the performance trend, and where a fourth ML model is configured to identify whether the cell of the network is nearing a capacity limitation of the cell. The system provides a report based on the identified performance issue.

Abstract: A wireless communication device is disclosed that includes a non-transitory memory, a processor, and a local agent. The local agent transmits context information to an AI engine and receives and stores sign language data sets selected based on the context information from the AI engine in the non-transitory memory. The local agent receives content in need of language assistance. In response to a determination to process the content locally, the local agent converts the content into first sign language gestures based on the data sets. In response to a determination to process the content remotely, the local agent sends at least some of the content to the AI engine, receives keys from the AI engine, and determines second sign language gestures based on the keys and the data sets. The local agent sends the first or second sign language gestures to a display of the wireless communication device for presentation.

Abstract: A wireless communication system attests to user data from a wireless user device. The wireless user device receives satellite signals from satellites and determines satellite signal metrics for the satellite signals. The wireless user device determines its geographic location based on the satellite signals. The wireless user device executes an operating system in trusted processing circuitry in response to device power-up. The wireless user device executes a network application in the trusted processing circuitry. The network application transfers the user data, geographic location, and signal metrics to network elements. The network elements determine known satellite metrics for the geographic location. The network elements compare the satellite signal metrics to the known satellite metrics and generate an attestation score for the user data based on the satellite comparison. The network elements store the user data, the geographic location, and the attestation score.

Abstract: A wireless communication network attests to a geographic location of a wireless user device. An access node receives signaling transferred by the wireless user device that indicates the wireless user device, the geographic location, satellite signal characteristics for the geographic location, and terrestrial signal characteristics for the geographic location. The network access node transfers the signaling to a location attestation node. The location attestation node determines known signal characteristics for the geographic location. The location attestation node compares the known signal characteristics to the satellite signal characteristics and the terrestrial signal characteristics. The location attestation node generates a location-attestation score based on the comparison. The location attestation node transfers information that indicates the wireless user device, the geographic location, and the location-attestation score. The location attestation node may comprise a distributed ledge node.

Abstract: A wireless communication system wirelessly transfers a network integration request to a distributed ledger over a communication satellite. The network integration request indicates an access node identifier, ledger credentials, and geographic location. The distributed ledger validates the ledger credentials, and in response, translates the access node identifier and the geographic location into an access node configuration. The distributed ledger transfers the access node configuration to the wireless access node over the communication satellite. Based on the access node configuration, the wireless access node wirelessly registers with a wireless communication network, wirelessly exchanges user data between User Equipment (UEs) and the wireless communication network, and hands-over some of the UEs with neighbor access nodes.