Abstract: In some implementations, a network device may transmit user equipment (UE) route selection (URSP) rules during a registration of a UE with a non-standalone (NSA) network, wherein the URSP rules exclude network slicing related rules based on a lack of network slicing support in the NSA network. The network device may transmit, in response to a handover of the UE from the NSA network to a standalone (SA) network that supports network slicing, updated URSP rules during a registration of the UE with the SA network, wherein the updated URSP rules include a flag in each rule that controls the mode of migration of ongoing session(s) at the UE from the NSA network to the SA network during mobility procedures.

Abstract: A system described herein may receive a plurality of data sets; generate, for each data set, an Eigenvector representing a maximum variance of the data set; generate, for each data set, a projected vector, wherein generating a particular projected vector includes identifying a lowest distance between respective values of the particular data set and the particular Eigenvector, wherein the particular projected vector includes values along the Eigenvector that are each a lowest distance from a corresponding value of the particular data set; compare respective projected vectors, associated with one or more data sets, with one or more other data sets of the plurality of data sets; generate a plurality of clusters based on the comparing, wherein each cluster includes one or more data sets of the plurality of data sets; and train one or more artificial intelligence/machine learning (“AI/ML”) models based on the plurality of clusters.

Abstract: In some implementations, a platform as a service (PaaS) orchestrator may receive a set of values for a set of parameters of a software component template for a software component. The PaaS orchestrator may generate the software component using the set of values for the set of parameters. The PaaS orchestrator may execute the software component in a sandbox environment. The PaaS orchestrator may validate the software component for deployment. The PaaS orchestrator may deploy the software component in a deployment environment of the PaaS orchestrator. The PaaS orchestrator may detect a network event relating to the deployment environment. The PaaS orchestrator may identify a network management action associated with the network event. The PaaS orchestrator may one or more instructions to cause the network management action to be performed.

Abstract: A device may generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.

Abstract: A method may include receiving, by at least one network device and from a user device, a registration message including a service identifier and at least one of a network slice identifier or a network slice token. The method may also include determining, based on information included in the registration message, whether the user device is authorized to use a network slice associated with the service identifier. The method may further include setting up a data session to be serviced by the network slice, in response to determining that the user device is authorized to use the network slice.

Abstract: A system described herein may monitor information associated with a set of User Equipment (“UEs”) associated with a first network, and may receive an access request, for access to a first UE of the set of UEs, from a second UE associated with a second network. The system may obtain, based on the access request and from the second network, monitored information associated with the second UE. The system may identify monitored information associated with the first UE and a particular access policy that is associated with the first UE and the second UE. The system may identify, based on the particular access policy, the monitored information associated with the first UE, and the monitored information associated with the second UE, whether to grant or deny the access request. The system may output, in response to the access request, an indication of whether the access request is granted or denied.

Abstract: A system described herein may maintain a set of policies associated with non-Internet Protocol (“IP”) messaging; maintain information associating an IP address of a User Equipment (“UE”) with a non-IP identifier of the UE; receive first traffic, that includes the IP address of the UE as a source of the first traffic, and payload information; identify, using the maintained information associating the IP address of the UE with the non-IP identifier of the UE, the non-IP identifier of the UE based on the IP address of the UE included in the first traffic; identify that the first traffic is associated with a particular policy of the set of policies; and forward second traffic, including the payload information and the non-IP identifier of the UE, to a destination device indicated in the first traffic, where forwarding the second traffic includes implementing the particular policy with respect to the second traffic.

Abstract: Disclosed are systems and methods for a computerized, network-based framework that provides enhanced algorithmic mechanisms for improving GSM location determinations for cell tower communications. The disclosed framework's operations can provide improved visit determinations/estimates and increased value for a user's wireless/cellular data, whereby optimized parameters from and/or of the signals associated with a user device (e.g., mobile device) and tower radio communications therebetween can be leveraged to discern the POIs a user has visited and/or is currently visiting. The location tracking of the user, within a predisposed uncertainty sector correlated among the user's device communications with corresponding radio towers, provides network coverage to track specific locations among POIs for which the user has visited and/or is currently located.

Abstract: A device identifies, in multi-context text data, unrelated text and coreference text, and extracts coreference clusters, coreference sentences, and coreference sentiments based on the coreference text. The device extracts unrelated sentences from the unrelated text, and assigns tenses to the coreference sentences and the unrelated sentences. The device extracts phrases and entities from the coreference sentences and unrelated sentences, and assigns tense flags that exclude present tense sentences. The device selects past tense phrases and future tense phrases, and combines the past tense phrases and the future tense phrases to generate phrases. The device identifies invalid phrases in the phrases, and identifies similarities between the coreference sentences and the invalid phrases. The device processes the coreference text, the coreference tenses, the coreference sentiments, and the similarities, with a reinforcement learning model, to generate final context text.

Abstract: One or more computing devices, systems, and/or methods for dynamic resource slice allocation are provided. A bidding engine dynamically tracks available network slices and available edge computing resources to generate a current list of resource slices corresponding to combinations of available network slices and available edge computing resources. A bidding auction is conducted for the current list of resource slices. A user equipment device is determined to be a winner of the bidding auction for a resource slice based upon a bidding request from the user equipment device. In this way, the user equipment device is provide with access to the resource slice based upon a determination that the user equipment device won the bidding auction.

Abstract: A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an detection and autocorrection of stale session information service. The detection and autocorrection service may include the addition of a header, which may indicate date and time information, with a message that has an origination timestamp header. The header may be added to the message by an intermediary network device relative to origin and destination network devices associated with a network procedure. The header may enable detection by a network device of stale session information. The header may enable the network device to determine whether to ignore the message as a duplicate message or use the message to carry out the network procedure. In this way, duplicate messages that may include the same origination timestamp header may be managed and stale information may be detected.

Abstract: A system may include a first network device. The first network device may include a first processor configured to: obtain a copy of a first message sent from an application on a User Equipment device (UE); construct a signature based on the copy of the first message; and send a second message including the signature to a second network device. The second message may request the second network device to either train a classification model or to provide a device type of the UE or an application type of the application to the first network device.

Abstract: A device may receive historical input data associated with trips traversed by a plurality of vehicles with vehicle tracking units (VTUs), and may process the historical input data to generate training data. The device may train a neural network model, with the training data, to generate a trained neural network model that provides a latent space representation of vectors, and may cluster the latent space representation of vectors to generate clusters. The device may receive input data associated with a trip traversed by a vehicle of the plurality of vehicles with the VTUs, and may process the input data to generate time series data. The device may compare the time series data and the clusters to determine whether the trip is anomalous or not anomalous, and may perform one or more actions based on the determination of whether the trip is anomalous or not anomalous.

Abstract: In some implementations, the techniques described herein relate to a method including: receiving a set of communication events from a user equipment to a base station; obtaining a set of geocoordinates corresponding to the user equipment; calculating an angular mean for the set of geocoordinates; sorting the set of geocoordinates based on distances between the set of geocoordinates and a location of the base station to generate an ordered list of geocoordinates; calculating a k-th order statistic of the ordered list of geocoordinates; generating a predicted location based on the angular mean and the k-th order statistic; and assigning the predicted location as a current location of the user equipment in a cellular core network.

Abstract: A device, such as a Subscriber Identification Module (“SIM”) device, may maintain a set of profiles that each include a respective set of authentication credentials for connecting to one or more wireless networks. The device may maintain a set of policies associating respective events with respective profiles of the set of profiles, may receive an indication of a particular event, and may identify, based on the set of policies, that the particular event is associated with a particular profile. The device may select the particular profile as an active profile, and may output a refresh command after selecting the particular profile as the active profile. The device may receive, based on outputting the refresh command, a request for the active profile, and may output, based on the request for the active profile, the set of authentication credentials associated with the selected particular profile.

Abstract: A system described herein may identify traffic associated with a User Equipment (“UE”) that is connected to a first network. The UE may maintain a plurality of UE identities via one or more SIM (“Subscriber Identification Module”) cards, Universal Integrated Circuit Cards (“UICCs”), etc. The system may determine, based on one or more policies, that the traffic is not authorized via the first network. In some situations, the traffic may not be authorized if the first network does not support a type, service, etc. of the traffic. The system may identify a second network, based on the one or more policies, via which the traffic is authorized, and may indicate the second network to the UE. The UE may automatically (e.g., without user intervention) switch to a particular UE identity that is associated with the second network, and may output the traffic via the second network using such UE identity.

Abstract: A device may receive channel data associated with digital channels utilized by a customer, and may identify, in the channel data, potential issues and sequential events for the potential issues. The device may generate a causal relationship graph based on the sequential events and the potential issues, and may process the causal relationship graph, with a machine learning model, to identify issues of the customer and events associated with the issues. The device may apply causal inference to identify causal relationships between the issues and the events, and may calculate an inference score for the issues and the events based on the causal relationships. The device may modify, based on the inference score, a customer journey defined by the events to generate a modified customer journey, and may perform one or more actions based on the modified customer journey.

Abstract: A system described herein may receive traffic from a User Equipment (“UE”) via a radio unit (“RU”), may identify one or more attributes of the traffic, and may select a first Central Unit (“CU”), from a group of CUs that includes the first CU and a second CU, based on the one or more attributes of the traffic. The one or more attributes may include a network slice. The device may further route the traffic to the selected first CU, wherein the CU aggregates received traffic and routes the aggregated traffic to a user plane gateway. The device may include a Distributed Unit (“DU”) of a radio access network (“RAN”) that includes the first and second CUs. The first and second CUs may include a first CU-User Plane (“CU-UP”) and a second CU-UP. The DU and the CUs may be configured by a CU-Control Plane (“CU-CP”) of the RAN.

Abstract: In some implementations, the techniques described herein relate to a method including: receiving a network request from a second network function, the network request including a parameter; validating the parameter upon receipt of the network request to determine a status of the network request, the status indicating a valid or invalid request; processing, by a first network function, a resource managed by the first network function based on the network request and returning a network response when the status indicates a valid request; and denying the network request and returning an error code when the status indicates an invalid request.

Abstract: A method, a network device, and a non-transitory computer-readable storage medium are described in relation to a secure operations, administration, and management (OAM) for remote networks service. The service may categorize performance metric (PM), fault metric (FM) data, and other types of data associated with a virtualized network device of a private network and provide such data to an OAM system of an operator network via a blockchain network or not based on the categorization. The OAM system may also manage orchestration and maintenance of the private network including the virtualized network device based on configuration data transmitted via the blockchain network or not. The virtualized network device may be a radio access network device, a core network device, or an edge server device. The secure OAM for remote networks service may also store service assurance policies as a smart contract in the blockchain network.