Abstract: Systems and methods for providing machine learning based location and directions for venue and campus network are provided. In one example, a system includes a BBU entity and RU(s) communicatively coupled to the BBU entity. The system further includes antenna(s) communicatively coupled to the RU(s), and each respective RU is communicatively coupled to a respective subset of the antenna(s). The BBU entity, the RU(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with UEs in a cell. The system further includes a machine learning computing system configured to receive time and location data and determine a predicted density for location areas in the cell based on the time and location data.

Abstract: Systems and methods for machine learning based network slice modification, addition, and deletion are provided. In one example, a method includes receiving time data, traffic data, and QoS data and determining a predicted radio resource usage of a base station based on the time data, traffic data, and QoS data. The base station includes at least one BBU, radio unit(s) communicatively coupled to the at least one BBU, and antenna(s) communicatively coupled to the radio unit(s). Each respective radio unit is communicatively coupled to a respective subset of the antenna(s). The at least one BBU, the radio unit(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with user equipment. The method further includes dynamically modifying, adding, or deleting one or more network slices based on the predicted radio resource usage of the base station.

Abstract: Systems and methods for machine learning based radio resource usage are provided. In one example, a system includes BBU(s), RU(s) communicatively coupled to the BBU(s), and antenna(s) communicatively coupled to the RU(s). Each respective RU is communicatively coupled to a respective subset of the antenna(s). The BBU(s), the RU(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with user equipment. The system further includes a machine learning computing system. The machine learning computer system is configured to receive time data and traffic data and determine a predicted radio resource usage of the base station based on the time data and the traffic data. The system is configured to adjust operation of at least one RU based on the predicted radio resource usage of the base station.

Abstract: Techniques are disclosed for using a model and data to determine adjustments, e.g., reductions, of power consumption, e.g., component(s), of a base station utilizing an older or optionally a newer RAT, and even when to decommission a base station utilizing the older RAT.

Abstract: A method for execution in a storage network begins with a storage network entity receiving a write request for a data object, where the data object is dispersed error encoded in accordance with dispersed error encoding parameters to produce a plurality of sets of encoded data slices. The method continues with the storage network entity determining whether to store a plurality of copies of the data object; and in response to a decision to store a plurality of copies of the data object, determining a plurality of vaults for storage of the data object. The method then continues by identifying a multicast IP address associated with the plurality vaults for storage of the plurality of copies. The method then continues by generating a multicast IP message to a plurality of storage units associated with each vault of the plurality of vaults, where the storage units of the plurality of storage units are configured to determine a multicast IP address from the multicast IP message.

Abstract: Systems and methods for radio intelligent controller machine learning assisted admission control are provided. In one example, a method includes receiving performance indicator(s) for standalone UEs and performance indicator(s) for non-standalone UEs from BBU(s) of a base station. The base station includes the BBU(s), a first radio unit, and antenna(s) configured to implement a base station for wirelessly communicating with user equipment in a cell. The method includes determining predicted traffic parameter(s) for standalone UEs based on the received performance indicator(s) for standalone UEs from the BBU(s) and determining predicted traffic parameter(s) for non-standalone UEs based on the received performance indicators for non-standalone UEs from the BBU(s). The method includes allocating resources for standalone and/or non-standalone UEs based on the predicted traffic parameter(s) for standalone UEs, the predicted traffic parameter(s) for non-standalone UEs, and service requirements for the base station.

Abstract: Systems and methods for supporting private networks using a DAS are provided. In one example, a method for supporting a private network with a DAS includes determining whether a private network is activated and connected to the DAS. The method further includes determining one or more paths of the DAS impacted by the private network. The method further includes receiving an indication of available resources and capability information for one or more components in the DAS. The method further includes dedicating resources of the DAS to the private network based on requirements of the private network and the available resources for the one or more components of the DAS. The method further includes utilizing the resources of the DAS dedicated to the private network for traffic having an identifier corresponding to the private network.

Abstract: Systems and methods for using a radio intelligent controller with a distributed antenna system and fronthaul multiplexer/fronthaul gateway (FHM/FHGW) are provided. In one example, a method for using a radio intelligent controller with a DAS includes receiving fronthaul information via an E2 interface from one or more nodes of the DAS included in a system. The method further includes automatically generating one or more operational parameters for one or more components of the system that includes the DAS based on the fronthaul information received via an E2 interface from the one or more nodes of the DAS. The method further includes adjusting operation of one or more components of the system that includes the DAS based on the one or more automatically generated operational parameters for the one or more components of the system that includes the DAS.

Abstract: Systems and methods for ML based ACB are provided herein. In an example, a system includes BBU(s), RU(s) communicatively coupled to the BBU(s), and antenna(s) communicatively coupled to the RU(s). Each respective RU of the RU(s) is communicatively coupled to a respective subset of the antenna(s). The BBU(s), the RU(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with UEs in a cell. The system includes a machine learning computing system configured to: receive time data, traffic data, and location data; and determine predicted barring parameter(s) for the base station based on the time data, the traffic data, and the location data. The system is configured to: adjust barring factor(s) and/or barring time(s) in an information message based on the predicted barring parameter(s) for the base station; and send the information message with the adjusted barring factor(s) and/or barring time(s) to UEs in the cell.

Abstract: Systems and methods for machine learning based dynamic selection of dual connectivity or carrier aggregation are provided. In an example, a system includes BBU(s), RU(s) communicatively coupled to the BBU(s), and antenna(s) communicatively coupled to the RU(s). Each respective RU of the RU(s) is communicatively coupled to a respective subset of the antenna(s). The BBU(s), the RU(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with user equipment. The system further includes a machine learning computing system configured to receive time data and traffic data, and determine a predicted mode of operation for a UE based on the time data and the traffic data. Component(s) of the system are configured to receive a request from the UE, and dynamically select a dual connectivity mode or a carrier aggregation mode for operation of the UE based on the predicted mode of operation for the UE.

Abstract: A method for execution by a processing system includes determining to retrieve index data for a set of objects stored by an object storage system. An index type is selected from a plurality of index. A request message indicating a request for the index data of one of a plurality of indices stored by the object storage system corresponding to the index type is generated, and the request message is transmitted to the object storage system. The index data for the set of objects is received in a response message from the object storage system. The index data includes a plurality of pairs that each include a key and a value, where the keys correspond to the index type, and where the value of each pair includes metadata of one of the set of objects. The index data is stored in local memory in response to receiving the index data.

Abstract: Systems and methods for fronthaul optimization using software defined networking are provided. In one example, a method includes receiving time period information and destination information for a time period from one or more base station entities (BSEs), each BSE configured to implement some functions for layer(s) of a wireless interface used to communicate with UEs. The method further includes determining a configuration of Ethernet switch(es) based on the destination information for the time period and topology information for the Ethernet switch(es). The Ethernet switch(es) are communicatively coupled to the BSE(s) and configured to: receive downlink fronthaul data from the BSE(s), be communicatively coupled to one or more RUs, and forward downlink fronthaul data from the one or more base station entities to the one or more RUs. The method further includes transmitting update(s) for forwarding rules to the Ethernet switch(es) based on the determined configuration for the Ethernet switch(es).

Abstract: Systems and methods for reducing latency in the data path between a source and destination and managing resources in a 5G network are provided. The systems and methods described herein include dynamically selecting user plane functions (UPFs) and/or dynamically placing UPFs at particular locations within the 5G network. In some examples, each UPF in the 5G network is statically placed, and the UPF used for communication between a source and destination is dynamically selected based on control plane data. In other examples, UPFs are dynamically placed, and the UPF used for communication is dynamically selected to reduce latency and/or resource usage based on control plane data. In other examples, UPFs are dynamically placed, and the UPF used for communication is dynamically selected to reduce latency and/or resource usage based on user plane data after the flow of packets has started between a source and destination.

Abstract: A method for execution by an access layer of an object storage system includes In various embodiments, a processing system of an access layer of an object storage system includes at least one processor and a memory that stores operational instructions, that when executed by the at least one processor cause the processing system to receive a request message from a requesting entity via a network, where the request message includes a pre-signed URL. A set of custom policy parameters are extracted from the pre-signed URL. Policy verification data is generated by comparing each attribute of a determined set of attributes of the access request to a corresponding custom policy parameter of the set of custom policy parameters. An access indicated in the request message is executed in response to the policy verification data indicating that each attribute compares favorably to the corresponding custom policy parameter.

Abstract: Systems and methods for replicating containers in object storage using intents are disclosed. A DS processing unit, upon reception of a write request may determine the location of replicated instances for the container. DS processing unit can then generate an intent for each fork, to indicate a specific write request received. The DS processing unit may save or persist intent objects within one of the containers, or in a dispersed data structure. A DS unit or DS processing unit may then check for intents whose operation has not been completed, and when resources to perform the operation are available, perform the operation specified in the intent. DS processing unit can remove a pending intent once the job or operation is complete. A clean-up agent could run periodically to complete unfinished jobs that are pending within DS processing unit or DS unit due to containers not being available for whatever reason.

Abstract: A system and method for co-locating a containerized application and an associated cloud storage bucket are provided. In embodiments, a method includes determining, by a computing device, a location of a containerized application in a data storage network; determining, by the computing device, that a cloud storage bucket does not yet exist in a geographic zone associated with the location of the containerized application; selecting, by the computing device, a data storage location for the cloud storage bucket from a plurality of data storage locations in the data storage network based on the geographic zone; and sending, by the computing device, a request to create the cloud storage bucket to the data storage location and causing creation and co-location of the cloud storage bucket within a predetermined distance of the containerized application.

Abstract: A storage system access point receives a first access request from a client device, wherein the first access request specifies first data. The storage system access point attempts to execute the first access request. In response to successfully accessing the first data, a first message is transmitted to an external audit system indicating that information corresponding to the first access request is to be recorded by the external audit system. In response to that the first data has been successfully accessed, and that the information corresponding to the first access request has been successfully recorded by the external audit system, notifying the client device that the first access request has been successfully completed.

Abstract: A method includes detecting, by a security module of a dispersed storage network (DSN), a potentially compromised encoded data slice (EDS) of a set of EDSs. The potentially compromised EDS is stored in a storage unit of a set of storage units of the DSN. The method further includes monitoring other storage units of the set of storage units to detect one or more other potentially compromised EDSs of the set of EDSs. When the one or more other potentially compromised EDSs are detected, the method includes determining a data compromise threat level based on the potentially compromised EDSs and the one or more other potentially compromised EDSs and enabling an automatic data preservation protocol based on the data compromise threat level. The automatic data preservation protocol includes one or more of: one or more data preservation options, one or more data tracking options, and one or more notification options.

Abstract: A method for execution by a computing device of an access layer of an object storage system includes receiving, via a network, a request message from an edge node, wherein the request message indicates a requested access of a data object. An update frequency of the data object is determined in response to receiving the request message, and a cache time to live (TTL) is determined based on the update frequency. Performance of the requested access of the data object is facilitated in response to receiving the request message, and a response message that includes the cache TTL is generated in response to performing the requested access. The response message is transmitted to the edge node via the network.

Abstract: A system and method for co-locating a containerized application and an associated cloud storage bucket are provided. In embodiments, a method includes determining, by a computing device, a location of a containerized application in a data storage network; determining, by the computing device, that a cloud storage bucket does not yet exist in a geographic zone associated with the location of the containerized application; selecting, by the computing device, a data storage location for the cloud storage bucket from a plurality of data storage locations in the data storage network based on the geographic zone; and sending, by the computing device, a request to create the cloud storage bucket to the data storage location and causing creation and co-location of the cloud storage bucket within a predetermined distance of the containerized application.