Qualcomm Incorporated

Cisco Technology, Inc.

Clumio, Inc.

Comm Vault Systems, Inc.

Rajeev Kumar

Xipeng Zhu

Shankar Krishnan

Gavin Bernard Horn

Aziz Gholmieh

Sanjay Kumar Hooda

Ozcan Ozturk

Taesang Yoo

Punyaslok Purkayastha

Prakash C Jain

Rajagopal Venkatraman

Rajat Prakash

Umesh Phuyal

Eren Balevi

Xia Hua

Lawrence Jaemyung Chang

Abdul Jabbar Abdul Rasheed

Woonho Jung

Douglas Qian

Alberto Rico Alvarino

Bharat Shrestha

Ramesh Yeevani-Srinivas

John Stewart

Karan Dhabalia

Rolland Miller

Himanshu Mehra

Luis Fernando Brisson Lopes

Vanitha Aravamudhan Kumar

Vishal Dalmiya

Anoop Vetteth

Roberto Mitsuo Kobo

Vikash Agarwal

Xiaoxia Zhang

Navid Abedini

Masato Kitazoe

Sitaramanjaneyulu Kanamarlapudi

Prasad Reddy Kadiri

Naeem Akl

Shyamsundar Nandkishor Maniyar

Muninder Singh Sambi

Nikhil Sharma

Balaji Pitta Venkatachalapathy

Kedar Karmarkar

Raja Janardanan

Karthik Kumar Thatikonda

Denis Neogi

Aziz Gholmich

Disclosed techniques relate to security of backup data. In some embodiments, a method includes receiving, by data protection service running on a cloud computing system, a first encrypted copy of a backup of a first data store that is associated with a first account of an organization, where the first encrypted copy is encrypted using a first custodian cryptographic key that is shared between the organization and the data protection service that is different than a first production cryptographic key that is private and used by the organization to encrypt a non-backup version of the first data store. The method may include generating a second encrypted copy of the backup, including by encrypting the backup using a storage cryptographic key. The method may include storing the second encrypted copy of the backup in a second data store that is associated with the data protection service.

Data protection service using isolated, encrypted backup data

Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Configuring a user equipment for machine learning

Systems, methods, and computer-readable media are provided for performing secure frame encryption as a service. For instance, a network edge device can determine at least a first path and a second path for routing a data packet. The network edge device can obtain a first plurality of values for at least one network metric, wherein the first plurality of values corresponds to the first path and at least a first backup path associated with the first path. The network edge device can obtain a second plurality of values for the at least one network metric, wherein the second plurality of values corresponds to the second path and at least a second backup path associated with the second path. The network edge device can select a path for routing the data packet based on a comparison of the first plurality of values and the second plurality of values.

Exit interface selection based on intermediate paths

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may measure quality of experience (QoE) metrics and transmit a QoE report that is formatted to be readable by a base station, such that a base station may receive the QoE report and independently perform adjustments associated with the service being utilized by the UE. A UE may receive a configuration message that includes a first configuration for reporting QoE measurements to a base station and a second configuration for reporting QoE measurements to a QoE server. The UE may measure one or more QoE metrics in accordance with the configuration message, and generate a first report for the base station based on the QoE measurements and the first configuration. Upon generating the report, the UE may transmit the first report to the base station in accordance with the first configuration.

Quality of experience measurement and reporting

In one embodiment, network node-to-node connectivity verification is performed in a network including data path processing of packets within a packet switching device. In one embodiment, an echo request connectivity test packet, emulating an echo request connectivity test packet received from a first connected network node, is inserted by the packet switching device prior in its data processing path prior to ingress processing performed for packets received from the first connected network node. A correspondingly received echo reply connectivity test packet is intercepted by the packet switching device during data path egress processing performed for packets to be forwarded to the first connected network node.

Network node-to-node connectivity verification including data path processing of packets within a packet switching device

Methods, systems, and devices for wireless communications are described. Aspects of the disclosure relate to enhanced load reporting between network entities (e.g., provided by a distributed unit (DU) to a centralized unit (CU) or by a CU to another CU). In some examples, the CU may provide one or more triggering events which may be used by the DU (or a second CU) to trigger the provisioning of a load report to the CU. Accordingly, the DU (or second CU) may be able to initiate a load report without relying on periodic reporting criteria. In some examples, the DU may initiate a load report to the CU without being configured with triggering events. In some examples, a DU may report measurements made associated with a communications load involving communications with both a parent network entity (e.g., a CU) and at least one child network entity (e.g., a user equipment).

Load reporting in backhaul communications

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a primary cell or a primary secondary cell (PSCell), configuration information for a conditional PSCell addition or change (CPAC), the configuration information indicating configurations for one or more candidate PSCells. The UE may detect a failure, associated with the conditional PSCell addition or change, associated with a first target PSCell from the one or more candidate PSCells. The UE may perform an action to recover the CPAC based at least in part on detecting the failure. Numerous other aspects are described.

Conditional primary secondary cell addition or change recovery procedure

Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Data collection reporting for non-terrestrial network cells

Methods, systems, and devices for wireless communications are described. In some examples, a user equipment (UE) may support dual connectivity. That is, the UE may establish communications with a master node and a secondary node of a wireless communications network. The master node may correspond to a master cell group (MCG) and the secondary node may correspond to a secondary cell group (SCG). In some examples, as described herein, the UE operating in dual connectivity may collect data for optimization of the wireless communications network or upon detecting a failure associated with the master cell group or the SCG and transmit the collected data to a network entity (e.g., one of the master node or the secondary node), where the collected data is based on the SCG being in a deactivated state. In some examples, upon receiving the collected data, the network entity may attempt to recover from the failure.

Data collection enhancements for secondary cell groups

This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements for RRC IDLE and INACTIVE state operations. In a first aspect, a method of wireless communication includes receiving, by a wireless communication device, artificial intelligence (AI) model configuration information for IDLE/INACTIVE state procedures; retrieving, by the wireless communication device, an AI model for IDLE/INACTIVE state procedures based on the AI model configuration information; and performing, by the wireless communication device, one or more IDLE/INACTIVE state procedures based on the AI model. Other aspects and features are also claimed and described.

Artificial intelligence based enhancements for idle and inactive state operations

A source network node and a UE may obtain at least one mobility related prediction associated with the UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and the source network node may handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction. The target network node may receive the handover request, obtain at least one mobility related prediction associated with the UE or the target network node, and output for transmission a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.

AI/ML based mobility related prediction for handover

Aspects described herein relate to receiving, from a network node, a list of supported models or model structures (MS) identifiers (IDs) per machine learning function name (MLFN) or machine learning feature (MLF) at the network node, updating a capability at the UE to an updated capability based on the list of supported models or MS IDs per MLFN or MLF at the network node, and downloading, at the UE and from a model repository, one or more models or MSs per MLFN or MLF based on the updated capability and available resources at the UE. Other aspects relate to transmitting the list of supported models or MS IDs and configuring use of a model or MS ID for a particular MLFN or MLF.

Techniques for downloading models in wireless communications

Automated techniques for converting network devices from a Layer 2 (L2) network into a Layer 3 (L3) network in a hierarchical manner are described herein. The network devices may be configured to boot such that their ports are in an initialization mode in which the ports are unable to transmit locally generated DHCP packets. When a network device detects that a neighbor (or 'peer') device has acquired an IP address or has been configured by a network controller, then the port on which the neighbor device is detected can then be transitioned from the initialization mode into a forwarding mode. In the forwarding mode, the port can be used to transmit packets to obtain an IP address. Thus, the network devices are converted from an L2 device to an L3 device in a hierarchical order such that upstream devices are discovered and converted into L3 devices before downstream devices.

Hierarchical provisioning of network devices

A method may include bridging in, via a fabric, a multicast data packet from a source device to a first edge device of a plurality of edge devices and flooding the multicast data packet to the plurality of edge devices within a mutual subnetwork of the fabric. The method further includes bridging out the multicast data packet from a second edge device of the plurality of edge devices to a receiving device. The source device and the receiving device are located within the mutual subnetwork.

Multicasting within a mutual subnetwork

Growing community of inventors

Sunnyvale, CA, United States of America

Rajeev Kumar