Abstract: An integration application comprising a plurality of components may be provided. The components may provide connections between systems. The integration application may be configured to allow the systems to exchange data. It may be determined that a new version of a first one of the components is a candidate for release. Prior to release of the new version of the first component, the integration application may be tested using the new version of the first component and an existing version of the first component. An instance of the integration application may be automatically caused to be upgraded to include the new version of the first component.

Abstract: Examples of the present disclosure relate to the optical identification of telecommunications equipment. In examples, a user interacts with an application to capture image data relating to a device according to instructions presented to the user. The application may further generate metadata, such as user responses to one or more questions. The image data and/or metadata are evaluated using a machine learning model to generate an equipment classification for devices pictured therein. The data may also be used to generate an equipment configuration for the device, as well as an operational state (e.g., based on one or more indicators present on the device, log data, etc.). Accordingly, such information may be used to update a pre-existing inventory record for the device, or generate a new inventory record. In other examples, such information is used to generate one or more predicted issues and associated actions to troubleshoot the device.

Abstract: In some examples, a method of wireless communication by a user equipment, UE, comprises receiving configured grant, CG, configuration data at a first UE, the CG configuration data comprising bi-directional CG information for an aligned sidelink communication session between the first UE and a second UE, monitoring for receipt of a first transmission of the communication session at the first UE, and on the basis of the monitoring, acquiring the channel for the sidelink communication session at the first UE.

Abstract: Novel tools and techniques are provided for scene aware searching. A system may include a media player configured to play a video stream, a database, and a server configured to host an artificial intelligence (AI) engine. The server may further include a processor and a non-transitory computer readable medium comprising a set of instructions that, when executable by the processor to receive, from the media device, a search query from a user. The AI engine may further be configured to obtain the video stream associated with the search query, identify one or more objects in the video stream, derive contextual data associated with the one or more objects, identify one or more matches based on the contextual data, and determine a result of the search query.

Abstract: An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

Abstract: Disclosed is a method comprising receiving, by a first terminal device, a first transmission from a second terminal device. The first terminal device measures a channel busy ratio over a first time window. The first terminal device determines a channel occupancy ratio of the second terminal device based at least partly on the received first transmission. The first terminal device updates the channel busy ratio based at least partly on the determined channel occupancy ratio and the measured channel busy ratio. The first terminal device determines one or more sidelink transmission parameters based at least partly on the updated channel busy ratio.

Abstract: Examples of the present disclosure relate to the optical identification of telecommunications equipment. In examples, a user interacts with an application to capture image data relating to a device according to instructions presented to the user. The application may further generate metadata, such as user responses to one or more questions. The image data and/or metadata are evaluated using a machine learning model to generate an equipment classification for devices pictured therein. The data may also be used to generate an equipment configuration for the device, as well as an operational state (e.g., based on one or more indicators present on the device, log data, etc.). Accordingly, such information may be used to update a pre-existing inventory record for the device, or generate a new inventory record. In other examples, such information is used to generate one or more predicted issues and associated actions to troubleshoot the device.

Abstract: An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

Abstract: Apparatus and method in a communication system for enhanced carrier selection are provided. The solution comprises selecting a carrier for first transmission to a second apparatus; controlling transmission utilising the selected carrier; receiving a transmission from the second apparatus and selecting a carrier for second transmission to the second apparatus at least partly based on the transmission from the second apparatus.

Abstract: Novel tools and techniques are provided for scene aware searching. A system may include a media player configured to play a video stream, a database, and a server configured to host an artificial intelligence (AI) engine. The server may further include a processor and a non-transitory computer readable medium comprising a set of instructions that, when executable by the processor to receive, from the media device, a search query from a user. The AI engine may further be configured to obtain the video stream associated with the search query, identify one or more objects in the video stream, derive contextual data associated with the one or more objects, identify one or more matches based on the contextual data, and determine a result of the search query.

Abstract: Systems, methods, and devices facilitate generation of application programming interfaces. Systems include a computing platform implemented using a server system, the computing platform being configurable to cause monitoring application programming interface (API) traffic associated with a client service, storing a representation of the API traffic in a database system, the representation including at least an API call identifier and metadata, and generating one or more API models based, at least in part, on the representation of the API traffic, the one or more API models being configured to represent a plurality of API interactions associated with the client service. The computing platform is further configurable to cause generating one or more API objects based, at least in part, on the API models, the one or more API objects being one or more components of a client service API.

Abstract: An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

Abstract: An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

Abstract: Apparatus and method in a communication system for reconfiguring resources for transmission are disclosed. More than one sidelink transmissions are transmitted to a second apparatus. It is determined that reception status feedbacks are not received regarding a given number of sidelink transmissions. Transmission resource used for sidelink transmission is reconfigured based on the determination.

Abstract: Novel tools and techniques are provided for scene aware searching. A system may include a media player configured to play a video stream, a database, and a server configured to host an artificial intelligence (AI) engine. The server may further include a processor and a non-transitory computer readable medium comprising a set of instructions that, when executable by the processor to receive, from the media device, a search query from a user. The AI engine may further be configured to obtain the video stream associated with the search query, identify one or more objects in the video stream, derive contextual data associated with the one or more objects, identify one or more matches based on the contextual data, and determine a result of the search query.

Abstract: An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

Abstract: A device receiving a transmission is configured to provide its feedback in a unique resource pattern within a set of resources (a resource pool). The resource pattern used by the device overlaps partially with one or more resource patterns used for feedback transmissions by other devices in the group. The transmitting device is able to identify the device by identifying an activation pattern matching the unique resource pattern of the device in the overall feedback that it receives from the devices of the group.

Abstract: Disclosed is a method comprising receiving, by a first terminal device, a first transmission from a second terminal device. The first terminal device measures a channel busy ratio over a first time window. The first terminal device determines a channel occupancy ratio of the second terminal device based at least partly on the received first transmission. The first terminal device updates the channel busy ratio based at least partly on the determined channel occupancy ratio and the measured channel busy ratio. The first terminal device determines one or more sidelink transmission parameters based at least partly on the updated channel busy ratio.

Abstract: Apparatus and method in a communication system for reconfiguring resources for transmission are disclosed. More than one sidelink transmissions are transmitted to a second apparatus. It is determined that reception status feedbacks are not received regarding a given number of sidelink transmissions. Transmission resource used for sidelink transmission is reconfigured based on the determination.

Abstract: Examples of the present disclosure relate to the optical identification of telecommunications equipment. In examples, a user interacts with an application to capture image data relating to a device according to instructions presented to the user. The application may further generate metadata, such as user responses to one or more questions. The image data and/or metadata are evaluated using a machine learning model to generate an equipment classification for devices pictured therein. The data may also be used to generate an equipment configuration for the device, as well as an operational state (e.g., based on one or more indicators present on the device, log data, etc.). Accordingly, such information may be used to update a pre-existing inventory record for the device, or generate a new inventory record. In other examples, such information is used to generate one or more predicted issues and associated actions to troubleshoot the device.