Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a generative channel model (GCM) that outputs channel information pertaining to a digital twin (DT). The UE may receive a configuration of a downlink reference signal. The UE may perform channel estimation using the configuration of the downlink reference signal. The UE may transmit a report, wherein the report includes a precoding indicator, and wherein at least one of computation of the precoding indicator, or a channel estimation algorithm for the channel estimation, uses the GCM. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, a transformer configuration that includes a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more channel state information (CSI) feedback tasks of a plurality of CSI feedback tasks associated with a transformer-based cross-node machine learning system, and transmit the at least one latent vector based at least in part on instantiating the transmitter neural network. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, decoder configuration information associated with a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks associated with a query-based cross-node machine learning system. The UE may receive, from the network node, query configuration information associated with a query-based decoder. The UE may transmit, to the network node and based at least in part on instantiation of the transmitter neural network by the UE, the at least one latent vector. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may obtain position information associated with a first subset of network node positions, of a set of network node positions, in connection with a map associated with a wireless coverage area. The network node may obtain map information associated with the map and may determine, based on a machine learning component, a first plurality of predicted pathloss values associated with a second subset of network node positions of the set of network node positions. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive, from a network entity, an activation signal that transitions the UE from a first mode where the UE is capable of determining a channel quality using reference signals from the network entity to a second mode where the UE is capable of determining the channel quality using radio maps associated with the network entity. The UE may receive, based at least in part on the activation signal, an indication of a set of radio maps associated with the network entity. The UE may perform a channel performance procedure to determine the channel quality associated with the network entity using the set of radio maps and a geographic location of the UE, the geographic location of the UE being mapped within the set of radio maps associated with the network entity.

Abstract: A method of wireless communication performed by a first transmission device incudes receiving a first set of observations from a first receiving device based on transmissions from the first transmission device to the first receiving device at a previous time slot of a fixed contention-based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes measuring an energy level from an ongoing data transmission of the second transmission device at a current time slot. The method further includes generating, at an artificial neural network of the first transmission device, at least one of a transmission determination, a set of transmission parameters, or a combination thereof, based on a second set of observations, the energy level, and a counter.

Abstract: A method of wireless communication performed by a first transmission device includes determining a set of spectrum sharing parameters based on sensing performed during a sensing period of a current time slot in a fixed contention based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes determining, at a first artificial neural network of the first transmission device, a transmission device action, and/or a transmission parameter in response to receiving the set of spectrum sharing parameters. The method further includes transmitting, from the first transmission device, to a first receiving device during a data transmission phase of the current time slot based on the transmission device action and/or the transmission parameter.

Abstract: An apparatus includes a first substrate, a second substrate, and an intermediate layer disposed between the first and second substrates. The intermediate layer is configured to be a first point of failure or breakage of the apparatus under an applied force. The apparatus can further include a bonding layer disposed between the first and second substrates. The bonding layer is configured to bond the intermediate layer to the first and second substrates. The apparatus can further include a fastener coupled to the first and second substrates. The fastener is configured to secure the intermediate layer to the first and second substrates. The intermediate layer can include a coating applied to the first substrate or the second substrate. The apparatus can further include a second intermediate layer disposed between the first substrate and the fastener or the second substrate and the fastener.

Abstract: An apparatus includes a first substrate, a second substrate, and an intermediate layer disposed between the first and second substrates. The intermediate layer is configured to be a first point of failure or breakage of the apparatus under an applied force. The apparatus can further include a bonding layer disposed between the first and second substrates. The bonding layer is configured to bond the intermediate layer to the first and second substrates. The apparatus can further include a fastener coupled to the first and second substrates. The fastener is configured to secure the intermediate layer to the first and second substrates. The intermediate layer can include a coating applied to the first substrate or the second substrate. The apparatus can further include a second intermediate layer disposed between the first substrate and the fastener or the second substrate and the fastener.

Abstract: A method of wireless communication performed by a first transmission device incudes receiving a first set of observations from a first receiving device based on transmissions from the first transmission device to the first receiving device at a previous time slot of a fixed contention-based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes measuring an energy level from an ongoing data transmission of the second transmission device at a current time slot. The method further includes generating, at an artificial neural network of the first transmission device, at least one of a transmission determination, a set of transmission parameters, or a combination thereof, based on a second set of observations, the energy level, and a counter.

Abstract: A method of wireless communication performed by a first transmission device includes determining a set of spectrum sharing parameters based on sensing performed during a sensing period of a current time slot in a fixed contention based spectrum sharing system. The first transmission device shares a spectrum with a second transmission device. The method also includes determining, at a first artificial neural network of the first transmission device, a transmission device action, and/or a transmission parameter in response to receiving the set of spectrum sharing parameters. The method further includes transmitting, from the first transmission device, to a first receiving device during a data transmission phase of the current time slot based on the transmission device action and/or the transmission parameter.

Abstract: Video information defining video content to be encoded may be obtained. Scene composition information for the video content may be obtained. The scene composition information may be determined by a convolutional neural network based on visuals represented within the video content. The video content may be encoded based on the scene composition information. The encoding of the video content may generate encoded video information defining the encoded video content.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving, by an image signal processor, one or more input image signals from one or more image sensors; determining a mapping based on the input image signal(s), wherein the mapping includes records that associate image portions of an output image with corresponding image portions of the input image signal(s); sorting the records of the mapping according to an order of the corresponding image portions of the input image signal(s); applying, by the image signal processor, image processing to image portions of the input image signal(s) to determine image portions of one or more processed images in the order; and determining, by the image signal processor, the image portions of the output image based at least in part on the mapping and the corresponding image portions of the processed image(s) in the order.

Abstract: Image signal processing including generating image signal processing based encoding hints for motion estimation may include an image signal processor obtaining an input image portion of an input image from the input image signal, generating motion information for the input image portion, processing the input image portion based on the motion information, outputting processed image data, and outputting the motion information as encoding hints, such that the motion information is accessible by an encoder for generating an encoded output bitstream by obtaining the processed image data as source image data, obtaining the motion information, generating prediction data for encoding the source image data based on the motion information, generating encoded image data based on the prediction data, and including the encoded image data in an encoded output bitstream.

Abstract: Video information defining video content to be encoded may be obtained. Scene composition information for the video content may be obtained. The scene composition information may be determined by a convolutional neural network based on visuals represented within the video content. The video content may be encoded based on the scene composition information. The encoding of the video content may generate encoded video information defining the encoded video content.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving, by an image signal processor, one or more input image signals from one or more image sensors; determining a mapping based on the input image signal(s), wherein the mapping includes records that associate image portions of an output image with corresponding image portions of the input image signal(s); sorting the records of the mapping according to an order of the corresponding image portions of the input image signal(s); applying, by the image signal processor, image processing to image portions of the input image signal(s) to determine image portions of one or more processed images in the order; and determining, by the image signal processor, the image portions of the output image based at least in part on the mapping and the corresponding image portions of the processed image(s) in the order.

Abstract: Systems and methods for utilizing on-camera sensor information to improve video and/or image encoding quality are discussed herein. Specifically, the systems and methods may utilize on-camera sensor information to efficiently determine whether to encode a particular frame within a set of frames as an intra frame. When captured on video, a particular arrangement of a group of pixels within a frame may comprise a visual representation of an object within the frame. When encoding video footage, motion information characterizing motion of an image capturing device over time may be used to predict displacement of an arrangement of group of pixels between frames. These predictions may be used to efficiently determine whether to encode a particular frame as an intra frame.

Abstract: Video information defining video content to be encoded may be obtained. Scene composition information for the video content may be obtained. The scene composition information may be determined by a convolutional neural network based on visuals represented within the video content. The video content may be encoded based on the scene composition information. The encoding of the video content may generate encoded video information defining the encoded video content.

Abstract: A camera system processes images based on image luminance data. The camera system includes an image sensor, an image pipeline, an encoder and a memory. The image sensor converts light incident upon the image sensor into raw image data. The image pipeline converts raw image data into color-space image data and calculates luminance levels of the color-space image data. The encoder can determine one or more of quantization levels, determining GOP structure or reference frame spacing for the color-space image data based on the luminance levels. The memory stores the color-space image data and the luminance levels.

Abstract: Image signal processing based encoding hints for bitrate control may include an image signal processor receiving an input image signal, identifying a block of a frame based on the input image signal, generating processed image data corresponding to the block, determining a processed image indication for the block, and outputting the processed image data and the processed image indication as an encoding hint. Image signal processing based encoding hints for bitrate control may include an encoder receiving source image data for a frame, the source image data including the processed image data corresponding to the block, receiving source image indications for the frame, the source image indications including the processed image indication, determining an encoding parameter for encoding the frame based on the processed image indication, generating an encoded frame by encoding the frame using the encoding parameter, and outputting or storing the encoded frame.