Abstract: The apparatus may be configured to receive a plurality of codewords associated with a plurality of messages, where the plurality of codewords comprises a first subset of the plurality of codewords associated with a first subset of the plurality of messages and a second subset of the plurality of codewords associated with a second subset of the plurality of messages, wherein the first and second subset of codewords are associated with first and second RM codes, wherein the first RM code is a subcode of the second RM code, decode the plurality of codewords using a decoding method that is associated with a first error tolerance for the first subset of the plurality of codewords that is higher than a second error tolerance for the second subset of the plurality of codewords, and output a plurality of decoded codewords associated with the plurality of messages.

Abstract: The apparatus may be a wireless device configured to estimate, for a first transmission in a first slot, a first channel associated with the first transmission, wherein the first transmission is associated with a first precoding, receive, in a second slot following the first slot, a second transmission associated with a second precoding, and estimate, based on the received second transmission and at least one of the received first transmission or the estimated first channel, a second channel associated with the second transmission.

Abstract: A processor-implemented method for estimating a channel by a deep generative model includes receiving, at a device, an observation of the channel and mapping, at the device, the observation to a mean value associated with the channel and a covariance matrix associated with the channel. The processor-implemented method also includes reconstructing, at the device, the channel based on the mean value and the covariance matrix.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. Certain techniques include receiving signals corresponding to a MIMO channel matrix; generating a first gram matrix from a basis for a first lattice corresponding to a first signal of the received signals; providing the first gram matrix to a neural lattice reduction model comprising an equivariant neural network configured to generate a current extended Gauss move; generating, with the neural lattice reduction model, a current partial changed basis based on the current extended Gauss move and the basis; executing one or more additional iterations of the neural lattice reduction model; and demapping the MIMO channel matrix based on combining the current partial changed basis and each of the additional partial changed basis generated by each of the one or more additional iterations of the neural lattice reduction model.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. Certain techniques include receiving signals corresponding to a MIMO channel matrix; generating a first gram matrix from a basis for a first lattice corresponding to a first signal of the received signals; providing the first gram matrix to a neural lattice reduction model comprising an equivariant neural network configured to generate a current extended Gauss move; generating, with the neural lattice reduction model, a current partial changed basis based on the current extended Gauss move and the basis; executing one or more additional iterations of the neural lattice reduction model; and demapping the MIMO channel matrix based on combining the current partial changed basis and each of the additional partial changed basis generated by each of the one or more additional iterations of the neural lattice reduction model.

Abstract: Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. Certain techniques include providing a first gram matrix to a neural lattice reduction model; generating, with the neural lattice reduction model, one or more partial changed bases; and generating a first reduced basis based on the one or more partial changed bases.

Abstract: A computer-implemented method for estimating a pose of an object includes receiving, at a pose estimation model, image data comprising a plurality of two-dimensional (2D) images of an object. Each 2D image of the plurality of 2D images has a different pose. The pose estimation model aligns a first 2D image of the plurality of 2D images with a second 2D image of the plurality of 2D images based on geometric properties related to the first 2D image and the second 2D image. The pose estimation model estimates a pose of the first 2D image and the second 2D image based on the plurality of 2D images and a loss associated with a common line between the first 2D image and the second 2D image.

Abstract: Methods, systems, and devices for wireless communications are described. A wireless device may receive an assignment of a set of resources associated with a channel, where the set of resources includes a first subset of resources allocated for data transmission and a second subset of resources allocated for a reference signal. The wireless device may generate, from the reference signal in accordance with a minimum mean square estimation (MMSE) operation, a first set of multiple channel estimations per layer of the channel. The wireless device may generate, in accordance with a nonlinear two-dimensional interpolation of the channel, a second set of multiple channel estimations per layer of the channel and may perform a refinement operation utilizing the estimations to generate a channel estimation associated with multiple layers.

Abstract: Methods, systems, and devices for wireless communications are described. A wireless device may receive an assignment of a set of resources associated with a channel where the set of resources includes a first subset of resources allocated for data transmission and a second subset of resources allocated for a reference signal. The wireless device may generate multiple channel estimations per layer of the channel and perform a refinement operation utilizing the estimations to generate a channel estimation associated with multiple layers. Each iteration of the refinement operation may include generating respective gradients associated with each per layer channel estimation; generating a current set of values of a latent variable; and modifying the channel estimations.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training and using machine learning models to estimate a layout of a spatial area. An example method generally includes estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and a three-dimensional representation of the spatial environment. One or more actions are taken based on the estimated representation of the channel.

Abstract: A processor-implemented method for estimating a channel by a deep generative model includes receiving, at a device, an observation of the channel and mapping, at the device, the observation to a mean value associated with the channel and a covariance matrix associated with the channel. The processor-implemented method also includes reconstructing, at the device, the channel based on the mean value and the covariance matrix.

Abstract: A processor-implemented method is presented. The method includes receiving an input sequence comprising a group of channel dynamics observations for a wireless communication channel. Each channel dynamics observation may correspond to a timing of a group of timings. The method also includes determining, via a recurrent neural network (RNN), a residual at each of the group of timings based on the group of channel dynamics observations. The method further includes updating Kalman filter (KF) parameters based on the residual and estimating, via the KF, a channel state based on the updated KF parameters.

Abstract: A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.

Abstract: A processor-implemented method is presented. The method includes receiving an input sequence comprising a group of channel dynamics observations for a wireless communication channel. Each channel dynamics observation may correspond to a timing of a group of timings. The method also includes determining, via a recurrent neural network (RNN), a residual at each of the group of timings based on the group of channel dynamics observations. The method further includes updating Kalman filter (KF) parameters based on the residual and estimating, via the KF, a channel state based on the updated KF parameters.

Abstract: Techniques for self-service orchestration are disclosed. A system deploys instances of a self-service orchestration agent to tenant-specific software-as-a-service (SaaS) environments operating in a multi-tenant SaaS environment, without reconfiguring existing software in the tenant-specific SaaS environments. Each self-service orchestration agent includes functionality to configure one or more components. Each tenant-specific SaaS environment includes a dedicated set of software operating on a dedicated logical partition of hardware infrastructure. The system receives, via a self-service orchestration interface, a request to configure a component across the tenant-specific SaaS environments.

Abstract: A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.

Abstract: Techniques for self-service orchestration are disclosed. A system deploys instances of a self-service orchestration agent to tenant-specific software-as-a-service (SaaS) environments operating in a multi-tenant SaaS environment, without reconfiguring existing software in the tenant-specific SaaS environments. Each self-service orchestration agent includes functionality to configure one or more components. Each tenant-specific SaaS environment includes a dedicated set of software operating on a dedicated logical partition of hardware infrastructure. The system receives, via a self-service orchestration interface, a request to configure a component across the tenant-specific SaaS environments.