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: Certain aspects of the present disclosure provide techniques and apparatuses for training and using machine learning models to estimate a representation of a channel between a transmitter and a receiver in a spatial environment. 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 information about the spatial environment. One or more actions are taken based on the estimated representation of the channel.

Abstract: A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.

Abstract: Certain aspects of the present disclosure provide techniques for wireless channel modeling. A set of input data is received for data transmitted, from a transmitter, as a signal in a wireless channel. A channel model is generated for the wireless channel using a generative adversarial network (GAN). A set of simulated output data is generated by transforming the first set of input data using the channel model.

Abstract: A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.