Qualcomm Incorporated

Kumar Pratik

Arash Behboodi

Joseph Binamira Soriaga

Max Welling

Rana Ali Amjad

Gabriele Cesa

Giovanni Luca Marchetti

Fabio Valerio Massoli

Anna Kuzina

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.

Equivariant generative prior for inverse problems with unknown rotation

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.

Geometric deep learning for lattice reduction

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.

Hypernetwork Kalman filter for channel estimation and tracking

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.

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Amsterdam, Netherlands

Kumar Pratik