Abstract: A method performs eigen decomposition with an artificial deep neural network. The deep neural network receives an input covariance matrix. The deep neural network has a number of convolutional layers and also a number of pooling layers. The deep neural network predicts dominant eigen information of the input covariance matrix, after applying the convolutional layers and the pooling layers to the input covariance matrix. The input covariance matrix may be a real-valued covariance matrix or a complex-valued covariance matrix having a concatenated pair of matrices, including a first matrix of real components and a second matrix of imaginary components. The dominant eigen information may be absolute values of a pair of dominant eigen values and sign information of the pair of dominant eigen values, and/or absolute values of a pair of dominant eigen vectors and sign information of the pair of dominant eigen vectors.

Abstract: Channel estimation performance depends on the amount of averaging performed by a channel impulse response coherent filter. For half-duplex UEs, which use a single phase locked loop (PLL) for both downlink transmissions and uplink transmissions, averaging may not be performed across downlink subframes before and after uplink subframes if the PLL's phase changes and locks to a random initial value when switching transmission directions. Techniques disclosed herein facilitate estimating the PLL's random initial phase and enable correcting the phase of symbols accordingly. By correcting the phase of the symbols, it is possible to average across symbols before and after a frequency re-tune and/or a transmission direction switch based on the phase correction. This may serve to improve the accuracy of channel estimation.

Abstract: Systems, methods, and computer-readable media are provided for high efficiency compression and decompression. An example method can include receiving, by a deep postprocessing network, a lower entropy image including a first version of a source image, the lower entropy image having a reduced entropy relative to the source image and a compressed state relative to the source image; decompressing the lower entropy image; identifying, by a generator network of the deep postprocessing network, a first set of style information having a similarity to a second set of style information missing from the lower entropy image, the second set of style information including style information included in the source image and removed from the lower entropy image; and generating, by the generator network of the deep postprocessing network, a higher entropy image including the lower entropy image modified to include the first set of style information.

Abstract: Channel estimation performance depends on the amount of averaging performed by a channel impulse response coherent filter. For half-duplex UEs, which use a single phase locked loop (PLL) for both downlink transmissions and uplink transmissions, averaging may not be performed across downlink subframes before and after uplink subframes if the PLL's phase changes and locks to a random initial value when switching transmission directions. Techniques disclosed herein facilitate estimating the PLL's random initial phase and enable correcting the phase of symbols accordingly. By correcting the phase of the symbols, it is possible to average across symbols before and after a frequency re-tune and/or a transmission direction switch based on the phase correction. This may serve to improve the accuracy of channel estimation.