Abstract: An apparatus for feature-based communications is provided that includes a probabilistic encoder and a transmitter. The probabilistic encoder is configured to encode source information into a set of probability distributions over a latent space. Each probability distribution represents one or more aspects of a subject of the source information. The transmitter is configured to transmit over a transmission channel, to a receiving electronic device, a set of transmission features representing the subject. Each transmission feature provides information about a respective one of the probability distributions in the latent space. The probabilistic encoder is configured to enforce constraints on distribution parameters of the probability distributions over the latent space based on a condition of the transmission channel. Enforcing constraints on the latent space in this manner enables the apparatus to transmit features that are at least as unreliable as the transmission channel.

Abstract: A codeword is generated based on a segmentation transform and a Polarization-Adjusted Convolutional (PAC) code that includes an outer convolutional code and a polar code, and based on separate encoding of respective different segments of convolutionally encoded input bits according to the polar code. Each segment of the respective segments includes multiple bits of the convolutionally encoded input bits for which the separate encoding of the segment is independent of the separate encoding of other segments. Separate decoding may be applied to segments of such a codeword to decode convolutionally encoded input bits corresponding to the separately encoded segments of the convolutionally encoded input bits.

Abstract: Methods and apparatus for scheduling uplink transmissions based on contributiveness to a downstream task are provided. Multiple devices to schedule for uplink transmission are selected from a set of candidate devices based on a contributiveness metric for each device. The contributiveness metric for each device is related to a downstream task in the wireless communication network and is indicative of how well the device is able to successfully transmit information to the network for the downstream task and how informative the information provided by the device is for the downstream task. The contributiveness metric of a candidate device may be learned via machine learning using a deep neural network.

Abstract: An apparatus for feature-based communications is provided that includes a probabilistic encoder and a transmitter. The probabilistic encoder is configured to encode source information into a set of probability distributions over a latent space. Each probability distribution represents one or more aspects of a subject of the source information. The transmitter is configured to transmit over a transmission channel, to a receiving electronic device, a set of transmission features representing the subject. Each transmission feature provides information about a respective one of the probability distributions in the latent space. The probabilistic encoder is configured to enforce constraints on distribution parameters of the probability distributions over the latent space based on a condition of the transmission channel. Enforcing constraints on the latent space in this manner enables the apparatus to transmit features that are at least as unreliable as the transmission channel.

Abstract: A codeword is generated based on a segmentation transform and a Polarization-Assisted Convolutional (PAC) code that includes an outer convolutional code and a polar code, and based on separate encoding of respective different segments of convolutionally encoded input bits according to the polar code. Each segment of the respective segments includes multiple bits of the convolutionally encoded input bits for which the separate encoding of the segment is independent of the separate encoding of other segments. Separate decoding may be applied to segments of such a codeword to decode convolutionally encoded input bits corresponding to the separately encoded segments of the convolutionally encoded input bits.

Abstract: A codeword is generated based on a segmentation transform and a Polarization-Assisted Convolutional (PAC) code that includes an outer convolutional code and a polar code, and based on separate encoding of respective different segments of convolutionally encoded input bits according to the polar code. Each segment of the respective segments includes multiple bits of the convolutionally encoded input bits for which the separate encoding of the segment is independent of the separate encoding of other segments. Separate decoding may be applied to segments of such a codeword to decode convolutionally encoded input bits corresponding to the separately encoded segments of the convolutionally encoded input bits.

Abstract: A codeword is generated based on a segmentation transform and a Polarization-Assisted Convolutional (PAC) code that includes an outer convolutional code and a polar code, and based on separate encoding of respective different segments of convolutionally encoded input bits according to the polar code. Each segment of the respective segments includes multiple bits of the convolutionally encoded input bits for which the separate encoding of the segment is independent of the separate encoding of other segments. Separate decoding may be applied to segments of such a codeword to decode convolutionally encoded input bits corresponding to the separately encoded segments of the convolutionally encoded input bits.