Abstract: According to an example, a massive multiple-input multiple-output (mMIMO) system operable for beamforming actuation may comprise an open radio access network radio unit (O-RU) having a processing device operable to: compute one or more of a predictive channel estimate, a beamforming estimate, or an antenna calibration; compute, based on an objective function having one or more of the predictive channel estimate, the beamforming estimate, or the antenna calibration as inputs, a power level for a data stream associated with a transmitting antenna path, wherein the power level for the data stream is selected to facilitate an equal-power distribution across a plurality of transmitting antenna paths; and set the power level for the data stream associated with the transmitting antenna path.

Abstract: One or more computer processors create a fully convolution network (FCN) comprising a plurality of 1×1 convolutions. The one or more computer processors append linear mapping layer (LM) to created FCN. The one or more computer processors capture a plurality of features utilizing multi-scale dilated convolutional kernels from the linear mapped FCN (LM-FCN). The one or more computer processors apply an average pool layer to the captured plurality of features along a temporal axis of a dilated convolutional kernel within the LM-FCN. The one or more computer processors predict a classification for subsequent time-series data utilizing the pooled plurality of features.

Abstract: A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

Abstract: According to an example, a massive multiple-input multiple-output (mMIMO) system operable for beamforming actuation may comprise an open radio access network radio unit (O-RU) having a processing device operable to: compute one or more of a predictive channel estimate, a beamforming estimate, or an antenna calibration; compute, based on an objective function having one or more of the predictive channel estimate, the beamforming estimate, or the antenna calibration as inputs, a power level for a data stream associated with a transmitting antenna path, wherein the power level for the data stream is selected to facilitate an equal-power distribution across a plurality of transmitting antenna paths; and set the power level for the data stream associated with the transmitting antenna path.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first signal at a first port of a communication system. The first signal may include a combination of an incident signal and a reflected signal. The method may include performing a first processing to the first signal. In response to the first processing, the method may include performing a second processing to the first signal. The method may include estimating a voltage standing wave ratio (VSWR) associated with a transmission line from results of the second processing to the first signal.

Abstract: According to an aspect of an embodiment, a base station configured for beamforming estimation in a massive multiple input multiple output (mMIMO) radio access network (RAN) (mMIMO-RAN) may comprise a processing device and a transceiver. The processing device may be configured to obtain a channel estimate for a user equipment (UE). The processing device may be configured to compute a first power level adjustment for a downlink (DL) signal and a second power level adjustment for the DL signal. The second power level adjustment may be based on a power constraint. The transceiver may be configured to transmit the DL signal to the UE.

Abstract: According to an aspect of an embodiment, a base station configured for beamforming estimation in a massive multiple input multiple output (mMIMO) radio access network (RAN) (mMIMO-RAN) may comprise a processing device and a transceiver. The processing device may be configured to obtain a channel estimate for a user equipment (UE). The processing device may be configured to compute a first power level adjustment for a downlink (DL) signal and a second power level adjustment for the DL signal. The second power level adjustment may be based on a power constraint. The transceiver may be configured to transmit the DL signal to the UE.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first signal at a first port of a communication system. The first signal may include a combination of an incident signal and a reflected signal. The method may include performing a first processing to the first signal. In response to the first processing, the method may include performing a second processing to the first signal. The method may include estimating a voltage standing wave ratio (VSWR) associated with a transmission line from results of the second processing to the first signal.

Abstract: Obtain, using at least one hardware processor, data characterizing a physical system governed by a physical conservation law. Apply, using the at least one hardware processor, contrastive learning to the data to automatically capture system invariants of the physical system. Employ, using the at least one hardware processor, a neural projection layer to guarantee that a corresponding dynamic machine learning model preserves the captured system invariants. Optionally, predict performance of the physical system using the corresponding dynamic machine learning model.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first signal at a first port of a communication system. The first signal may include a combination of an incident signal and a reflected signal. The method may include performing a first processing to the first signal. In response to the first processing, the method may include performing a second processing to the first signal. The method may include estimating a voltage standing wave ratio (VSWR) associated with a transmission line from results of the second processing to the first signal.

Abstract: According to an aspect of an embodiment, a base station configured for beamforming estimation in a massive multiple input multiple output (mMIMO) radio access network (RAN) (mMIMO-RAN) may comprise a processing device and a transceiver. The processing device may be configured to compute, at the base station, a precoder based on jointly maximizing the signal with respect to noise and minimizing an interference with respect to the noise using an approximate decoder. The processing device may be configured to compute, at the base station, the precoder based on a power constraint. The processing device may be configured to generate, at the base station, the precoder for precoding a downlink signal for transmission from the base station to a user equipment (UE). The transceiver may be configured to transmit the DL signal to the UE.

Abstract: According to an example, a massive multiple-input multiple-output (mMIMO) system operable for beamforming actuation may comprise an open radio access network radio unit (O-RU) having a processing device operable to: compute one or more of a predictive channel estimate, a beamforming estimate, or an antenna calibration; compute, based on an objective function having one or more of the predictive channel estimate, the beamforming estimate, or the antenna calibration as inputs, a power level for a data stream associated with a transmitting antenna path, wherein the power level for the data stream is selected to facilitate an equal-power distribution across a plurality of transmitting antenna paths; and set the power level for the data stream associated with the transmitting antenna path.

Abstract: One or more computer processors create a fully convolution network (FCN) comprising a plurality of 1×1 convolutions. The one or more computer processors append linear mapping layer (LM) to created FCN. The one or more computer processors capture a plurality of features utilizing multi-scale dilated convolutional kernels from the linear mapped FCN (LM-FCN). The one or more computer processors apply an average pool layer to the captured plurality of features along a temporal axis of a dilated convolutional kernel within the LM-FCN. The one or more computer processors predict a classification for subsequent time-series data utilizing the pooled plurality of features.

Abstract: Providing a trained reinforcement learning (RL) model by formulating a decision process problem for the RL model, defining at least one of a logarithmic loss function for the RL model and defining an initiation point for the RL model according to an optimized spectral norm of the RL model, training the system according to the logarithmic loss function or from the initiation point, and providing the trained RL model.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first signal at a first port of a communication system. The first signal may include a combination of an incident signal and a reflected signal. The method may include performing a first processing to the first signal. In response to the first processing, the method may include performing a second processing to the first signal. The method may include estimating a voltage standing wave ratio (VSWR) associated with a transmission line from results of the second processing to the first signal.

Abstract: Disclosed are systems, methods, and other implementations that include a machine-implemented artificial neural network including a plurality of nodes, with the nodes forming at plurality of layers including an input layer, at least one hidden layer, and an output layer, and a plurality of links, with each link coupling a corresponding source node and a receiving node. At least one link is configured to evaluate a piecewise linear function of a value provided by a first source node, from the plurality of nodes, to yield a value for a first receiving node coupled to the at least one link. Each node of a hidden layer is configured to aggregate values provided by links for which that node is the receiving node of the link, with the first receiving node providing non-linear output resulting, in part, from the at least one link configured to evaluate the piecewise linear function.

Abstract: A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.

Abstract: Disclosed are systems, devices, modules, methods, and other implementations, including a method for digital predistortion that includes receiving, by a digital predistorter, a first signal that depends on amplitude variations based on an input signal, u, with the variations of the first signal corresponding to time variations in non-linear characteristics of a transmit chain that includes a power amplifier. The method further includes receiving, by the digital predistorter, the input signal u, generating, by the digital predistorter, based at least in part on signals comprising the input signal u and the first signal, a digitally predistorted signal v to mitigate the non-linear behavior of the transmit chain, and providing the predistorted signal v to the transmit chain.

Abstract: According to an aspect of an embodiment, a base station configured for beamforming estimation in a massive multiple input multiple output (mMIMO) radio access network (RAN) (mMIMO-RAN) may comprise a processing device and a transceiver. The processing device may be configured to obtain a channel estimate for a user equipment (UE). The processing device may be configured to compute a first power level adjustment for a downlink (DL) signal and a second power level adjustment for the DL signal. The second power level adjustment may be based on a power constraint. The transceiver may be configured to transmit the DL signal to the UE.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first signal at a first port of a communication system. The first signal may include a combination of an incident signal and a reflected signal. The method may include performing a first processing to the first signal. In response to the first processing, the method may include performing a second processing to the first signal. The method may include estimating a voltage standing wave ratio (VSWR) associated with a transmission line from results of the second processing to the first signal.