Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform both a channel estimation and a non-linearity estimation (e.g., a power amplifier (PA) model estimation) on a demodulation reference signal (DMRS) transmitted by a base station. For example, the DMRS may include two portions that correspond to two peak-to-average power ratio (PAPR) values. Low PAPR values may enable the UE to perform the channel estimation, and high PAPR values may enable the UE to perform the PA non-linearity estimation. Accordingly, a first portion of the DMRS may correspond to a lower PAPR value, and the second portion of the DMRS may correspond to a higher PAPR value. In some examples, the base station may signal different parameters for each portion of the DMRS to the UE, or the UE may use fixed values to receive each portion of the DMRS.

One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform both a channel estimation and a non-linearity estimation (e.g., a power amplifier (PA) model estimation) on a demodulation reference signal (DMRS) transmitted by a base station. For example, the DMRS may include two portions that correspond to two peak-to-average power ratio (PAPR) values. Low PAPR values may enable the UE to perform the channel estimation, and high PAPR values may enable the UE to perform the PA non-linearity estimation. Accordingly, a first portion of the DMRS may correspond to a lower PAPR value, and the second portion of the DMRS may correspond to a higher PAPR value. In some examples, the base station may signal different parameters for each portion of the DMRS to the UE, or the UE may use fixed values to receive each portion of the DMRS.

A distributed unit (DU) may include a transceiver configured to communicate with a plurality of radio units (RUs) that are configured to serve a plurality of user equipments (UEs). The DU may include a processor configured to determine RU precoding parameters for UEs served by a first RU set from the plurality of RUs based on estimated channel parameters for communication channels between the first RU set and at least one of interfering UEs served by other RUs from the plurality of RUs; to encode information indicating the determined precoding parameters for downlink transmissions to the first RU set and determine DU precoding parameters for downlink transmissions to the UEs served by the first RU set based on the determined RU precoding parameters; and/or precode communication signals based on the determined DU precoding parameters.

An operation method of a first receiving node in a distributed communication system may comprise: receiving a signal from a transmitting node; extracting a combined signal vector from a reception signal vector corresponding to a vector of the received signal; obtaining a compressed combined signal vector by extracting a preset number T of combined signal elements from among a plurality of combined signal elements constituting the combined signal vector; quantizing the compressed combined signal vector to obtain a quantized combined signal vector; and transmitting the quantized combined signal vector to a second receiving node included in the distributed communication system.

A physical layer transceiver for connecting a host device to a wireline channel medium includes a host interface for coupling to the host device, a line interface for coupling to the channel medium, a transmit path operatively coupled to the host interface and the line interface, a receive path operatively coupled to the line interface and the host interface, and adaptive filter circuitry operatively coupled to at least one of the transmit path and the receive path for filtering signals on the at least one of the transmit path and the receive path, the adaptive filter circuitry including a non-linear equalizer. The non-linear equalizer may be a neural network equalizer based on a multi-layer perceptron or a radial-basis function, or may be a linear equalizer with a non-linear activation function. The non-linear equalizer also may have a front-end filter to reduce input complexity.

One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

An operation method of a first receiving node in a distributed communication system may comprise: receiving a signal from a transmitting node; extracting a combined signal vector from a reception signal vector corresponding to a vector of the received signal; obtaining a compressed combined signal vector by extracting a preset number T of combined signal elements from among a plurality of combined signal elements constituting the combined signal vector; quantizing the compressed combined signal vector to obtain a quantized combined signal vector; and transmitting the quantized combined signal vector to a second receiving node included in the distributed communication system.

Techniques, systems, architectures, and methods for providing improved feature detection of signals, especially those in relatively high interference regions, thereby allowing for earlier and longer range detection of communications and radar signals are herein provided. The techniques utilize a general framework of total variation denoising, where signals are assumed to be sparse in a combination of their first or higher order derivatives, to increase signal-to-interference ratio, which is followed by cyclostationarity detection, which is used to estimate signal features, including the period of the signals of interest and their modulation type.

One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.