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.

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.

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.

To enable use of channel related information more suitable for propagation environment. A wireless communication apparatus according to an example aspect of the present invention includes: a memory storing instructions; and one or more processors configured to execute the instructions to: acquire correlation information regarding correlation between first channel related information generated through channel estimation for a first estimation period and second channel related information generated through channel estimation for one or more estimation periods before the first estimation period; and perform control for a statistic of channel related information, based on the correlation information.

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 as. 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.

A method for diminishing passive intermodulation (PIM) is provided. The method comprises: upsampling an uplink baseband modulated signal; upsampling a downlink baseband modulated signal; determining a time delay for the upsampled downlink baseband modulated signal; time delaying the upsampled downlink baseband modulated signal by the determined time delay; estimating a third order PIM distortion (PIMD) product by filtering the time delayed, upsampled downlink baseband modulated signal with a third order power series kernel; generating a first filtered signal by subtracting the estimated third order PIMD product from the upsampled downlink baseband modulated signal; estimating a Nth order PIMD product by filtering the time delayed, upsampled downlink baseband modulated signal with a Nth order power series kernel; generating a nth filtered signal by subtracting the estimated Nth order PIMD product from the n1th filtered signal; and downsampling the nth filtered signal.

To enable use of channel related information more suitable for propagation environment. A wireless communication apparatus according to an example aspect of the present invention includes: a memory storing instructions; and one or more processors configured to execute the instructions to: acquire correlation information regarding correlation between first channel related information generated through channel estimation for a first estimation period and second channel related information generated through channel estimation for one or more estimation periods before the first estimation period; and perform control for a statistic of channel related information, based on the correlation information.

A method for diminishing passive intermodulation (PIM) is provided. The method comprises: upsampling an uplink baseband modulated signal; upsampling a downlink baseband modulated signal; determining a time delay for the upsampled downlink baseband modulated signal; time delaying the upsampled downlink baseband modulated signal by the determined time delay; estimating a third order PIM distortion (PIMD) product by filtering the time delayed, upsampled downlink baseband modulated signal with a third order power series kernel; generating a first filtered signal by subtracting the estimated third order PIMD product from the upsampled downlink baseband modulated signal; estimating a Nth order PIMD product by filtering the time delayed, upsampled downlink baseband modulated signal with a Nth order power series kernel; generating a nth filtered signal by subtracting the estimated Nth order PIMD product from the n1th filtered signal; and downsampling the nth filtered signal.

A wireless communication system may use forward-forward learning to for end-to-end learning. A transmitter may pass a positive dataset and a negative dataset through each of its layers for model training. Each layer may correspond to a goodness function. The transmitter may send the positive dataset to a receiver. The receiver may generate a second positive dataset and a second negative dataset based on the positive dataset sent from the receiver. The receiver may train each of its layers using the second positive dataset and the second negative dataset.

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.