Disclosed is a device for estimating a channel for communication system, which includes: a construction module designed to construct, on the basis of a physical model, a set of vectors) associated with a plurality of values of at least one parameter; an initialization module designed to initialize, as a function of the constructed vectors), columns of weighting coefficients defining at least a part of an artificial neural network; an application module designed to apply, as an input to the part of the artificial neural network, a vector determined as a function of noisy values so as to produce as an output a vector including estimated values; and a module for updating the weighting coefficients of the part of the artificial neural network by a learning technique. An associated method and computer program are also described.

Methods, systems, and devices for wireless communications are described. Generally, the described techniques at a user equipment (UE) provide for efficiently reporting channel state information (CSI) to a base station with an appropriate level of accuracy. In particular, the base station may indicate a level of accuracy to the UE for reporting CSI. The UE may encode the CSI using a first neural network, and the base station may decode the CSI using a second neural network. The first and second neural networks may form a neural network pair, and the UE may train the neural network pair based on the level of accuracy indicated by the base station. For example, the base station may indicate a loss function corresponding to a level of accuracy with which CSI is to be reported by the UE, and the UE may train the neural network pair using the loss function.

A method may include generating an estimated time offset based on a reference signal in a communication system, and adjusting a transform window in the communication system based on the estimated time offset, wherein the estimated time offset is generated based on machine learning. Generating the estimated time offset may include applying the machine learning to one or more channel estimates. Generating the estimated time offset may include extracting one or more features from one or more channel estimates, and generating the estimated time offset based on the one or more features. Extracting the one or more features may include determining a correlation between a first channel and a second channel. The correlation may include a frequency domain correlation between the first channel and the second channel. Extracting the one or more features may include extracting a subset of a set of features of the one or more channel estimates.

A method for implementing uplink and downlink channel reciprocity, a communication node, and a storage medium are provided. The method for implementing uplink and downlink channel reciprocity includes: performing channel estimation in real time according to a received reference signal to obtain a channel estimation result; inputting the channel estimation result into a pre-trained neural network model, to output a channel adaptive matrix; and performing channel adaptation processing on a transmission signal by applying the channel adaptive matrix to the transmission signal.

Systems and methods for Wi-Fi sensing are provided. A method for Wi-Fi sensing carried out by a sensing decision unit in operation on at least one processor configured to execute instructions. Measured channel state information (M-CSI) representing a sensing measurement and receiver front end state information (RFE-SI) are received. According to the RFE-SI, sensing decision input information is determined.

Examples described herein include methods, devices, and systems which may compensate input data for I/Q imbalance or noise related thereto to generate compensated input data. In doing such the above compensation, during an uplink transmission time interval (TTI), a switch path is activated to provide converted input data to a receiver stage including a recurrent neural network (RNN). The RNN may calculate an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the I/Q imbalance. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the converted input data may also be transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path may be deactivated and the receiver stage may receive an additional RF wireless transmission to be processed in the receiver stage.

We generally describe an apparatus (200, 300) for user plane traffic quality analysis in a wireless communication network, the apparatus (200, 300) comprising: an interface configured to be coupled to a user plane probe (214) arranged on a bidirectional user plane traffic flow path of user plane traffic flowing through the wireless communication network between a first terminal (202) and a second terminal (204) of the wireless communication network, and an estimation unit (706) coupled to the interface, wherein the estimation unit (706) is configured to estimate, based on a probing, by the user plane probe (214), of the user plane traffic flowing in a first direction (304) in a first segment (301) of the user plane traffic flow path from the first terminal (202) to the user plane probe (214), a user plane traffic quality of the user plane traffic flowing in a second direction (310) in the first segment (301) from the user plane probe (214) to the first terminal (202), wherein the first direction (304) is opposite to the second direction (310), and wherein the user plane traffic is not testable via the user plane probe (214) in the second direction (310).

A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.

Examples described herein include methods, devices, and systems which may compensate input data for I/Q imbalance or noise related thereto to generate compensated input data. In doing such the above compensation, during an uplink transmission time interval (TTI), a switch path is activated to provide converted input data to a receiver stage including a recurrent neural network (RNN). The RNN may calculate an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the I/Q imbalance. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the converted input data may also be transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path may be deactivated and the receiver stage may receive an additional RF wireless transmission to be processed in the receiver stage.

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.