A signal transmission apparatus (1) includes: a distortion compensation unit (11) for performing a distortion compensation processing on an input signal (x) by using a Neural Network (112) including L+1 arithmetic layers that include L (L is a variable number representing an integer equal to or larger than 1) hidden layer (112M) and an output layer (112O); a storage unit (13) for storing parameter sets (131) each of which includes a parameter for Q (Q is a variable number representing an integer equal to or smaller than L) arithmetic layer of the L+1 arithmetic layers; and an application unit (142) for selecting one parameter set from the parameter sets based on a signal pattern of the input signal and applying the parameter included in the selected one parameter set to the M number of arithmetic layer, a parameter of another arithmetic layer of the L+1 arithmetic layers, which is other than the Q arithmetic layer, is fixed.

Aspects of the disclosure relate to determining channel state information (CSI) on a component carrier. In an example operation, a device determines a mapping between first time-frequency resources corresponding to a first component carrier (CC) and second time-frequency resources corresponding to a second CC using a prediction algorithm. The device receives, from a base station, a channel state information reference signal (CSI-RS) on the first time-frequency resources corresponding to the first CC and measures first CSI on the first time-frequency resources corresponding to the first CC based on the received CSI-RS. The device further predicts second CSI on the second time-frequency resources corresponding to the second CC based on the measured first CSI using the prediction algorithm. The device then generates a CSI report based on the predicted second CSI and sends the CSI report to the base station.

Systems, methods, and devices to reduce the channel estimation overhead by collecting data from many UEs and building a location-based mathematical model are disclosed. During building of the model, a reference signal is used to collect location- and signal-related data from connected UEs. Once the model is successfully built, it is then transmitted and/or downloaded to each new UE that connects to the base station. The UEs and/or the base stations then use this model to determine their own transmission parameter values. The UEs also report their location to the base stations, which use the model to estimate channel conditions and adapt transmission parameters for themselves.

Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may receive a request to report updates for one or more weights of a neural network configured for encoding channel state information feedback messages. The first device may transmit a report that indicates the updates for the one or more weights. Numerous other aspects are provided.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may select, based at least in part on a classifier, an autoencoder of a set of autoencoders to be used for encoding an observed wireless communication vector to generate a latent vector. The client may transmit the latent vector and an indication of the autoencoder. Numerous other aspects are provided.

A pilot information symbol sending method, a channel estimation method, and a communications device. The method includes: determining, based on a discrete Fourier transform DFT matrix and a sensing matrix, a pilot information symbol corresponding to each antenna on each pilot resource; and sending a corresponding pilot information symbol on each of the pilot resources for each of the antennas; where the sensing matrix is determined through training of channel information.

A method for automatically identifying an impairment of a communication medium includes (1) obtaining a spectrum response of communication signals traveling through the communication medium, (2) converting the spectrum response from a frequency domain to a time domain, to generate an impulse response, and (3) identifying the impairment of the communication medium at least partially based on one or more characteristics of the impulse response.

A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.

A method performed by a radio unit for handling a number of received radio signals over an array of antennas comprised in the radio unit. The radio unit transforms the number of received radio signals into a number of sequences of complex symbols. The radio unit further filters the number of sequences of complex symbols by inputting the number of sequences of complex symbols into a trained computational model comprising an alternating sequence of linear and nonlinear functions and thereby obtaining a reduced number of sequences. The radio unit further transmits the reduced number of sequences to a baseband unit over a front-haul link.