Apparatuses, systems and methods are described including: converting generator inputs to a generator output vector using a generator, wherein the generator is a model of a channel of a data transmission system and wherein the generator comprises a generator neural network; selectively providing either the generator output vector or an output vector of the channel of the data transmission system to an input of a discriminator, wherein the discriminator comprises a discriminator neural network; using the discriminator to generate a probability indicative of whether the discriminator input is the channel output vector or the generator output vector; and training at least some weights of the discriminator neural network using a first loss function and training at least some weights of the generator neural network using a second loss function in order to improve the accuracy of the model of the channel.

An apparatus, method and computer program is described comprising receiving data at a receiver of a transmission system; using a receiver algorithm to convert data received at the receiver into an estimate of the first coded data, the receiver algorithm having one or more trainable parameters; generating an estimate of first data bits by decoding the estimate of the first coded data, said decoding making use of an error correction code of said encoding of the first data bits; generating a refined estimate of the first coded data by encoding the estimate of the first data bits; generating a loss function based on a function of the refined estimate of the first coded data and the estimate of the first coded data; updating the trainable parameters of the receiver algorithm in order to minimise the loss function; and controlling a repetition of updating the trainable parameters by generating, for each repetition, for the same received data, a further refined estimate of the first coded data, a further loss function and further updated trainable parameters.

Methods, systems, and devices for wireless communications are described. In some systems, a device, such as an internet of things (IoT) device, may include a configuration or software (e.g., in baseband) that is common for multiple applications of the device. In some aspects, the device may select a setting for at least some if not each of a set of parameters associated with or defining a device profile of the device based on an application of the device. The device may perform a mapping procedure to map the settings for the parameters associated with the device profile to one or more baseband configurations or baseband handles and the device may customize the baseband of the device using the one or more baseband configurations. As such, the device may operate or communicate using the baseband that is customized based on the device profile and application of the device.

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 systems and methods which include wireless devices and systems with examples of mixing input data delayed versions of at least a portion of the respective processing results with coefficient data specific to a processing mode selection. For example, a computing system with processing units may mix the input data delayed versions of respective outputs of various layers of multiplication/accumulation processing units (MAC units) for a transmission in a radio frequency (RF) wireless domain with the coefficient data to generate output data that is representative of the transmission being processed according to a wireless processing mode selection. In another example, such mixing input data with delayed versions of processing results may be to receive and process noisy wireless input data. Examples of systems and methods described herein may facilitate the processing of data for 5G wireless communications in a power-efficient and time-efficient manner.

The disclosure relates to a communication technique and a system for combining a 5G communication system with IoT technology to support a higher data rate after a 4G system. Based on 5G communication and IoT-related technologies, the disclosure may be applied to intelligent services such as smart homes, smart buildings, smart cities, smart or connected cars, healthcare, digital education, retail, and security and safety related services. The disclosure provides a method and apparatus that enable a communication device supporting full duplex to cancel the self-interference signal in the digital domain.

A detector coherent-receives a signal being transmitted from a transmitter. A filter group includes a plurality of filters connected in series along a signal path of a reception signal. The plurality of filters include a plurality of non-linear distortion compensation filters and one or more linear distortion compensation filters. A coefficient updating unit controls a filter coefficient of the plurality of non-linear distortion compensation filters and a filter coefficient of at least some of the linear distortion compensation filters. The coefficient updating unit adaptively controls the filter coefficient, by using an error back propagation method, based on a difference between an output signal being output from the filter group and a predetermined value of the output signal.

Embodiments of the present disclosure provide a method, device, and computer readable medium for channel equalization. The method comprises receiving, at a first device, a first signal from a second device via a plurality of subcarriers over a communication channel; sampling the first signal to obtain sampled symbols; and generating a second signal based on the obtained sampled symbols using a direct association between sampled symbols and payloads, the second signal indicating a payload of the first signal carried on an effective subcarrier of the plurality of subcarriers. Through the use of the direct association between sampled symbols and payloads, it is possible to achieve channel equalization in a less complicated, more reliable, and cost-effective manner, so as to extract the payload in the received signal.

In a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE), a method of a first terminal in a wireless communication system is disclosed and may include performing channel measurement, based on one or more first reference signals received from a base station; identifying channel distribution information between the first terminal and the base station, based on the measured channel; selecting one or more representative channel vectors (RCVs), based on the identified channel distribution information; generating one or more constellations corresponding to the selected one or more RCVs; transmitting constellation set information including the generated one or more constellations to the base station; and performing communication with the base station, based on the generated one or more constellations.

Systems, devices, and methods related to hybrid basis function, neural network-based digital predistortion (DPD) are provided. An example apparatus for a radio frequency (RF) transceiver includes a digital predistortion (DPD) actuator to receive an input signal associated with a nonlinear component of the RF transceiver and output a predistorted signal. The DPD actuator includes a basis-function-based actuator to perform a first DPD operation using a set of basis functions associated with a first nonlinear characteristic of the nonlinear component. The DPD actuator further includes a neural network-based actuator to perform a second DPD operation using a first neural network associated with a second nonlinear characteristic of the nonlinear component. The predistorted signal is based on a first output signal of the basis-function-based actuator and a second output signal of the neural network-based actuator.