Embodiments of the present application provide a signal transmission method, a terminal, a network device, and a storage medium. The method comprises: receiving multiple first downlink signals; determining downlink receiving frequency corresponding to the first downlink signal, and respectively determining, on the basis of the downlink receiving frequency corresponding to the first downlink signal, transmitting frequency of uplink signals in association with the first downlink signal; and transmitting the uplink signals on the basis of the transmitting frequency of the uplink signals, wherein a frequency shift determined on the basis of the uplink signals in association with the first downlink signal is used for determining transmitting frequency of second downlink signals. In the embodiments of the present application, a pre-compensation of a downlink Doppler shift is performed by means of each transmission receiving point, and therefore, an error of channel estimation is reduced, and the performance of downlink transmission is improved.

This document discloses a solution for computing beamforming coefficients for a radio channel between two apparatuses. According to an aspect, a method comprises: obtaining a channel matrix representing a radio channel between an apparatus and another apparatus; obtaining an initial eigenvector estimate for an eigenvector of the channel matrix; computing an intermediate eigenvector estimate based on the channel matrix and the initial eigenvector estimate; computing an error vector representing an error between the initial eigenvector estimate and the intermediate eigenvector estimate; computing a final eigenvector estimate based on the error vector and the intermediate eigenvector estimate; and determining beamforming coefficients based on the final eigenvector estimate and communicating with said another apparatus by using the beamforming coefficients.

A learning-based system and method for interference whitening method. In some embodiments, the method includes receiving a signal; extracting a first set of features from the signal; making a first selection, by a first neural network, based on the first set of features; and selecting a first covariance matrix, from a plurality of covariance matrices, based on the first selection.

The present disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, directed to configuration of a beamforming vector using an estimated covariance matrix, which may reduce latency commensurate with a beam training procedure. In some aspects, an apparatus may receive, from another apparatus, information indicating a configured subset of a set of TX beams at the other apparatus. The other apparatus may transmit pilot signals via the configured subset of the set of TX beams. The apparatus may transmit, to the other apparatus, an accumulated signal strength value associated with a set of signal strength values measured using received pilot signals. The apparatus may estimate a channel covariance matrix using the accumulated signal strength value, and may calculate a beamforming vector from the channel covariance matrix. The apparatus and the other apparatus may perform a beam training procedure using respective beamforming vectors.

A computer-implemented reconstruction method of discrete digital signal recovery in noisy overloaded wireless communication systems in the presence of hardware impairments that is characterized by a channel matrix of complex coefficients, the method including, receiving the signal from channel by a signal detector, estimation of hardware impairments parameter η is done at the receiver, estimation noise power is done by a noise power estimator, forwarding the detected signal and hardware impairments parameter η and noise power estimation to a decoder that estimates the transmitted symbol, wherein the estimation of the decoder produces a symbol that could probably have been transmitted it is forwarded to a de-mapper, which outputs the bit estimates corresponding to the estimated transmit signal and the corresponding estimated symbol to a microprocessor for further processing.

Disclosed are a method and an apparatus for transmitting and receiving channel state information in a wireless communication system. A method for transmitting channel state information (CSI) according to an embodiment of the present disclosure may comprise the steps of: receiving configuration information related to the CSI from a base station, wherein the configuration information includes information on a CSI-RS resource set; receiving a CSI-reference signal (CSI-RS) from the base station; and transmitting the CSI to the base station on the basis of the configuration information and the CSI-RS. The CSI may include a first CSI set based on a single CSI resource in the CSI-RS resource set and/or a second CSI set based on a CSI-RS resource combination in the CSI-RS resource set, and the number of CSI processing units (CPUs) required for calculation of the second CSI set and the number of CPUs required for calculation of the first CSI set may be individually determined.

An operating method of a communication device for providing a beamformed transmission signal to a plurality of terminals may include determining a target transmission vector based on an area restriction condition for each of the plurality of terminals, generating a beam selection matrix for selecting some of a plurality of antennas based on the target transmission vector and a beam selection condition, generating a precoding matrix based on the target transmission vector and the beam selection matrix, and generating a transmission signal based on the beam selection matrix and the precoding matrix.

Aspects of the subject disclosure may include, for example, determining a coherence block for each user equipment (UE) of a plurality of UEs being served by the first cell, resulting in a plurality of coherence blocks, responsive to the determining, identifying a smallest coherence block from the plurality of coherence blocks, identifying a pilot sequence length based on the smallest coherence block, determining a plurality of orthogonal pilot sequences based on the identifying the pilot sequence length, designating, from the plurality of orthogonal pilot sequences, a first group of orthogonal pilot sequences for use in the first cell, and distributing, to each neighboring cell of a plurality of neighboring cells adjacent to the first cell, a respective group of orthogonal pilot sequences from a remainder of the plurality of orthogonal pilot sequences, to prevent pilot contamination between the first cell and the plurality of neighboring cells. Other embodiments are disclosed.

The present disclosure relates to methods and devices for Channel State Information, (CSI) prediction. More particularly the disclosure pertains to predicting CSI for a dynamic channel that is varying over time, e.g. because the receiver is moving. This object is obtained by a method performed in a first wireless node of predicting CSI of a dynamic wireless channel H between the first wireless node and a second wireless node. The method comprises deriving channel covariance estimates C.sub.k(n), . . . , C.sub.k(n−M) of the dynamic wireless channel H, estimating one or more channel properties of the dynamic wireless channel H, wherein one of the estimated channel properties defines a spectrum spread of the dynamic wireless channel H, and determining a covariance prediction filter, based on the estimated one or more channel properties. The method further comprises predicting one or more channel covariance estimates Ĉ.sub.k(n+N|n) by applying the determined covariance prediction filter to the derived channel covariance estimates C.sub.k(n), . . . , C.sub.k(n−M) and calculating a predicted CSI using the predicted covariance estimates Ĉ.sub.k(n+N|n). Hence, this disclosure proposes predicting CSI by predicting channel covariance using a methodology which implies deriving optimal covariance prediction filters.

Introduced here are processes in which noise covariance is estimated across resource blocks that have similar distributions of noise. These processes result in more accurate estimation of noise occurring in a given channel, as accuracy can be improved by increasing the number of resource blocks being examined while also identifying and then filtering those resource blocks that are contaminated with interference. At a high level, these processes represent an automated approach to detecting imbalance between resource blocks with noise and resource blocks with interference in addition to noise and then forming clusters of resource blocks having similar characteristics to provide more samples that can be used in estimating noise covariance.