Method of non-iterative singular-value decomposition (SVD). The method includes receiving, by receiver, a signal; determining, by a channel matrix generator connected to the receiver, a channel matrix for the received signal; reducing, by a singular-value decomposer connected to the channel matrix generator, the dimension of the channel matrix; performing, by the singular-value decomposer, an SVD on the dimension-reduced channel matrix to determine singular vectors and corresponding coefficients that maximize singular values of the singular vectors; and outputting a result of the SVD based on at least one of when the dimension of the dimension-reduced channel matrix is less than or equal to 2 and when two greatest singular values of corresponding singular vectors are determined.

The embodiments herein use a factorization based technique for determining filter coefficients for a subset of the subcarriers in a wireless frequency band. Once the filter coefficients for the subset of the subcarriers are calculated, the network device uses these filter coefficients to identify the filter coefficients in a neighboring subcarrier. To do so, the network device uses pseudo-inverse iteration to convert the already calculated filter coefficients into filter coefficients for a neighboring subcarrier. The network device can repeat this process for the next set of neighboring subcarriers until all the filter coefficients have been calculated.

Method for non-iterative singular-value decomposition (SVD). The method includes receiving, by receiver, signal; determining, by channel matrix generator, channel matrix for received signal, where channel matrix has dimension N.sub.rx?N.sub.tx, N.sub.rx is number of receive antennas, N.sub.tx is integer indicating number of transmit antennas; reducing, by singular-value decomposer, dimension of channel matrix to min(N.sub.rx,N.sub.tx)?min(N.sub.rx,N.sub.tx), where min( ) is function that returns coefficient with minimum value; performing, by singular-value decomposer, SVD on dimension-reduced channel matrix to determine singular vectors and corresponding coefficients that maximize singular values of singular vectors; outputting result of SVD based on at least one of when dimension of dimension-reduced channel matrix is less than or equal to 2 and when two greatest singular values of corresponding singular vectors are determined; when result of SVD not output, subtracting, by singular-value decomposer, singular vectors from dimension-reduced channel matrix to reduce rank and returning to performing SVD.

Embodiments of this application disclose a channel estimation method and apparatus, and relate to the field of communications technologies, to help reduce indication overheads. The method may include: generating and sending indication information, where the indication information is used to indicate M N-dimensional precoding vectors, each precoding vector is applied to one of M frequency bands, the M N-dimensional precoding vectors form a space-frequency matrix, and the space-frequency matrix is generated by performing weighted combination on a plurality of space-frequency component matrices, where the space-frequency matrix is an M?N-dimensional space-frequency vector or an X?Y space-frequency matrix, X and Y are one and the other of M and N, M?1, N24 2, and both M and N are integers.

The embodiments herein use a factorization based technique for determining filter coefficients for a subset of the subcarriers in a wireless frequency band. Once the filter coefficients for the subset of the subcarriers are calculated, the network device uses these filter coefficients to identify the filter coefficients in a neighboring subcarrier. To do so, the network device uses pseudo-inverse iteration to convert the already calculated filter coefficients into filter coefficients for a neighboring subcarrier. The network device can repeat this process for the next set of neighboring subcarriers until all the filter coefficients have been calculated.

A system and method for MIB estimation including generating a signal model for rank=2, based on the reference signals of a received wireless signal; converting the signal model to a four-parameter representation; determining, for values of parameters derived from the four-parameter representation, whether mutual information per bit (MIB) values depend on a single parameter or on a plurality of parameters; if the MIB values depend on the single parameter, calculating MIB values based on the single parameter; and if the MIB values depend on the plurality of parameters, calculating MIB values based on the plurality of parameters. Calculating MIB values based on the single parameter, determining, whether MIB values depend on a single parameter or on a plurality of parameters and, calculating MIB values based on the plurality of parameters, are performed using a machine learning algorithm.

The present disclosure generally relates to the field of Faster-Than-Nyquist Signaling More specifically, the present disclosure relates to a technique of supporting Faster-Than-Nyquist transmission of data in a Multiple Input Multiple Output environment. A method embodiment comprises: forming two or more spatial data streams from data to be transmitted in the MIMO environment; partitioning a frequency band available for transmission of the data in the MIMO environment over the two or more spatial data streams into two or more sub-bands; and processing each of the two or more spatial data streams using FTN sampling.

A method and system for performing quadrature amplitude modulation (QAM) decoding of a received signal includes finding for each layer a region in a first constellation diagram of the received signal, the region including a portion of the first constellation diagram, the portion having the same size of a second constellation diagram, and a first constellation order of the received signal is higher than a second constellation order of the second constellation diagram; and, for each layer: finding a first portion of bits based on bits that are constant among constellation points located in the region of the layer; decoding the received signal using a QAM decoder having the second constellation order to obtain a second portion of bits; adjusting the second portion of bits based on the region of the layer; and merging the first portion of bits with the second portion of bits to obtain a decoded symbol.

Embodiments of the invention provides a decoder for decoding a signal received through a transmission channel in a communication system, said signal carrying information symbols selected from a given alphabet and being associated with a signal vector, said transmission channel being represented by a channel matrix, wherein said decoder comprises: a sub-block division unit (301) configured to divide the received signal vector into a set of sub-vectors in correspondence with a division of a matrix related to said channel matrix; a candidate set estimation unit (305) for recursively determining candidate estimates of sub-blocks of the transmitted signal corresponding to said sub-vectors, each estimate of a given sub-block being determined from at least one candidate estimate of the previously processed sub-blocks, wherein said candidate set estimation unit is configured to determine a set of candidate estimates for at least one sub-block of the transmitted signal by applying at least one iteration of a decoding algorithm using the estimates determined for the previously processed sub-blocks, the number of candidate estimates determined for said sub-block being strictly inferior to the cardinal of the alphabet and superior or equal to two, the decoder further comprising a signal estimation unit (306) for calculating an estimate of the transmitted signal from said candidate estimates determined for said sub-blocks.

The embodiments herein use a factorization based technique for determining filter coefficients for a subset of the subcarriers in a wireless frequency band. Once the filter coefficients for the subset of the subcarriers are calculated, the network device uses these filter coefficients to identify the filter coefficients in a neighboring subcarrier. To do so, the network device uses pseudo-inverse iteration to convert the already calculated filter coefficients into filter coefficients for a neighboring subcarrier. The network device can repeat this process for the next set of neighboring subcarriers until all the filter coefficients have been calculated.