A channel estimation circuit (100) includes an input interface (110). The input interface (110) is configured to receive a plurality of pilot symbols from a communication channel. Furthermore, the channel estimation circuit (100) includes processing circuitry (120). The processing circuitry (120) is configured to generate a channel autocorrelation matrix and at least one channel cross-correlation vector. The generating of the channel autocorrelation matrix and the channel cross-correlation vector can be based on predetermined statistical information on the communication channel. Additionally, the processing circuitry (120) is configured to generate a subspace mapping for a subspace transformation based on the channel autocorrelation matrix. Additionally, the processing circuitry (120) is configured to generate a subspace transformed channel autocorrelation matrix, at least one subspace trans-formed channel cross-correlation vector, and a plurality of subspace transformed pilot symbols, by applying the subspace mapping to the channel autocorrelation matrix, the channel cross-correlation vector, and to the plurality of pilot symbols. Additionally, the processing circuitry (120) is configured to generate a plurality of subspace channel estimation filter coefficients based on the subspace transformed channel autocorrelation matrix and the sub-space transformed channel cross-correlation vector. Additionally, the processing circuitry (120) is configured to generate an estimate of at least one channel coefficient of the communication channel based on the subspace transformed pilot symbols and the subspace channel estimation filter coefficients. Furthermore, the channel estimation circuit (100) includes an output interface (150) configured to provide the estimate of the at least one chan-nel coefficient.

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.

Embodiments of this application provide a channel estimation method and apparatus, and relate to the field of communications technologies, to help reduce indication overheads. The method includes: generating and sending indication information, wherein 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, wherein the space-frequency matrix is an MN-dimensional space-frequency vector or an XY space-frequency matrix, X and Y are one and the other of M and N, M1, N2, and both M and N are integers.

Apparatuses, systems, and techniques to perform channel estimation on one or more signals. In at least one embodiment, channel estimation on one or more wireless signals is performed in parallel based on one or more frequencies of one or more signals.

This application provides an uplink multi-station channel estimation method, a station (STA), and an access point (AP), which can be applied to an uplink multi-user multiple-input multiple-output scenario. The uplink multi-station channel estimation method includes: a STA generating a frame including a first group of training sequences and a second group of training sequences, and sending the frame to the AP. The AP calculates a frequency offset value between the STA and the AP based on the received first group of training sequences and the received second group of training sequences. The AP performs channel estimation based on the calculated frequency offset value. According to the technical solutions provided in this application, the AP can more accurately learn of frequency offset values between a plurality of STAs and the AP. This improves channel estimation precision.

A plurality of circuit units of a matrix processor of a communication device are used to decompose a plurality of channel matrices, corresponding to a plurality of orthogonal frequency division multiplexing (OFDM) tones, over a plurality of cycles to determine matrix equalizer coefficients. Decomposing the plurality of channel matrices includes determining respective modes of operation for respective ones of the circuit units for respective ones of the cycles. The respective modes of operation are selected from a set of modes that includes at least one of a bypass mode for propagating input signals to output signals without altering the input signals and an idle mode for saving power when a particular circuit unit is not needed during a particular cycle. The respective circuit units are individually controlled to operate in the determined respective modes during the corresponding cycles. The determined matrix coefficients are then applied to received data signals.

Methods and apparatus for sub-block based architecture of Cholesky decomposition and channel whitening. In an exemplary embodiment, an apparatus is provided that parallel processes sub-block matrices (R.sub.00, R.sub.10, and R.sub.11) of a covariance matrix (R) to determine a whitening coefficient matrix (W). The apparatus includes a first LDL coefficient calculator that calculates a first whitening matrix W.sub.00, lower triangle matrix L.sub.00, and diagonal matrix D.sub.00 from the sub-block matrix R.sub.00, a first matrix calculator that calculates a lower triangle matrix L.sub.10 from the sub-block matrix R.sub.10 and the matrices L.sub.00 and D.sub.00, and a second matrix calculator that calculates a matrix X from the matrices D.sub.00 and L.sub.10. The apparatus also includes a matrix subtractor that calculates a matrix Z from the matrix X and the sub-block matrix R.sub.11, a second LDL coefficient calculator that calculates a third whitening matrix W.sub.11, lower triangle matrix L.sub.11, and a diagonal matrix D.sub.11 from the matrix Z, and a third matrix calculator that calculates a second whitening matrix W.sub.10 from the matrices L.sub.00, L.sub.10, L.sub.11, and D.sub.11.

Introduced here is at least one technique to better estimate interference at a receiver. The technique includes receiving a plurality of reference signals, which each have information indicative of noise. Thus, the technique further includes, for each reference signal, determining a noise estimation and determining a distance metric and log-likelihood ratio (LLR) of the noise estimation. Once the distance metric and LLR of each reference signal is determined, the receiver can determine a final LLR based on the distance metric and LLR of each reference signal. In this manner, a final LLR is determined. This technique can be applied by any device operating on MIMO technology.

This disclosure describes a receiver having equalization with noise de-whitening mitigation for wireless communication. An input port receives, via an antenna, a signal communicated over a wireless communication link, the signal comprising a noise component. Control circuitry performs zero forcing equalization of the received signal to generate a zero forcing equalization result signal. The zero forcing equalization causes de-whitening of the noise component by increasing a correlation among elements of the noise component. The control circuitry mitigates the de-whitening of the noise component by: determining a noise variance value based on channel properties of the wireless communication link, and modifying the zero forcing equalization result signal based on the noise variance value. The modified zero forcing equalization result signal is communicated, via an output port, to log-likelihood ratio (LLR) generation circuitry for LLR computation.

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.