A method of updating spatial filters in a radio network comprising at least two transmit nodes each in radio communication with at least one receive node on a Multiple-Input Multiple-Output (MIMO) radio channel, comprises transmitting, from the respective transmit node, first reference signals precoded by a first spatial filter of the respective transmit node; and receiving, from the receive nodes, second reference signals that are precoded using a second spatial filter of the respective receive node and an error matrix of the respective receive node. The second spatial filter depends on a channel estimate based on the transmitted first reference signals. The error matrix is indicative of an error of the first and second spatial filters in equalizing the MIMO radio channel. The method further comprises recomputing, for each of the at least one receive node in radio communication with the respective transmit node, the error matrix of the respective receive node; and updating the first spatial filter of the respective transmit node using the recomputed error matrix.

Apparatus, methods, and computer program products for improving precoding downlink signaling are provided. An example method may include determining one or more noise covariance parameters for a noise covariance report, the noise covariance parameters being associated with a noise covariance matrix for whitening. The example method further may include transmitting, to a base station, the noise covariance report including the one or more noise covariance parameters or a noise whitening matrix for the whitening, the noise covariance report being transmitted based on a transmission periodicity of one or more slots.

Methods and apparatuses for IQ time skew calibration in a coherent transceiver are described. A four-channel signal is received. A set of inputs is constructed for a 4×8 MIMO equalizer by converting the four-channel signal into four complex inputs that each have a phase shift corresponding to an estimated carrier frequency offset. The set of inputs further includes conjugate replicas of the four complex inputs. Using output from the 4×8 MIMO equalizer, equalizer coefficients are calculated by minimizing error between the MIMO output and a reference signal. Receiver and transmitter IQ skew are estimated using the equalizer coefficients, by converting the equalizer coefficients form the time domain to the frequency domain to determine receiver and transmitter IQ differential phase responses, which are indicative of respective receiver and transmitter IQ skew in the time domain. Skew compensation is then performed.

Apparatuses, methods, and systems are disclosed for calculating an EVM of a transmitter. An apparatus includes a transceiver that receives, using an unbiased linear minimum mean square error (“MMSE”) equalizer, a transmission signal transmitted via a propagation channel, the signal generated and transmitted using an antenna port at a transmitter, the antenna port comprising a plurality of antennas (N) and an antenna connector for each of the plurality of antennas. The apparatus includes a processor that determines an EVM for the antenna port of the transmitter based on an output of the unbiased linear MMSE equalizer.

Apparatuses, systems, and techniques to perform signal processing operations in a fifth generation (“5G”) radio signal. In at least one embodiment, one or more processors equalize, in parallel, one or more 5G radio signals.

Finite-alphabet beamforming for multi-antenna wideband systems is provided. The combination of massive multi-user multiple-input multiple-output (MU-MIMO) technology and millimeter-wave (mmWave) communication enables unprecedentedly high data rates for radio frequency (RF) communications. In such systems, beamforming must be performed at extremely high rates over hundreds of antennas. For example, spatial equalization applies beamforming in the uplink to mitigate interference among user equipment (UEs) at a base station (BS). Finite-alphabet equalization provides a new paradigm that restricts the entries of a spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost equalization hardware. Similarly, precoding applies beamforming in the downlink to maximize the reception of a signal transmitted from a BS to a target UE. Finite-alphabet precoding can be applied in the downlink to similarly improve power and cost in precoding hardware.

Finite-alphabet beamforming for multi-antenna wideband systems is provided. The combination of massive multi-user multiple-input multiple-output (MU-MIMO) technology and millimeter-wave (mmWave) communication enables unprecedentedly high data rates for radio frequency (RF) communications. In such systems, beamforming must be performed at extremely high rates over hundreds of antennas. For example, spatial equalization applies beamforming in the uplink to mitigate interference among user equipment (UEs) at a base station (BS). Finite-alphabet equalization provides a new paradigm that restricts the entries of a spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost equalization hardware. Similarly, precoding applies beamforming in the downlink to maximize the reception of a signal transmitted from a BS to a target UE. Finite-alphabet precoding can be applied in the downlink to similarly improve power and cost in precoding hardware.

Embodiments of the present disclosure are related, in general to communication, but exclusively relate to method and receiver for detecting data in a communication network. The method comprises transforming by a receiver, a received signal in to frequency domain to generate transformed signal. Also, the method comprises equalizing the transformed signal to obtain an estimated precoded signal, which is transformed using inverse Fourier transform to obtain a time domain signal. The time domain signal is de-rotated to produce de-rotated data, on which processing is performed by separating real part and imaginary part associated with the de-rotated signal. The real part and the imaginary parts are filtered and combined to produce a signal, that is demodulated to detect the signal.

A method of updating spatial filters in a radio network comprising at least two transmit nodes each in radio communication with at least one receive node on a Multiple-Input Multiple-Output (MIMO) radio channel, comprises transmitting, from the respective transmit node, first reference signals precoded by a first spatial filter of the respective transmit node; and receiving, from the receive nodes, second reference signals that are precoded using a second spatial filter of the respective receive node and an error matrix of the respective receive node. The second spatial filter depends on a channel estimate based on the transmitted first reference signals. The error matrix is indicative of an error of the first and second spatial filters in equalizing the MIMO radio channel. The method further comprises recomputing, for each of the at least one receive node in radio communication with the respective transmit node, the error matrix of the respective receive node; and updating the first spatial filter of the respective transmit node using the recomputed error matrix.

Apparatuses, systems, and methods are disclosed for stochastic linear detection. A digital signal processor determines information about a plurality of transmitted signals based on a plurality of received signals. An initialization module determines an estimator matrix and a noise shaping matrix based on channel state information that relates the transmitted signals to the received signals. A sample generation module stochastically generates a plurality of signal estimates so that each signal estimate is a sum of a fixed component and a random component. The fixed component may be based on applying the estimator matrix to a vector of the received signals, and the random component may be based on applying the noise shaping matrix to generated noise. An output module sends soft information to an error-correcting code (ECC) decoder for decoding bits carried by the transmitted signals. The soft information may be based on the plurality of signal estimates.