24 results about "Mimo channel" patented technology

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

Some wireless communications systems modulate signals for transmission via a channel. Probabilistic shaping may utilize non-uniformly distributed modulation constellations to increase spectral efficiency. In some cases, wireless fading may occur in a multiple-input and multiple-output (MIMO) channel, which may cause a loss of the shaping gain from probabilistic shaping. Some examples of approaches to linear MIMO demodulation for probabilistic shaping using auxiliary dimensions are provided. A wireless device may receive information indicating a non-uniform probabilistic distribution of a modulation constellation for a MIMO signal. One or more symbols of the MIMO signal may be transmitted or received via a channel. The wireless device may demodulate the symbol based at least in part on a transformation of the modulation constellation to a subspace using dimensions associated with the non-uniform probabilistic distribution and a point of the transformed modulation constellation nearest to a projected symbol in the subspace.

The invention provides a multiple-input-multiple-output channel model construction method and device, network equipment and a medium, and relates to the technical field of wireless communication. The method comprises: according to at least one obtained model parameter, determining a spatial non-stationary matrix and a near-field spherical wave matrix corresponding to a multiple-input-multiple-output (MIMO) channel, the at least one model parameter being obtained based on a three-dimensional (3D) MIMO channel model; elements in the spatial non-stationary matrix are used for describing the visibility of clusters in the MIMO channel to antenna array elements, and the near-field spherical wave matrix is used for describing the influence of spherical wave propagation on signal amplitude and phase; and according to the spatial non-stationary matrix and the near-field spherical wave matrix, a target MIMO channel model is constructed, and the target MIMO channel model is used for simulating a channel coefficient of the MIMO channel. According to the scheme of the invention, the problem that the existing MIMO channel model is not suitable for the simulation test of a large-scale MIMO channel is solved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

In some implementations, a network test device may identify a beam pattern associated with a gNodeB. The network test device may select, from the beam pattern, a first location, wherein the first location is associated with a first layer of a user equipment (UE). The network test device may select, based on the beam pattern, a second location associated with a second layer of the UE, wherein the second location is selected from a set of candidate locations associated with the first layer. The network test device may create one or more multiple-input multiple-output (MIMO) channels based on selected layers. The network test device may use the one or more MIMO channels to test a multiple user MIMO (MU-MIMO) system in a simulation or emulation environment.

Some wireless communications systems modulate signals for transmission via a channel. Probabilistic shaping may utilize non-uniformly distributed modulation constellations to increase spectral efficiency. In some cases, wireless fading may occur in a multiple-input and multiple-output (MIMO) channel, which may cause a loss of the shaping gain from probabilistic shaping. Some examples of approaches to linear MIMO demodulation for probabilistic shaping using auxiliary dimensions are provided. A wireless device may receive information indicating a non-uniform probabilistic distribution of a modulation constellation for a MIMO signal. One or more symbols of the MIMO signal may be transmitted or received via a channel. The wireless device may demodulate the symbol based at least in part on a transformation of the modulation constellation to a subspace using dimensions associated with the non-uniform probabilistic distribution and a point of the transformed modulation constellation nearest to a projected symbol in the subspace.

Some wireless communications systems modulate signals for transmission via a channel. Probabilistic shaping may utilize non-uniformly distributed modulation constellations to increase spectral efficiency. In some cases, wireless fading may occur in a multiple-input and multiple-output (MIMO) channel, which may cause a loss of the shaping gain from probabilistic shaping. Some examples of approaches to linear MIMO demodulation for probabilistic shaping using auxiliary dimensions are provided. A wireless device may receive information indicating a non-uniform probabilistic distribution of a modulation constellation for a MIMO signal. One or more symbols of the MIMO signal may be transmitted or received via a channel. The wireless device may demodulate the symbol based at least in part on a transformation of the modulation constellation to a subspace using dimensions associated with the non-uniform probabilistic distribution and a point of the transformed modulation constellation nearest to a projected symbol in the subspace.

Example embodiments of the disclosure relate to methods, apparatuses, and computer readable storage medium for beam quality determination based on Gram matrix of a Multiple Input Multiple Output Channel In the methods, a first apparatus determines a channel matrix based on at least one reference signal. The at least one reference signal is transmitted using at least one transmission beam among a set of transmission beams by a second apparatus and received using at least one reception beam among a set of reception beams by the first apparatus. The first apparatus determines a Gram matrix associated with a channel capacity based on the channel matrix and determines a beam quality for at least one of the at least one transmission beam or the at least one reception beam based on the Gram matrix.

The application provides a multiple-input multiple-output (MIMO) channel estimation method based on an angular energy and diffusion model. Firstly, under the condition of limited pilot resources, a coarse channel is obtained by using limited pilot observation signals, and the coarse channel is converted to an angular domain by a discrete Fourier transform (DFT) to calculate the energy distribution in different angular directions. Secondly, key angular directions are selected according to the angular energy distribution, and a non-uniform pilot matrix is constructed based on the selected directions, so that the pilot resources are preferentially used in the main energy directions of the channel. Finally, an angular strong path weight is constructed according to the key angular directions and is introduced into the diffusion model training process. In the backward recovery stage, the channel is gradually denoised and recovered by combining the pilot observation constraint and the angular weighted posterior correction, and a complete MIMO channel estimation result is obtained. The application can fully utilize the channel angular energy distribution characteristics under limited pilot resources, improve the pilot utilization efficiency, enhance the recovery ability of the diffusion model to the main channel components, and thus improve the MIMO channel estimation precision and recovery stability.

Systems and methods are disclosed for port selection for a wireless communication system. In one embodiment, a method performed by a Radio Access Network (RAN) node comprises dividing a channel matrix of one subcarrier of a Multiple-Input-Multiple-Output (MIMO) channel between an antenna array of the RAN node and a particular User Equipment (UE) into sub-matrices. The method further comprises forming a re-ordered channel matrix as a concatenation of the sub-matrices and forming a port-sorting matrix based on eigen vector matrices and eigen value matrices obtained via Eigen Value Decompositions (EVDs) performed on channel covariance matrices for the sub-matrices. The method further comprises applying the port-sorting matrix to the re-ordered channel matrix, re-ordering column vectors in the port-sorted channel matrix based on eigen values obtained from the EVDs, and applying grouping of the re-ordered, port-sorted channel matrix and port sorting accordingly to obtain a final port-sorted channel matrix.

Various aspects of the present disclosure relate to performing multi-layer precoding for near-field multiple-input multiple-output (MIMO) channels. A transmitter may select, for each layer (e.g., or eigen-channel or eigen-mode) of multiple MIMO layers, a symbol from a signal constellation rotated at an angle that is unique to / for the layer (with respect to the other layers) and precode (or pre-multiply) a symbol vector of the symbols selected for each layer with a unitary matrix. The transmitter may then transmit an indication to a receiver that identifies the multi-layer precoding for the data transmission.

Disclosed are techniques for transmitting reference signals for positioning estimation over a multipath multiple-input multiple-output (MIMO) channel and for processing the reference signals. In each aspect, a first node is configured to transmit a first reference signal resource set and a second reference signal resource set, wherein the first and second reference signal resource sets appear on a first subband and a second subband of the MIMO channel and / or during a first and a second time interval on the MIMO channel, wherein each reference signal resource in the first and second reference signal resource sets utilizes at least a first and a second MIMO precoder; and to transmit the first and second reference signal sets to a second node over the MIMO channel, wherein the first node transmits the first and second reference signal sets to assist the second node in performing positioning measurements based on joint processing of the first and second reference signal sets.

The application provides a wireless communication channel estimation method and system based on holographic multiple-input and multiple-output, comprising: equipping a uniform planar array for a holographic multiple-input and multiple-output channel, and sending a pilot signal by a user equipment for uplink channel estimation; predefining an antenna element index of the uniform planar array, and establishing a coordinate system with the center of the uniform planar array as the origin; constructing an angle covariance matrix, so that the original channel including three-dimensional angle and distance information is converted into the current covariance matrix only having angle information; constructing a distance covariance matrix based on the estimated angle information, so that the original channel including three-dimensional angle and distance information is converted into the covariance matrix containing distance information; and based on the constructed angle covariance matrix and distance covariance matrix, informing the user equipment by a transmission and reception point that the two matrix estimation tasks are completed, so that the construction of the wireless communication channel is realized. The application solves the problem of high cost of antenna control technology in the next generation of wireless communication.

Example embodiments of the disclosure relate to methods, apparatuses, and computer readable storage medium for beam quality report based on Gram matrix of a Multiple Input Multiple Output Channel. In the methods, a first apparatus receives, from a second apparatus, a configuration indicating that a beam report is to be transmitted based on a beam quality determined using a Gram matrix associated with a channel capacity. The Gram matrix is determined based on a channel matrix associated with at least one transmission beam among a set of transmission beams of the second apparatus and at least one reception beam among a set of reception beams of the first apparatus. The first apparatus further transmits the beam report to the second apparatus based on the configuration.

Some examples of the techniques described herein may provide multiple-input and multiple-output (MIMO) channel state feedback (CSF) based on dictionary learning. An adaptive dictionary may provide an enhanced compression ratio for CSF information relative to a fixed dictionary. Various approaches for applying a sparse representation for CSF are provided herein. Techniques for applying dictionary learning for CSF procedures are also provided herein. Sparse representation may be utilized in wireless communications. Sparse representation may include representing information with a reduced quantity of information. For example, a sparse representation of a signal based on dictionary learning may be utilized to compress transmission data with an adaptive basis to provide enhanced efficiency for computational or communication resource utilization. Adapting the dictionary may improve compression or performance for communicating signals via a MIMO channel. For example, a UE may utilize a learned dictionary to compress channel state information.

Methods and apparatuses for multiple input-multiple output (MIMO) channel access are provided herein. A method performed by a station (STA) may include receiving, from an initiating STA, a frame including information indicating a broadcast address and a control trailer. The control trailer may include information indicating that a multi-user multiple input-multiple output (MU-MIMO) frame is to be received from the initiating STA, and information indicative of a plurality of STAs, the plurality of STAs including the STA. The method may include transmitting a response frame to the indicated broadcast address based on the received frame and receiving the MU-MIMO frame from the initiating STA.

Techniques pertaining to multiple-input-multiple-output (MIMO) joint time of arrival (ToA) and angle of arrival (AoA) for Wi-Fi systems in wireless communications are described. An apparatus performs channel estimation to obtain a plurality of multiple-input-multiple-output (MIMO) channel state information (CSI). The apparatus then calculates weighted MIMO CSI and determines a time of arrival (ToA) or the ToA and an angle of arrival (AoA) based at least on the weighted MIMO CSI.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

The present disclosure provides a crest factor reduction system and method for suppressing peak regrowth. The system receives a complex signal from the physical layer (PHY) of a base station equipped with an orthogonal frequency division multiplexing (OFDM) function. Further, the system uses a coordinate rotation digital computer (CORDIC) to convert the complex signal into polar form. Also, the system utilizes an advanced interpolation processing (AIP) technique to minimize the peak regrowth that may occur at x4 (digital upconversion (DUC)) in the latter half of the downlink chain. Thereby, the initial problem of peak regrowth associated with the DUC process can be suppressed by the proposed CFR design. Further, in the CFR design of the present invention, since multiple multiple-input multiple-output (MIMO) channels can be operated by multiplexing in the time domain, the resource utilization rate can be further reduced.

Some examples of the techniques described herein may provide multiple-input and multiple-output (MIMO) channel state feedback (CSF) based on dictionary learning. An adaptive dictionary may provide an enhanced compression ratio for CSF information relative to a fixed dictionary. Various approaches for applying a sparse representation for CSF are provided herein. Techniques for applying dictionary learning for CSF procedures are also provided herein. Sparse representation may be utilized in wireless communications. Sparse representation may include representing information with a reduced quantity of information. For example, a sparse representation of a signal based on dictionary learning may be utilized to compress transmission data with an adaptive basis to provide enhanced efficiency for computational or communication resource utilization. Adapting the dictionary may improve compression or performance for communicating signals via a MIMO channel. For example, a UE may utilize a learned dictionary to compress channel state information.

The present disclosure provides a system and a method for crest factor reduction (CFR) with a restricted peak regrowth. The system receives a complex signal from a physical layer (PHY) of a base station equipped with orthogonal frequency division multiplexing (OFDM). The system uses a coordinate rotation digital computer (CORDIC) to convert the complex signal into a polar form. Further, the system utilizes an advanced interpolation processing (AIP) technique to minimize the peak regrowth that can happen in an x4 (digital up-conversion (DUC) in the later stage of a downlink chain. Hence, a teething problem of peak growth due to DUC process is restricted to a limited regrowth in the proposed CFR design. Further, the CFR design can operate multiple multiple-input multiple-output (MIMO) channels by multiplexing in time domain which further reduces resource utilization.

Apparatus and methods are provided for multi-cluster multiple input multiple output (MIMO) channel modeling with extended Tapped Delay Line (TDL) channel model that includes the TX and RX beam steering. In one novel aspect, extended TDL channel model is used for each cluster for the multi-cluster MIMO channel modeling, wherein the extended TDL channel model includes a transmitting beam steering and a receiving beam steering. In one embodiment, a spatial MIMO channel model is configured based on a combination of a number of spatial clusters, wherein each spatial cluster is associated with a statistically independent and time-varying extended TDL channel model that includes a spatial correlation component and for each spatial cluster, a transmitting and receiving beam steering matrix are configured. One or more channel properties, including the number of clusters, and per cluster configurations are selected based on a desired test scope.

Disclosed is an integrated circuit for a communication apparatus. The integrated circuit includes a receiver, which, in operation, receives a physical layer protocol data unit (PPDU) with a single frequency resource, and circuitry, which, in operation, processes the PPDU. The PPDU includes a first field that indicates a first number of symbols of a long training field (LTF) for Multiple Input Multiple Output (MIMO) channel estimation in the PPDU. The first number is larger than or equal to a second number of the symbols of the LTF. The second number is determined according to a number of spatial streams (Nss) for the PPDU. The circuitry processes the PPDU based on the first number.

A communication device for providing an explicit channel state information, CSI, feedback in a wireless communication system includes a transceiver to receive, from a transmitter a radio signal via a radio time-variant frequency MIMO channel, the radio signal including downlink reference signals according to a reference signal configuration, and downlink signals including the reference signal configuration, and a processor. The processor estimates the CSI using measurements on the downlink reference signals of the radio channel according to the reference signal configuration over one or more time instants / slots, constructs a frequency-domain channel tensor using the CSI estimate, performs a higher-order principal component analysis, HO-PCA, on the channel tensor, identifies a plurality of dominant principal components of the channel tensor, thereby obtaining a compressed channel tensor, and reports to the transmitter the explicit CSI including the dominant principal components of the channel tensor.