152 results about "Mimo systems" patented technology

This invention provides a novel transceiver design to control / compensate for the spatial-wideband effect in wideband massive multiple-input multiple-output (MIMO) systems for both sensing and communication networks. The proposed design aims to divide the ultra-wideband signal into narrow band beams and control them with a simplified exhaustive search-based precoding to align the beam angle to the target direction.

A network node may generate an initial precoder for a plurality of antennas, determine antenna radiated power for each of the plurality of antennas based on the initial precoder, select a subset of antennas from the plurality of antennas based on the determined antenna radiated power, generate at least one transmission precoder for at least one selected antenna of the selected subset of antennas, and perform communication with at least one user equipment device using the at least one transmission precoder and the at least one selected antenna of the selected subset of antennas.

A method of MIMO channel estimation is provided that involves the transmission of MIMO reference signals that use a non-uniform pattern of resources in space, time, frequency, and / or code and is calculated from a prior knowledge. The prior knowledge is obtained from a plurality of MIMO channel samples associated to a region. In a receiver, MIMO reference signal are received based on a receiver-MIMO-reference-signal-placement is also a non-uniform pattern of resources in space, time, frequency, and / or code and is calculated from the prior knowledge. A MIMO channel is estimated from channel measurement on the reference signals and the prior knowledge.

The invention provides a vortex electromagnetic wave radar multipath virtual image suppression method and system, and relates to the technical field of radar imaging virtual image suppression, and the method comprises the steps: adjusting the circular ring radiuses corresponding to different modes in a uniform concentric circular ring array according to the prior information of a target pitch angle, and obtaining the radar emission configuration of main lobe alignment; performing time domain sampling on the multi-path vortex radar echo signal in each mode to form a two-dimensional radar echo matrix; analyzing the obtained two-dimensional radar echo matrix and the MISO system echo after phase compensation by using an azimuth dimension point spread function to obtain dual-system imaging features; and performing modal domain FFT and time domain pulse compression processing on the multipath echo features under the MISO and MIMO systems by using fast Fourier transform and pulse compression, and finally outputting a virtual image suppressed synthetic radar image. The multi-path virtual image suppression method has the beneficial effects that the suppression of the multi-path virtual image is realized by utilizing the characteristic that the real image positions of the imaging results are completely the same and the virtual image positions are different under the multi-transmitting and single-receiving systems.

The invention discloses a CSI (Channel State Information) feedback method and system based on deep learning, belongs to the technical field of communication, and aims to solve the technical problems of low recovery precision, reduced frequency efficiency and huge uplink bandwidth consumption of CSI feedback in a large-scale MIMO (Multiple Input Multiple Output) system. Comprising the following steps that a channel state information feedback model TCMRNet is constructed and trained, the TCMRNet comprises an encoder and a decoder, LSTM network units and convolution layers with different convolution kernel sizes are introduced into the encoder and the decoder, the output end of each convolution layer is connected with a batch normalization layer, and the output end of each convolution layer is connected with an LSTM network unit; the encoder is used for carrying out multi-resolution feature extraction and time correlation analysis on input channel state information and compressing extracted features into feature vectors to be output, and the decoder takes the feature vectors as input and is used for decompressing the feature vectors, recovering the channel state information and outputting reconstructed channel state information; and performing time domain transformation on the reconstructed channel state information.

A method and system are provided for scheduling data transmission in a Multiple-Input Multiple-Output (MIMO) system. The MIMO system may comprise at least one MIMO transmitter and at least one MIMO receiver. Feedback from one or more receivers may be used by a transmitter to improve quality, capacity, and scheduling in MIMO communication systems. The method may include generating or receiving information pertaining to a MIMO channel metric and information pertaining to a Channel Quality Indicator (CQI) in respect of a transmitted signal; and sending a next transmission to a receiver using a MIMO mode selected in accordance with the information pertaining to the MIMO channel metric, and an adaptive coding and modulation selected in accordance with the information pertaining to the CQI.

Techniques of using interferometry for sensing movement of an object in a communications network operating at high frequency are provided. The distance between slits does not need to be large to achieve high resolution due to the short wavelength (λ) at high frequency. Therefore, considering multi-panel, or multi-portion panel transmission, the distance between panels, or portions of a panel, may be sufficient to achieve high accuracy, even considering the case of the single panel that is virtually divided into two or more subpanels by controlling the phase shifts in each subpanel. Methods of signaling associated with an over-the-air (OTA) interferometer between a transmitter and receiver for measuring movement of an object are provided.

The invention discloses a DRL-based power distribution method in a VR overlapped XL-MIMO system, belonging to the technical field of wireless communication multi-antenna transmission, comprising: in an XL-MIMO multi-user downlink scene communication system, collecting position information of users and VR information in an environment, and using a rectangular area to represent the positions of the users; based on the position information and the VR information, generating a corresponding user channel matrix, and constructing a corresponding user precoding matrix; defining a state space, an action space, a reward value and a priority experience playback mechanism of the Markov decision process; establishing a strategy network and a value network for updating a strategy function and a value function; and selecting an optimal strategy network to generate an optimal power distribution strategy according to the maximum reward value obtained by feedback through continuous iterative updating. According to the method provided by the invention, the problem of high complexity of a power distribution algorithm in an existing real scene is solved by utilizing the VR characteristics of the user, and the transmission performance of the system is effectively improved.

Examples pertaining to repeater gain control for device collaboration in distributed MIMO systems are described. An apparatus (e.g., a repeater) may determine a first compensation value for a first path loss on a first link between the apparatus and a UE, and determine a second compensation value for a second path loss on a second link between the apparatus and a network node of a wireless network. The apparatus may also determine a repeater gain for uplink power control according to the first compensation value and the second compensation value. The apparatus may further forward a radio signal from the UE to the network node according to the repeater gain.

The invention relates to the field of communication, and provides a visible light communication method for a display and MIMO system, and the method comprises the steps: obtaining to-be-displayed content and to-be-transmitted data; according to the number S of pixels, needing to be lightened at a single time, of the to-be-displayed content, the QAM order of a channel corresponding to each pixel is determined; dividing the data to be transmitted into P groups of transmission data, so that each group of transmission data is transmitted through one channel; respectively performing QAM mapping on the P groups of transmission data according to the QAM order to generate P groups of QAM code elements; respectively carrying out OFDM modulation on the P groups of QAM code elements to generate P modulation signals; and driving the micro-LED array to synchronously transmit P paths of optical signals and perform communication based on the P modulation signals. According to the invention, the advantages of MIMO and micro-LED are combined, the spectrum utilization efficiency is improved on the basis of improving the modulation bandwidth, and the communication rate of a visible light communication system can be maximized.

The invention discloses a spatial non-stationary large-scale MIMO channel estimation method based on pre-screening and a multi-stage dynamic threshold strategy, belongs to the technical field of wireless communication, and solves the technical problems of insufficient channel estimation precision and relatively high calculation complexity of a spatial non-stationary large-scale MIMO system in the prior art. The channel estimation method is divided into two stages: pre-screening strategy stage estimation: carrying out preliminary screening on all possible candidate atoms so as to reduce the calculation amount; by calculating the correlation between the candidate atoms and the residual error, selecting the candidate atoms which are most likely to be non-zero to form a primary candidate set; estimating in a multi-stage dynamic threshold adjustment stage, further screening candidate atom sets selected according to a pre-selection strategy, and improving the accuracy of non-zero element estimation through a dynamic threshold mechanism; the dynamic threshold is adjusted according to the current signal-to-noise ratio (SNR) so as to adaptively process the estimation precision under different channel conditions and ensure the robustness of channel estimation under different signal-to-noise ratio conditions.

The invention belongs to the field of unmanned aerial vehicle auxiliary MIMO system physical layer authentication, and discloses a bidirectional privacy protection unmanned aerial vehicle auxiliary MIMO system physical layer authentication method, which comprises the following steps: step 1, considering a frame consisting of a pilot frequency and a data symbol, and establishing a carrier frequency offset statistical model representing the identity of an unmanned aerial vehicle; step 2, establishing a CFO session key negotiation algorithm based on the carrier frequency offset statistical model and the elliptic curve cryptography; 3, designing an unmanned aerial vehicle identity bidirectional authentication framework to complete identity authentication based on a carrier frequency offset statistical model and a CFO session key negotiation algorithm; and 4, based on the carrier-free frequency offset statistical model, performing authentication performance analysis, and quantifying the authentication effect. Compared with the prior art, the method has the advantages that the robustness and the accuracy of attacking identity-based impersonation attacks are better.

The invention relates to the technical field of wireless communication, in particular to a variational Bayesian inference-based RIS-assisted large-scale MIMO system channel estimation algorithm, which comprises the following steps: S1, establishing a single RIS-assisted large-scale MIMO system model through parameter setting, and determining the number of transmitting antennas, receiving antennas and RIS reflection elements of a large-scale MIMO system; according to the method, the uncertainty of the channel is modeled through the variational Bayesian inference technology, and the posterior distribution of the channel state information is deduced, so that the channel estimation precision is improved; meanwhile, an adaptive compressed sensing algorithm is introduced, a compressed sensing matrix is dynamically optimized by using channel sparsity characteristics, the data sampling amount and the calculation complexity are reduced, and compared with the prior art, the method has higher estimation precision, lower calculation complexity and higher system robustness in a high-noise and complex channel environment, and the method is suitable for being applied to the field of communication. The method is suitable for efficient deployment and application of a large-scale MIMO system in a next-generation wireless communication network.

This invention discloses a monostatic symmetric nested array MIMO system and a near-field positioning method, belonging to the field of monostatic MIMO near-field positioning technology. This invention can effectively improve the positioning performance during near-field estimation. The monostatic symmetric nested array MIMO system proposed in this invention has a symmetrically distributed transmit and receive arrays. Compared to other symmetric MIMO systems, its differential array is a positionally continuous array with more degrees of freedom. With the same physical array aperture, fewer physical array elements are actually used, reducing costs. The near-field positioning method proposed in this invention calculates the fourth-order statistics of the signal and quantizes them to obtain a virtual differential array of the differential array, achieving accurate near-field positioning of sparse MIMO structure arrays. Furthermore, it can be extended to more sparse MIMO symmetric systems.

The present invention discloses a signal detection system and method for a MIMO system based on parallel Cholesky matrix decomposition, which mainly solves the problems of high complexity in signal detection of existing MIMO systems and doubling of detection delay when performing signal detection in the real number domain. The implementation steps are as follows: the channel matrix conversion module converts the channel complex matrix into a channel real matrix and generates an iterative matrix; the channel matrix decomposition module parallelly decomposes the iterative matrix by using the Cholesky matrix decomposition method; the K-Best parallel search module parallelly searches for paths between adjacent two layers by using the K-Best parallel search method; the detection result decision module completes the detection of signals of all antennas and determines the detection value of the transmitted signal. The parallel design of the signal detection process in the present invention effectively reduces the detection complexity, reduces the detection delay, and can realize signal detection with fewer resource quantities.

A method for compressing Sounding Reference Signal (SRS) channel information in massive MIMO systems is disclosed. When SRS channel estimation is performed in a Radio Unit (RU), the resulting channel estimates create significant fronthaul bandwidth demands. The method transforms SRS channel estimates from antenna space to domains with superior energy compacting properties, enabling transmission of only a subset of channel values while preserving information quality. The method includes beam space compression using mathematical transforms such as DFT, SVD, or EVD to convert channel estimates to beam space, followed by selection of the most significant beams. Alternative embodiments include tap domain compression using DCT or DFT, and delay-Doppler domain compression. The methods support static beam space compression using predefined beam bases and dynamic compression employing adaptive beam bases. O-RAN C-Plane implementations extend Section Type 6 to support the compression methods. The methods significantly reduce fronthaul bandwidth requirements while maintaining channel estimation accuracy, enabling improved system capacity in massive MIMO deployments.

The invention discloses a method for removing a downlink beam of a cellular mMIMO (multiple input multiple output) system based on non-ideal calibration, which comprises the following steps of: firstly, giving a mapping relation between an uplink channel and a downlink channel under non-ideal calibration based on a Rician fading channel model, and then, according to an expression of data received by a downlink user under a non-ideal calibration condition, calculating a downlink beam of the cellular mMIMO system based on the non-ideal calibration. According to the method, a downlink rate expression of a cellular-removed large-scale MIMO system is deduced, and finally, a downlink sum rate maximization optimization problem is iteratively solved by using a Lagrangian dual transformation method and a scaling type alternating direction multiplier method with a purpose of maximizing the downlink sum rate, so that an optimal downlink beam sequence is obtained. According to the method, the negative influence of a reciprocity calibration error on the system performance is reduced, the performance loss caused by non-ideal calibration is reduced, and the downlink rate of the cellular-removed large-scale MIMO system under the non-ideal calibration is effectively improved.

The present invention discloses a robust data transmission method for a MIMO system, comprising the following steps: Step 1: Obtain the noise power of the channel, the channel estimation value, and the uncertainty of the channel information at the transmitter; Step 2: Initialize the iteration index, the iteration step size, and the precoding matrix; Step 3: Given the current precoding matrix, calculate the worst channel error matrix; Step 4: Calculate the current precoding direction matrix and update the precoding matrix; Step 5: Update the iteration index; Step 6: Repeat Steps 2-5 until the algorithm converges, and output the precoding matrix; Step 7: Multiply the precoding matrix by the data at the transmitter and send the result. The method of the present invention designs an algorithm with an inner and outer two-layer iteration structure, and this method has excellent system performance in a relatively wide signal-to-noise ratio region. Compared with some existing data transmission schemes, it can achieve a larger worst transmission rate of the system, that is, it is optimal in terms of the worst transmission rate performance.

This application provides a method, apparatus, medium, and product for ultra-large-scale MIMO channel modeling, relating to the field of wireless communication technology. The method includes: obtaining the cluster power of each cluster according to a preset channel model; determining the spatially non-stationary SnS state of each cluster according to the cluster power or a preset statistical distribution model; determining a first power attenuation factor for different clusters at the base station based on the SnS state of each cluster; determining a second power attenuation factor for each element of the user equipment; and generating channel coefficients based on the first and second power attenuation factors. The solution of this application provides a simple, physically meaningful, and efficient channel modeling method, effectively overcoming the shortcomings of existing technologies and providing reliable theoretical support for the design and optimization of ultra-large-scale MIMO systems.

The application discloses a Markov chain Monte Carlo channel estimation method based on sparse Bayesian learning, and belongs to the technical field of wireless communication. The method comprises the following steps: modeling unknown quantities in a channel estimation problem as random variables, and constructing a Bayesian probabilistic graph model; introducing a structured mixture Gaussian prior distribution for channel elements, and converting the mixture Gaussian prior into a product form through a Bernoulli discrete variable; deriving conditional posterior distributions of all variables, performing high-dimensional sampling through multiple parallel Gibbs samplers, and obtaining a channel estimation value by averaging converged samples. The application solves the problems of traditional compressed sensing algorithms, such as insufficient dependence on prior information and easy falling into local optimization, by combining sparse Bayesian learning and Markov chain Monte Carlo method, significantly improves channel estimation accuracy, and is suitable for super-large-scale MIMO systems by reducing computational complexity through parallel sampling.

The invention presents an innovative antenna panel design for Massive MIMO systems, aimed at achieving 180-degree cell coverage in 5G networks. The design incorporates a split-panel configuration, dividing the antenna array into two perpendicular panels, each covering a 90-degree sector. This approach minimizes the number of antennas needed per cell, reducing from three to two while maintaining full coverage. The split-panel design enhances beamforming precision and improves signal quality by effectively directing beams across a wide area, optimizing coverage and reducing interference. The antenna panels can be dynamically reconfigured to adapt to varying user distributions and environmental conditions, ensuring efficient network performance. The invention is particularly suitable for deployment in urban and suburban settings, offering a scalable and cost-effective solution to enhance network capacity and coverage.

This invention discloses a prediction-aided channel estimation method suitable for TDD Massive MIMO systems, including an online model training step and an aging channel prediction step. This invention uses a Latent ODE model to continuously model the channel, solving the problem of not being able to directly obtain the CSI at aging time points within a time slot, thus breaking away from the paradigm of "discrete time series prediction model + interpolation function". The online training prediction architecture proposed in this invention avoids the model aging problem caused by the high latency of complex model training by decoupling the MIMO channel using SISO. Furthermore, online training enables the prediction model to track the dynamic changes in the propagation environment in real time, thereby improving model inference performance.

This invention provides a novel transceiver design to control / compensate for the spatial-wideband effect in wideband massive multiple-input multiple-output (MIMO) systems for both sensing and communication networks. The proposed design aims to divide the ultra-wideband signal into narrowband beams and control them with a simplified exhaustive search-based precoding to align the beam angle to the target direction.

Techniques are disclosed for implementing a CMOS-compatible millimeter wave matrix computing network architecture, which enables high-speed matrix operations for deep learning neural networks through a reconfigurable feedforward architecture using matrix computing meshes. Each mesh may include hybrid couplers and adjustable phase shifters. The architecture may be configured in various arrangements with programmable weights. The architecture offers advantages over existing solutions through full CMOS compatibility, the elimination of optical-electrical conversion, improved scalability, total latency, and superior power efficiency. Applications include massive MIMO systems and cognitive radar, in which the network may be implemented as part of RF front ends to reduce ADC requirements, system complexity, and power consumption.

This invention provides a method, apparatus, and terminal device for estimating transmitted signals, belonging to the field of wireless communication technology. The method includes determining a received signal vector, a channel matrix, and a noise variance; inputting the received signal vector, channel matrix, and noise variance into a signal estimation model to obtain an estimated transmitted signal value output by the signal estimation model; wherein, the signal estimation model is obtained by optimizing a minimum mean square error detector based on a Fourier transform algorithm and a pre-determined weighting factor, and the weighting factor is used to adjust the convergence step size. This invention uses the Fourier transform algorithm to replace matrix inversion in the minimum mean square error detector, avoiding explicit matrix operations, reducing computational complexity, and can be used in M-MIMO systems with large-scale antennas. Furthermore, adjusting the convergence step size based on the weighting factor is beneficial for stable convergence and prevents severe degradation of bit error rate performance under high-dimensional and high-order modulation conditions.

The invention discloses a tensor decomposition-based flux fusion positioning algorithm, which comprises the following steps of: estimating a channel transmission coefficient of each subcarrier by using a single communication time slot by utilizing an OFDM (Orthogonal Frequency Division Multiplexing) technology and the characteristics of an MIMO (Multiple Input Multiple Output) system, constructing a virtual array and a virtual signal matrix, converting into a third-order tensor according to a certain method, performing CP tensor decomposition on the third-order tensor, and finally obtaining the flux fusion positioning algorithm. And the azimuth angle and distance of each target are calculated to realize the sympathetic fusion. The azimuth angle and the distance of the target can be estimated by using a single communication time slot without searching the target in the space, so that higher estimation precision and higher operation speed can be kept.

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products that may be implemented in a wireless transmit / receive unit (WTRU) and / or a network access point (NAP) with respect to MIMO precoding using a precoding neural network (NN) at the NAP. Training of the NN may use artificial and / or measured channel state information (CSI). In one embodiment, the WTRU may provide feedback to the NAP in the form of CSI statistics, time correlation, and / or bit error rate (BER) regarding a channel. The NAP may inform the WTRU of information for extracting training samples which are fed back from the WTRU to the NAP. The NAP may perform retraining of the NN using the training samples to adjust and / or recalibrate the NN weights. The information for extracting training samples may include CSI reference signal (CSI-RS) density and / or CSI-RS transmission time slots for extracting the training samples at the WTRU.

The application provides a joint structured OFDM modulation and precoding design method of a large-scale MIMO system, and relates to the technical field of MIMO communication.In the application, a point-to-point large-scale MIMO structured OFDM (mMIMO-SOFDM) system is constructed by using a structured OFDM modulation method; and based on a joint spatial precoding and structured frequency domain equalization scheme, a closed-form solution of each subcarrier frequency domain equalizer, transmission precoding and reception precoding of the mMIMO-SOFDM system is solved.By proposing the structured OFDM scheme, the excessive cyclic prefix overhead caused by the double wideband effect of the large-scale MIMO system is reduced.In addition, the proposed joint spatial precoding and structured frequency domain equalization scheme further improves the spectral efficiency of the system and reduces the peak-to-average ratio.

A network node utilizes a standard-defined CSI-RS resource mapping scheme defined for up to 32 logical ports to manage CSI reporting from a terminal where an antenna system of the network node includes more than 32 logical antenna ports. The network node sends CSI configuration message for a density-adjusted CSI resource mapping scheme where one or more transmission modes of the standard-defined CSI- RS resource mapping scheme are limited to one or more values of the reference signal transmission density to less than 1.0.

The present invention provides a method for obtaining the electromagnetic effective degrees of freedom of a MIMO system in a non-free space, the method comprising S1: establishing a MIMO communication system model in a non-free space including a reflective ground; S2: performing electromagnetic simulation by respectively changing the number of array units, the communication distance, the array height, and the dielectric constant of the reflective ground; S3: obtaining a corresponding channel matrix according to the simulated receiving electric field, and establishing a full polarization electromagnetic channel model; S4: calculating the electromagnetic effective degrees of freedom using the obtained simulation results, and drawing an electromagnetic effective degrees of freedom curve. The generated channel correlation matrix is ​​subjected to singular value decomposition, and the electromagnetic effective degrees of freedom calculated can more comprehensively characterize the performance of the communication channel. The present invention can assist in analyzing the performance of a communication system in a non-free space through the electromagnetic effective degrees of freedom, and thus can serve as an effective basis for designing and laying out indoor and outdoor antenna arrays that assist wireless communications.