14 results about "Mimo systems" patented technology

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.

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 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.

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 relates to the communication field and discloses a channel data decoding method, device and equipment for a MIMO system, wherein the method comprises the following steps: acquiring a receiving signal matrix output by a MIMO channel; determining an estimated matrix of a sending signal matrix based on the receiving signal matrix; acquiring soft information of each estimated symbol in the estimated matrix to construct a soft information matrix of the estimated matrix; iteratively updating the soft information matrix; in any iteration round, decoding and updating the soft information matrix of the current iteration round to obtain a candidate codeword list set of the current iteration round; after the iterative updating is completed, determining a target codeword matrix based on the candidate codeword list set of the last iteration round; checking the target codeword matrix; and outputting information corresponding to the target codeword matrix as a decoding result when the checking result represents that the target codeword matrix is correct. The technical scheme provided by the application can reduce the bit error rate and the missing detection rate of channel transmission and significantly improve the decoding performance.

This invention proposes a collaborative uplink / downlink channel extrapolation method for large-scale MIMO systems. Addressing the issues of limited uplink pilot bandwidth and uplink / downlink timing asynchrony in time-division duplex systems, this method proposes a collaborative channel reconstruction scheme between the User Equipment (UE) and the Base Station (BS). Specifically, the method includes: at the UE, extracting downlink propagation delay parameters from a small number of high-energy downlink beam observations using a smoothed MUSIC algorithm, and feeding back only the low-dimensional delay parameters to the base station; at the BS, estimating the uplink / downlink delay offset using uplink narrowband pilot observations, and calibrating the fed-back delay parameters; at the BS, using complementary subspace projection technology, further estimating the lost delay paths in the downlink observations from the uplink observations, and merging them to obtain a complete delay set; based on the complete delay set, reconstructing the full-band uplink channel using a compressed sensing algorithm. This method significantly reduces computational complexity while maintaining channel extrapolation accuracy.

This invention discloses a multi-user joint decoding method for cellular-free massive MIMO systems based on model-driven deep learning. In the simulation part of data decoding, an optimization problem of the simulation combination matrix is ​​established with the objective of maximizing the achievable rate of the cellular-free massive MIMO system. Based on a heuristic algorithm, the optimization objective is simplified, and the optimization problem is decomposed into multiple sub-problems. The single AP simulation precoding matrix optimization problem is solved one by one to obtain the optimal simulation combination matrix. The multi-user joint LDPC decoding algorithm is extended to the cellular-free massive MIMO scenario. Multi-user joint LDPC decoding is implemented based on the MIMO-LDPC-Net decoding network. In this network, the input layer generates initial values ​​as input to the hidden layer, which is composed of stacked modules consisting of a first sub-layer, a second sub-layer, and a MIMO detection layer. The output layer generates the log-likelihood ratio of the variable nodes. Experiments show that the heuristic algorithm + AI-assisted joint decoding algorithm has a 1dB signal-to-noise ratio gain compared to the traditional algorithm.

The application discloses a near-field target positioning method based on atomic norm space frequency modeling. The method establishes a near-field array receiving signal model, then carries out a space frequency structural representation of the near-field signal, then carries out a meshless sparse space frequency representation based on atomic norm minimization, then carries out frequency ridge line extraction and linear fitting, and finally realizes joint estimation of angles and distances and system output. The application fully utilizes the space frequency variation law of the near-field signal, obtains the angle and distance information of the target by extracting the main frequency ridge line and carrying out linear fitting, can realize high-precision and low-complexity joint estimation of angles and distances, has significant advantages in parameter estimation precision, robustness and calculation efficiency, and can reduce the influence of noise and window effect on the estimation precision, thereby providing a high-precision and easy-to-implement technical scheme for near-field target positioning in large-scale MIMO systems and 6G communication.

A communications network system including antenna elements and a processor coupled with the antenna elements, the processor executing instructions to perform operations including establishing a first data communication link over a first frequency band between the CPU and the first AP of a first group of APs, causing the first AP to establish a second data communications link over a second frequency band between the first AP and a first UE, transmitting, via beamforming by the antenna elements, data to the first AP over the first data communications link, the data configured to be relayed via the first AP to the first UE over the second data communications link, obtaining an end-to-end data rate of the data communication between the CPU and the first UE, and achieving a higher end-to-end data rate than the obtained end-to-end data rate by adjusting beamforming vectors.

This invention belongs to the field of wireless communication technology, specifically relating to a SIM-enabled massive MIMO communication system. The system employs a single-RF link transmitter architecture, replacing traditional antenna arrays and single-layer reconfigurable smart surfaces with a SIM, enabling efficient signal transmission in multi-user communication scenarios. Specifically, the stacked smart metasurface applies controllable phase modulation to the incident single-frequency carrier signal, achieving signal modulation and precoding without the need for traditional baseband modulation. This allows different users to receive their desired signals with low mutual interference. Compared to traditional massive MIMO systems, this system architecture effectively alleviates the engineering challenges of high hardware complexity, high integration density, increased power consumption, and limited heat dissipation faced by high-frequency communication, while significantly reducing reliance on high-precision digital-to-analog converters and multiple RF links.

The application discloses a millimeter wave movable array beam training method based on quantum correlation simulated annealing, and relates to the technical field of wireless communication.The application proposes a quantum correlation simulated annealing (QCSA) algorithm aiming at the high-dimensional, non-convex and strongly coupled beam-array position joint optimization problem caused by the movable array, and adopts an alternating optimization framework to decompose the joint optimization problem into iterative solving of the transmitting and receiving subspaces; in each subspace optimization, the physical correlation between parameters is learned through Gaussian sampling based on an adaptive covariance matrix, the global search capability is enhanced by using an acceptance criterion containing a thermodynamic term and a quantum tunneling term to escape from a local optimum, and a periodic oscillation thermal scheduling strategy is adopted to dynamically balance exploration and utilization, so that the application has the characteristics of low overhead, fast convergence and high robustness, and is suitable for 6G millimeter wave and terahertz movable array MIMO systems.

This invention relates to a NOMP-based RIS-assisted millimeter-wave MIMO system channel estimation method, belonging to the category of channel estimation methods. Taking the downlink of a RIS-assisted millimeter-wave MIMO system as an example, the channel model of the base station (BS)-RIS and the RIS-mobile station (MS) is represented as a concatenated channel model in Khatri-Rao product form. One-dimensional NOMP, two-dimensional NOMP, and least squares (LS) algorithms are used to estimate multiple parameters in the concatenated channel, thereby achieving channel estimation. The advantages are that the proposed RIS-assisted millimeter-wave MIMO system channel estimation method estimates channel parameters with lower pilot overhead, providing effective CSI for RIS-assisted systems; applying the NOMP method to RIS-assisted millimeter-wave MIMO channel estimation can solve the grid mismatch problem existing in traditional compressed sensing algorithms, improve the accuracy of joint parameter estimation, and thus improve the channel estimation accuracy.

This application relates to a broadband reception method, design, and medium based on a LoS-MIMO channel, and pertains to the field of wireless communication. The aim of this application is to address the limitation of high-speed transmission in practical applications of LoS-MIMO systems. The receiving end of this application acquires the received signal and obtains multiple serial time-domain received signals after down-conversion; it performs timing synchronization on the time-domain received signals to determine the start position of OFDM symbols; it converts each received signal into multiple parallel low-speed data streams; it calculates and accumulates the local carrier frequency offset correlation value of the low-speed data using a cyclic prefix, and estimates the carrier frequency offset value based on the accumulation result; it uses the estimated value to perform phase compensation on each parallel low-speed data stream and removes the cyclic prefix, performing OFDM symbol demapping; it performs channel estimation and equalization based on the frequency-domain OFDM symbols, and performs inverse discrete Fourier transform and recovery processing on the equalized signal to obtain the original bitstream.

An example embodiment may involve receiving, at a node of a radio access network, input signals on a plurality of wireless channels, wherein uplink channels in the plurality of wireless channels are respectively associated with downlink channels in the plurality of wireless channels; determining up-link characteristics for the uplink channels; providing, to a trained neural network, the uplink characteristics, wherein the trained neural network was iteratively trained, with a training data set of recorded uplink characteristics and recorded downlink characteristics, to form predictions of the recorded downlink characteristics from the recorded uplink characteristics, wherein the trained neural network includes an encoder that maps the recorded uplink characteristics to latent vector representations, and a decoder that maps the latent vector representations to the predictions; receiving, from the trained neural network, a prediction of downlink characteristics for the downlink channels; and transmitting, on the downlink channels, output signals based on the prediction.