15 results about "Channel correlation matrix" patented technology

The application provides a data processing method, device and equipment applied to a convolutional neural network, the method comprising: performing convolution calculation on received input feature data of a current layer to obtain initial output feature data of the current layer; inputting the input feature data to a channel attention structure to make the channel attention structure perform predetermined processing on the input feature data, output a channel correlation matrix, and multiply the initial output feature data and the channel correlation matrix to obtain final output feature data of the current layer. In the method, the channel attention structure is arranged in at least one layer of the convolutional neural network, and the obtained channel correlation matrix is used to calibrate spatial features. Since the channel correlation matrix can represent the similarity degree of transformation results of channels of the current layer under different shared convolution kernels, the importance of the channels can be accurately measured, and thus the spatial features can be accurately calibrated.

This invention relates to the field of risk management in underground engineering, and discloses a multi-parameter collaborative perception and intelligent alarm system for prominent key geological anomalies. The system includes: a data acquisition and preprocessing module, which acquires and preprocesses multi-dimensional parameter data, outputting a multi-parameter data matrix; a feature extraction module, which extracts features from the time-series data using a temporal feature extraction network based on the multi-parameter data matrix, obtaining a temporal feature vector; and simultaneously extracts features from image data using a multi-branch hybrid pooling convolutional neural network, obtaining an image feature vector; a feature fusion module, which constructs a cross-parameter channel correlation matrix through a cross-modal feature channel attention mechanism, outputting a multi-parameter collaborative representation vector; and an alarm generation module, which inputs the multi-parameter collaborative representation vector into an integrated learning anomaly discrimination model, outputting anomaly probability values ​​and generating multi-level alarm signals. This achieves accurate perception and early warning of key geological anomalies, significantly improving alarm accuracy and timeliness.

The invention relates to the field of underground engineering risk management and control, and discloses a multi-parameter collaborative perception and intelligent alarm system for highlighting master control geological anomalies, and the system comprises a data collection and preprocessing module which collects and preprocesses multi-dimensional parameter data, and outputs a multi-parameter data matrix; the feature extraction module is used for extracting features from the time sequence data by using a time sequence feature extraction network based on the multi-parameter data matrix to obtain a time sequence feature vector; extracting features from the image data by using a multi-branch hybrid pooling convolutional neural network to obtain an image feature vector; the feature fusion module is used for constructing a cross-parameter channel correlation matrix through a cross-modal feature channel attention mechanism and outputting a multi-parameter collaborative representation vector; and the alarm generation module inputs the multi-parameter collaborative representation vector into the integrated learning anomaly discrimination model, outputs an anomaly probability value and generates a multi-stage alarm signal. Therefore, accurate sensing and advanced early warning of the main control geological anomaly are realized, and the alarm accuracy and timeliness are remarkably improved.

The invention discloses a progressive multi-modal target tracking method based on adaptive channel exchange, which is used for solving the problem of modal difference between an infrared image and a visible light image. According to the method, a modal mixing layer design is adopted, and efficient fusion of cross-modal features is realized through a progressive interaction strategy. Specifically, firstly, a cross-modal channel correlation matrix is calculated, a multi-layer perceptron is utilized to predict a channel exchange probability, and a probability re-parameterization technology is adopted in a training stage to realize adaptive channel selection; and then, inputting the mixed multi-modal features into a parameter-shared double-flow Transform network, and carrying out joint feature extraction and relation modeling on the mixed multi-modal features and the parameter-shared double-flow Transform network. According to the method, through progressive feature interaction, the distribution difference between modes is effectively reduced, and the feature fusion effect is improved. According to the method, the accuracy and robustness of multi-modal target tracking can be remarkably improved in a complex environment, and the method is suitable for a multi-sensor visual system in the fields of intelligent monitoring, automatic driving and the like.

The invention relates to a multi-antenna wireless communication system beam forming method and system based on quantum approximate optimization, and belongs to the field of multi-antenna wireless communication, and the method comprises the following steps: S1, constructing a weighted channel correlation matrix reflecting the characteristics of a multi-user comprehensive channel according to channel state information and wireless resource weight; s2, discretizing the continuous phase of each transmitting antenna, sequentially distributing a gradually doubled binary variable for the discrete phase, combining the phases of all antennas to jointly construct a quantum state representation corresponding to a quantum approximate optimization algorithm, and determining a state space corresponding to the quantum approximate optimization algorithm; s3, executing quantum circuit evolution and observation through a quantum approximate optimization algorithm, and updating and optimizing the initial parameters of the power distribution quantum optimization through gradient descent of a classical algorithm to obtain optimal parameters of the power distribution quantum optimization; and S4, converting the optimal power distribution quantum optimization parameter into a phase combination of all antennas to obtain an optimal power distribution beam.

The application discloses a channel state information (CSI) measurement method, a terminal and a network side device, and belongs to the technical field of communication. The CSI measurement method comprises the following steps: a network side device sends CSI measurement configuration information to a terminal, wherein the CSI measurement configuration information is used for configuring target CSI measurement resources associated with target CSI information; and the network side device receives the target CSI information from the terminal. The target CSI information comprises at least one of the following: a Doppler frequency offset, a Doppler spread, a Doppler spectrum, a time domain channel correlation matrix, a delay spread, an average delay, a delay spectrum and a frequency domain channel correlation matrix.

Example implementations include methods, apparatuses, and computer readable media for wireless communication configured to measure a reference signal to estimate a channel using a predefined set of sensing beams. Estimating channels may include generating an N * N channel correlation matrix, where N is a number of antenna elements in an antenna array. Implementations also include generating a set of dynamic beam weights based on the estimated channel to maximize reference signal received power (RSRP). The set of dynamic beam weights may be based on eigenvectors of the channel correlation matrix. In addition, embodiments also include applying the dynamic set of beam weights to the antenna array.

The invention discloses a channel modeling and performance analysis method and system for a super-large-scale MIMO system. The method comprises the following steps: constructing an XL-MIMO system channel model; explicitly introducing a non-ideal channel state information (CSI) factor into the channel model, and constructing a composite channel matrix containing a channel estimation error; a visible region diagonal matrix is introduced, and a spatial correlation matrix of a stable XL-MIMO channel is combined to further decompose a transmitting end correlation matrix; calculating elements of a transmitting end correlation matrix and a receiving end channel correlation matrix by adopting a spherical wave hypothesis; combining the constructed channel model with an actual communication link, and establishing a received signal model; and deriving a compact lower bound expression of the traversal capacity of the system based on the established channel model. According to the method, an accurate and efficient theoretical tool and design guidance can be provided for design, deployment and performance evaluation of a large-scale antenna array in a 6G communication system.

This application proposes a channel measurement method, apparatus, terminal, chip, and storage medium, relating to the field of communications. The method includes: performing channel estimation based on CSI-RS to obtain a target channel estimation result; determining an equivalent channel correlation matrix and channel quality metric parameters for at least one precoding matrix based on the target channel estimation result; classifying channel correlations based on the equivalent channel correlation matrix, channel quality metric parameters, and communication configuration parameters of the communication scenario in which the CSI-RS is located, obtaining a classification result; wherein the classification result is used to indicate the spatial correlation of the communication channel corresponding to the precoding matrix. Therefore, it eliminates the need for pre-discretization of the communication scenario or manually setting a universal threshold, avoiding correlation judgment bias caused by overly coarse scenario division granularity or insufficient threshold adaptability. This allows the classification result to adapt to the dynamic changes of the actual channel environment, significantly improving the accuracy and robustness of channel spatial correlation identification.

The present invention relates to a RIS‑IM‑NOMA system design method based on channel distribution information, and belongs to the field of communication technology. The method comprises the following steps: S1: establishing a channel model constructed using a channel correlation matrix; S2: establishing a RIS‑IM‑NOMA‑CDI system model, defining a reflection coefficient matrix and a detection method; S3: deriving and analyzing the theoretical upper limit of the bit error rate of the system model; S4: theoretically analyzing the spectrum efficiency performance of the system model, introducing an interruption probability limit to propose the fairness problem of spectrum efficiency between users; S5: optimizing the power allocation method. The present invention utilizes channel distribution information to construct a channel for data transmission through the channel correlation matrix observed by the transmitting end, adopts a greedy detection method to perform signal detection at the receiving end and derives the theoretical upper limit of the bit error rate, and discusses the optimal power allocation method under the fairness problem.

A method (100) for transmitting a binary data sequence over a Doppler-spread OTFS (Orthogonal Time Frequency Space) communication channel, comprising: - receiving (102) a binary data sequence (b) to be transmitted, - encoding (104) the binary data sequence (b) in a channel encoder which generates a coded bit stream (b c ) outputs, - mapping (108) the bit stream (b c ) to data symbols (x d ), - Superimposing (118) pilot signals (x sp ) via the data signals (x d ), to receive a transmission signal (x), - Modulating (120) the transmission signal (x), and - transmitting (122) the modulated signal over the OTFS communication channel, the method further comprising, prior to the overlaying (118): - receiving (110) information concerning OTFS system parameters, including one or more of the number of delay ranges (M), the number of Doppler ranges (N), the carrier frequency (f c ), and the subcarrier spacing (Δf), - receiving (112) information concerning the OTFS communication channel, including one or more of the channel correlation matrix (R hh ), the channel length (L), the BEM order (Q), and the Gaussian noise variance (σ 2 ), - determining (114) a power allocation ratio (α*) of the pilot signals to be used for the superposition (118) exclusively on the basis of the received system parameters and the received channel information, and - Providing (116) the determined power allocation ratio (α*) of the pilot signals to the superposition step (118).

The application provides a stroke patient oral cavity weakness intelligence evaluation method and system, the evaluation method comprises: acquiring video data and extracting a key frame image sequence containing a target function area, inputting a three-dimensional convolutional neural network to extract an initial space-time feature map containing a channel, time and space dimensions; a grade condition vector and a channel correlation matrix are generated in parallel, the former is obtained by aggregating global context descriptors from the initial space-time feature map and a condition generation module, representing the probability of each potential evaluation grade, and the latter is constructed based on channel dimension information to represent the intrinsic correlation within the channel; candidate channel attention parameters are generated guided by the grade condition vector, after adjustment by the channel correlation matrix, weighted fusion is performed according to the grade probability, and Sigmoid activation is performed to obtain the channel attention weight; the attention weight is applied to the initial space-time feature map for feature re-calibration, and the re-calibrated feature map is input into a classification network to obtain the evaluation grade.

The present application relates to a kind of quantum approximation optimization-based multi-antenna wireless communication system beam forming method, system, belong to multi-antenna wireless communication field, comprising the following steps: S1, according to the channel state information and the wireless resource weight, construct the weighted channel correlation matrix reflecting the characteristics of multi-user integrated channel;S2, the continuous phase of each transmitting antenna is discretized, and a gradually doubled binary variable is sequentially assigned to the discrete phase, the phase combination of all antennas is jointly constructed to determine the state space corresponding to quantum approximation optimization algorithm corresponding to quantum state representation of quantum approximation optimization algorithm;S3, by quantum approximation optimization algorithm, quantum circuit evolution and observation are executed, and the power allocation quantum optimization initial parameter is updated and optimized by the gradient descent of classical algorithm, to obtain power allocation quantum optimization optimal parameter;S4, power allocation quantum optimization optimal parameter is converted into the phase combination of all antennas, and the optimal power allocation beam is obtained.

The application discloses a noise estimation method, device, storage medium and computer program product, relates to the technical field of communication, and the method comprises the following steps: determining a noise-free frequency domain channel correlation matrix and a least square channel estimation based on a pilot signal; performing eigenvalue decomposition on the noise-free frequency domain channel correlation matrix to obtain a noise space eigenvector; and calculating noise estimation by projecting the noise space eigenvector to the least square channel estimation. The application solves the problem that the traditional method depending on a signal space is greatly affected by the filter passband width and the passband ripple, and has a large error in a high SNR multipath channel, improves the accuracy of noise estimation, improves the performance of equalization under high SNR high-order modulation, reduces BLER, solves the problem of large correlation matrix calculation amount, and reduces the complexity of system design.

Methods and apparatus for beam management where a beam is selected based on an estimated channel correlation matrix. The apparatus determines a channel correlation matrix based on downlink SSB reference signal received at the UE. The apparatus estimates a RSRP of a plurality of beams associated with an uplink channel based on the channel correlation matrix and associated beam weights. The apparatus selects a first beam from the plurality of beams having a highest estimated RSRP for communication with a base station. The apparatus communicates with the base station via the first beam.