44 results about "Channel knowledge" patented technology

The invention discloses a workflow-based multi-modal water conservancy large model decision support method, which comprises the following steps of: S1, preprocessing multi-modal input data based on a local model, and converting a data format; s2, constructing a modular workflow supporting parallel processing through a specified DSL (Digital Subscriber Line) framework; s3, constructing a feature model suitable for the water conservancy field through the multi-modal data subjected to synchronous acquisition and processing, and performing field adaptation and fine adjustment on the feature model; s4, on the basis of the feature model subjected to domain adaptation and fine adjustment, a specified workflow is constructed, and multi-modal task resources are dynamically scheduled; and S5, combining a local knowledge base with networking data by using a dynamic fusion algorithm to realize dual-channel knowledge fusion for executing the multi-modal task in the S4 in parallel. According to the invention, real-time acquisition and parallel processing of multi-modal data can be realized, and the stability of a local knowledge base and the real-time performance of networking data are dynamically balanced through a dual-channel knowledge fusion mechanism.

The invention discloses a dual-channel sparse distillation-based multi-modal model lightweight method, which comprises the following five core steps of: connecting a pre-training visual encoder and a language model, and constructing a hybrid expert architecture; vision-language feature preliminary alignment is realized through feature mapping and cross-modal attention; two-channel knowledge migration is designed, an explicit channel uses adaptive KL divergence to align teacher and student model output distribution, and an implicit channel migrates feature knowledge through a cross-modal attention adapter; reasoning optimization training is carried out on the training set by constructing positive and negative samples, and student model learning is guided to distinguish high-quality output and low-quality output; top-k experts are dynamically selected during reasoning deployment, and expert output is weighted and aggregated through routing weights. According to the method, the calculation overhead is remarkably reduced while the expression ability of the model is maintained, the parameter quantity is reduced by about 60%, the accuracy rate of a cross-modal understanding task is improved by more than 5%, and the reasoning delay on edge equipment is controlled within 300ms.

The invention discloses a rapid communication method based on wireless channel knowledge map construction, and relates to the technical field of sixth generation communication, the method comprises the following steps: collecting a channel parameter matrix of a user in communication, processing to generate a standard channel parameter matrix of each user, and constructing an adjacent matrix based on the spatial distance of the user; carrying out feature aggregation and relevance capture on the standard channel parameter matrix by adopting a graph auto-encoder, generating a coding feature matrix, and mapping the coding feature matrix into a reconstructed adjacent matrix to verify whether the coding feature matrix is output or not; a partition clustering algorithm based on community discovery is adopted, the coding feature vectors of all the users are iteratively merged, a terminal community set is generated, and a centroid feature matrix is calculated; a weight prediction network is adopted to carry out dimension expansion mapping on a target position, the target position is spliced with the centroid feature matrix, a weight coefficient vector is obtained through a multi-head attention mechanism and dimension expansion mapping, a channel parameter matrix of each user is weighted and summed, and a channel parameter matrix of the target position is obtained; and accurately constructing a channel knowledge map and establishing a communication process between the base station and the user.

The present application relates to the field of communications, and discloses a wireless communication method and apparatus, and a device. The wireless communication method in embodiments of the present application comprises: a terminal acquires first information; the terminal executes a first operation on the basis of the first information, wherein the first information comprises at least one of the following: at least one set of channel knowledge maps, environment information of at least one cell and position information of at least one cell or base station or TRP, a cell list associated with at least one area, and a cell list associated with a target scenario; wherein the first operation comprises at least one of the following: making a cell handover decision, making a cell reselection decision, and determining whether to send a first uplink signal.

This invention discloses a fully passive jammer and a fully passive jamming method for channel knowledge map scenarios. The jammer includes: a data acquisition module for generating a multi-state channel dataset by traversing and controlling the configuration of a reconfigurable smart metasurface and performing channel simulation; a map construction module for constructing a channel path map based on channel state information; a beamforming parameter determination module for determining fixed hybrid precoding parameters based on the channel path map in an interference-free reference scenario; and a performance evaluation module for reusing the fixed precoding parameters and calculating the communication rate based on the real channel state in a dynamic scenario with disco-type reconfigurable smart metasurface interference, thereby quantifying performance loss. This invention transforms dynamic interference into an analyzable channel sequence through multiple simulation sampling, separates beam selection from rate evaluation, objectively quantifies mismatch penalties, and provides a standardized evaluation platform for countermeasure research on disco-type reconfigurable smart metasurfaces.

The invention relates to an antenna clamping system joint beam forming optimization method based on a channel knowledge map. The method comprises the following steps: acquiring the channel knowledge map covering a target service area; constructing a predicted equivalent channel matrix from a base station to a user according to the channel knowledge map and the multi-waveguide architecture of the clamping antenna system; the method comprises the following steps of: establishing an optimization problem which takes a base station transmitting beam forming matrix and all clamping antenna positions as joint optimization variables by taking maximization of system downlink performance as a target; solving the optimization problem by adopting an alternating optimization algorithm to obtain an optimal transmitting beam forming weight and an optimal antenna clamping position; and according to a solving result, configuring a transmitting beam former of the base station, and controlling a clamping antenna driving system to move each clamping antenna to an optimal position. Through fusion of a channel knowledge map and a clamping antenna structure, a prediction channel model is constructed, joint optimization is converted into a deterministic problem of prior assistance, real-time channel dependence is reduced, overhead and delay are reduced, and system performance is improved.

A method for a channel knowledge map and a communication device, in the method, a first terminal device can report a first position, first perception information and first channel state information, so that a first network element can construct the channel knowledge map, and because the perception information exists in the channel knowledge map, the perception information can be used as position auxiliary information, so that the channel knowledge map can be used as position auxiliary information. The method and the device are used for assisting in identifying terminal equipment close to a real position, improving the accuracy of a channel knowledge map and reducing the influence of a positioning error on the channel knowledge map.

The invention discloses a motor-oriented fault mode knowledge graph construction and updating method and a motor-oriented fault mode knowledge graph construction and updating system. The motor-oriented fault mode knowledge graph construction and updating method comprises the following steps: constructing a motor fault domain ontology model: defining a core concept class hierarchy, an object attribute and a semantic relationship, obtaining structured data and unstructured data, defining a data attribute, and setting a logic constraint and an axiom; performing double-channel knowledge extraction: performing knowledge extraction on the unstructured data by adopting a deep learning model, performing knowledge extraction on the structured data by adopting a mapping method, and generating original triple knowledge; knowledge fusion: integrating knowledge from different data sources through entity alignment, attribute fusion and conflict resolution to construct a high-quality and unambiguous knowledge graph, and storing the high-quality and unambiguous knowledge graph in a graph database; and performing intelligent fault reasoning and dynamic updating based on the knowledge graph. According to the method, multi-source data are fused, long and difficult entities and complex semantic relationships are identified more accurately, and the accuracy and efficiency of troubleshooting are improved through a double weighting algorithm.

The invention provides a microchannel knowledge map construction method fusing an environment topology model, and relates to the technical field of wireless communication, and the method comprises the steps: S1, constructing environment information conditions which comprise a signal intensity sampling map, KNN interpolation enhancement input, an LoS map, a building height map in a task area, and a base station position map; s2, taking an environment information condition as input, extracting environment deep information through a convolutional network encoder, and representing the environment deep information as high-dimensional environment topological information; s3, acquiring an environment information vector corresponding to the target position based on the high-dimensional environment topology information; s4, the target position and the corresponding environment information vector are spliced to serve as input, the received signal strength serves as a label, and a regression network is trained; and S5, predicting the received signal strength in the task area based on the trained regression network to obtain a microchannel knowledge map. According to the method, the construction precision of the channel knowledge map can be effectively improved.

The invention discloses an underwater image enhancement method based on cross knowledge embedding learning, and the method comprises the steps: embedding the red channel knowledge, contrast knowledge and gradient knowledge of an underwater image into a deep learning model, and improving the adaptability of the deep learning model to an underwater optical scene based on the common guidance of a plurality of knowledge. The problems of wavelength selective attenuation and forward and backward scattering in the underwater imaging process are comprehensively solved, and the data quality of underwater images is effectively improved.

The invention provides a statistical prior assisted multi-user scheduling method, which comprises the following steps of: firstly, establishing a statistical prior channel knowledge map from a user geographic position to channel state information based on regional division under the coverage of a base station and long-term observed channel data, and evaluating the reliability of statistical prior; and then selecting scheduling users by adopting a two-stage user screening method. In the first stage, all sub-regions covered by a base station are grouped by using statistical prior information, and a preliminary active user set is obtained by using the groups. And in the second stage, the correlation between the users is taken as a criterion, and orthogonal users are selected by using an angle domain power spectrum. In the first stage, a limited number of active users can be selected under the scene of massive users, and the pilot frequency overhead of channel estimation is reduced. In the second stage, an effective orthogonal user set can be selected by using instantaneous channel information and statistical prior, and the whole algorithm has relatively low calculation complexity.

The application discloses a kind of based on adaptive channel sensing and tracking method of channel knowledge base, comprising the following steps: defining wireless channel in MIMO system, channel knowledge base and channel observation model;Based on channel knowledge base and channel sensing sequence, define the joint probability distribution based on channel measurement sequence, channel sequence, user position sequence;Adopt the switching Kalman filter architecture of fusion channel knowledge base, realize channel sensing matrix adaptive design, and carry out user positioning and channel tracking.The application is based on historical low-dimensional (relative to high-dimensional channel) channel observation to predict high-dimensional channel, according to channel prediction error to design channel sensing matrix to be used for real-time channel measurement, to realize the differential entropy minimization of channel estimation;And based on the low-dimensional channel measurement observed by adaptive channel sensing matrix, utilize the channel space-time statistical distribution (channel covariance matrix) provided in channel knowledge base and switching Kalman filtering technology to realize channel tracking.

The present invention discloses a workflow-based multimodal water conservancy large-scale model decision support method, comprising: S1. pre-processing multimodal input data based on a local model and converting the data format; S2. constructing a modular workflow that supports parallel processing by specifying a DSL framework; S3. constructing a feature model adapted to the water conservancy field by synchronously collecting and processing multimodal data, and performing domain adaptation and fine-tuning on the feature model; S4. constructing a specified workflow based on the domain-adapted and fine-tuned feature model to dynamically schedule multimodal task resources; S5. using a dynamic fusion algorithm to combine a local knowledge base with networked data to achieve dual-channel knowledge fusion for parallel execution of the multimodal tasks in S4. The present invention can achieve real-time collection and parallel processing of multimodal data, and through a dual-channel knowledge fusion mechanism, dynamically balance the stability of the local knowledge base with the real-time performance of the networked data.

The application discloses a wireless communication environment change perception method based on a channel knowledge map, relates to the technical field of perception, and acquires the position and channel information of users in a local area and calls the position and channel information of multiple grid points; the actual departure direction and the actual arrival direction are calculated according to the path information of each reflection path of the user, a maximum likelihood estimation problem is constructed, an alternating iterative optimization is adopted to obtain the optimal departure direction and the optimal arrival direction, and the initial estimated position of the reflection point of each reflection path of the user is calculated; based on the mirror consistency principle, a clustering correction algorithm is adopted to determine real-time mirror base station classes and iteratively correct the initial estimated position of the reflection point to obtain the estimated position of the reflection point in the real-time mirror base station class; the position and channel information of the grid points are processed in the same way to obtain historical mirror base station classes, the real-time mirror base station classes and the historical mirror base station classes are matched based on the minimum matching principle, and the reflection surface change condition is tested and judged, so that efficient and high-precision environment perception is realized.

The application relates to the technical field of wireless communication, in particular to a channel knowledge map construction method based on an improved Transform and an improved Transform system. The channel knowledge map construction method comprises the following steps: acquiring an overhead snapshot of a target communication area and encoding the overhead snapshot into an n*n binary environment coding matrix E, wherein a matrix element '1' represents that a corresponding subarea has a building, and a matrix element '0' represents that the corresponding subarea has no building; inputting the binary environment coding matrix E into a pre-trained improved Transform neural network model, wherein the improved Transform neural network model is a pure decoder model constructed based on a Transform decoder architecture, and a standard Transform encoder part and a multi-head encoder-decoder attention layer are removed; and outputting a corresponding path loss prediction matrix H through the improved Transform neural network model to complete the channel knowledge map construction of the target communication area.

The invention discloses a channel knowledge map generation method based on a denoising diffusion probability model, and the method comprises the steps: generating high-precision channel data through the mapping relation between environment coordinate features and CSI data through DDPM. Compared with the existing method, the method has the advantages that the measurement overhead is reduced, the accuracy and adaptability of data generation are improved, and an efficient and low-cost solution is provided for generation of the channel knowledge map in a wireless communication system.

The present invention discloses a low pilot overhead channel estimation method based on a user-side channel knowledge map, which belongs to the field of channel estimation. The method includes: the user queries the user-side channel knowledge map locally according to its own location information; the user negotiates with the base station on the number of pilots used for the current communication based on the channel knowledge map; based on the large-scale downlink wireless millimeter wave channel model, the transmission azimuth angle domain is gridded and a suitable alternative function is found to replace the maximum likelihood function; the noise coefficient, gain matrix variance and angle offset are repeatedly iteratively optimized and solved using the prior information provided by the user-side channel knowledge map; the gain channel matrix is ​​accurately estimated based on the prior information provided by the locally queried user-side channel knowledge map and the received pilot data. The actual number of pilots required for transmission and reception in the present invention is relatively small, and the channel estimation result is comparable to the accuracy of traditional channel estimation.

The invention provides a channel state information prediction method and related equipment. The channel state information prediction method comprises the following steps: acquiring position information of a transmitting end and a receiving end of a signal and geometric information of a scatterer in an environment where the transmitting end and the receiving end are located; determining electromagnetic environment knowledge from the transmitting end to the receiving end according to the position information and the geometric information, wherein the electromagnetic environment knowledge is used for representing a relationship between channel information and environment information between the transmitting end and the receiving end; coding the electromagnetic environment knowledge into a channel knowledge vector based on a pre-trained scene embedding model; searching an embedded vector of which the similarity with the channel knowledge vector is higher than a threshold value from a pre-constructed channel knowledge base; obtaining one or more channel responses corresponding to the searched embedded vector; and fusing the searched channel responses corresponding to the one or more embedded vectors to obtain a channel state information prediction result of the sending end and the receiving end. According to the method, the service delay is greatly reduced, and the harsh requirements of scenes such as low-delay high-reliability communication and the like are met.

The invention relates to the technical field of group intelligent knowledge updating, in particular to a group intelligent knowledge updating method and device in a resource limited scene, and the method comprises the steps: constructing a dual-channel knowledge interaction architecture applied to each intelligent node, the dual-channel knowledge interaction architecture comprising a prototype collaborative sharing channel and a parameter collaborative sharing channel; based on the prototype collaborative sharing channel, supporting interaction of local prototypes among the intelligent nodes, performing global semantic alignment operation on similar prototypes of the intelligent nodes to generate a global prototype, and performing constraint correction on feature parameters of the intelligent nodes through the global prototype; and based on the parameter collaborative sharing channel, supporting interaction of the feature parameters of the intelligent nodes after prototype correction. Therefore, the problems of lack of effective knowledge sharing, inconsistent prototype semantics, unstable model updating and the like among the group intelligent nodes under the condition of resource limitation can be solved.

The application discloses a downlink channel knowledge map (CKM) construction method and a channel estimation method based on a diffusion model, and belongs to the technical field of wireless communication. The method divides the service area of a base station into a grid, combines pilot data and position tags of a receiving device to construct an offline training data set, and uses a diffusion model to learn channel statistical information of different positions to generate a CKM. In the channel estimation stage, based on the CKM and a small amount of pilot data, noise is gradually removed through the reverse process of the diffusion model to realize efficient channel estimation. Compared with traditional methods, the application significantly reduces the number of pilot requirements (only 1-2 pilots are needed), improves the estimation accuracy (the NMSE is reduced by more than 40%), and is suitable for high-density MIMO systems and is applicable to 6G environment perception communication scenarios.

A method for a channel knowledge map and a communication apparatus. In the method, a first terminal device may report a first position, first sensing information, and first channel state information, so that a first network element may construct a channel knowledge map. Sensing information exists in a channel knowledge map, and can be used as position assistance information to assist in identifying a terminal device having a similar real position, thereby improving the accuracy of the channel knowledge map and reducing the impact of a positioning error on the channel knowledge map.

The invention discloses a wireless communication environment change sensing method based on a channel knowledge map, and relates to the technical field of sensing, and the method comprises the steps: obtaining the position and channel information of a user in a local region, and calling the positions and channel information of a plurality of grid points; calculating an actual departure direction and an actual arrival direction according to path information of each reflection path of the user, constructing a maximum likelihood estimation problem, solving an optimal departure direction and an optimal arrival direction by adopting alternating iterative optimization, and calculating an initial estimation position of a reflection point of each reflection path of the user; based on a mirror image consistency principle, determining a real-time mirror image base station class by adopting a clustering correction algorithm and iteratively correcting the reflection point initial estimation position to obtain a reflection point estimation position in the real-time mirror image base station class; the positions and channel information of the grid points are processed in the same mode to obtain a historical mirror base station class, the real-time mirror base station class and the historical mirror base station class are matched based on the minimum matching principle for testing, the change condition of the reflecting surface is judged, and efficient and high-precision environment perception is achieved.

The invention relates to the technical field of wireless communication, in particular to an improved Transform model, a channel knowledge map construction method and a construction precision calculation method. The channel knowledge map construction method comprises the following steps that an overlook snapshot graph of a target communication area is obtained, the overlook snapshot graph is coded into an n * n binary environment coding matrix E, a matrix element 1 represents that a building exists in a corresponding sub-area, and a matrix element 0 represents that no building exists in the corresponding sub-area; the binary environment coding matrix E is input to a pre-trained improved Transform neural network model, the improved Transform neural network model is a pure decoder model constructed based on a Transform decoder architecture, and a standard Transform encoder part and a multi-head encoder-decoder attention layer are removed; and carrying out processing through the improved Transform neural network model, and outputting a corresponding path loss prediction matrix H so as to complete channel knowledge map construction of the target communication area.

The invention provides an access method and device based on a large model, relates to the field of multi-source heterogeneous carrier access, and solves the technical problems of difficulty in unified processing of multi-source heterogeneous carriers, insufficient field professional semantic understanding and poor rule engine generalization in the existing bidding access technology. The method specifically comprises the steps of obtaining first data of bidding and tendering; preprocessing the first data based on a vision-language combined model to determine a coordinate semantic map; performing deep semantic understanding on the coordinate semantic map based on a two-channel knowledge engine to obtain a first result; query information input by the user is obtained, the query information is converted into SQL query matched with the multiple databases, and the query information is used for querying information needed by the user from the first result; performing three-level credibility guarantee verification on the coordinate semantic map and the SQL query result, and determining a verification result; and if the verification is passed, taking the SQL query result as a structured access result. The method is used for bidding and data fetching.

The application discloses a mobile target detection method and device based on a cell-free network, equipment and products, and relates to the technical field of wireless network communication. The method comprises the following steps: determining n target detection periods based on target variation degrees, and acquiring predicted target positions in the n target detection periods; evaluating channel states of each node to the predicted target positions and a fusion center according to the predicted target positions in the n target detection periods and a channel knowledge map; acquiring optimal node combinations and corresponding optimal periods by calculating normalized traces based on a fusion covariance and the channel knowledge map; performing target detection based on the optimal node combinations and the corresponding optimal periods, and estimating a target position by fusing node detection information obtained through target detection, wherein the node detection information comprises information detected by all nodes participating in detection. The method realizes continuous and accurate tracking of the target position, improves the detection accuracy, and guarantees the communication efficiency.

The invention discloses a backscattering label deployment method based on a channel knowledge graph. The backscattering label deployment method comprises the following steps: acquiring actual channel data in an offline measurement environment; constructing a spatial perception model of wireless signal propagation by adopting a spatial loss field modeling method; modeling shadow fading by using a Gaussian process regression method, deducing shadow loss distribution at any position, and finally constructing a wireless channel knowledge graph; analyzing interference distribution according to the atlas, selecting an area with the minimum interference as a deployment position of a backscattering label, and selecting a proper precoding strategy according to link characteristics; in the actual deployment stage, the communication quality of each position is evaluated through the signal to interference plus noise ratio, the deployment position and system parameters are adjusted, and accurate deployment of the backscattering label is completed. According to the method, backscattering label deployment is optimized through the wireless channel knowledge graph, accurate interference prediction and environment adaptability are provided, and an efficient and low-cost solution is provided for the future 6G passive Internet of Things.

The invention discloses a scheduling optimization method for realizing a multi-active IRS-assisted multi-user system based on a channel knowledge graph, and relates to the technical field of wireless communication. A channel knowledge graph for outputting user statistical performance indexes is constructed based on a deep neural network, and an efficient user time-frequency resource scheduling scheme is designed in a multi-user scene by using the graph. According to the method, the requirement for obtaining instant channel state information of a multi-IRS multi-user full link is avoided, the overhead of channel estimation is effectively reduced, and the channel knowledge graph based on the deep neural network can efficiently carry out sample storage and fitting inference to obtain statistical performance indexes of users. According to the designed GS-KMeans-IB algorithm based on the channel mapping knowledge domain, a multi-user time-frequency resource scheduling scheme can be rapidly given, and the corresponding performance effect is close to the performance upper bound obtained through Gurobi solving. The scheduling optimization method has important practical application significance on the scheduling problem of the multi-active IRS assisted multi-user system.

The application discloses a fast communication method based on a wireless channel knowledge map, and relates to the field of the sixth generation communication technology. Channel parameter matrices of users in communication are collected and processed to generate standard channel parameter matrices of each user, and an adjacency matrix is constructed based on the spatial distance of the users. A graph auto-encoder is used to aggregate features and capture relevance of the standard channel parameter matrices, to generate an encoded feature matrix and map it to a reconstructed adjacency matrix to verify whether to output. A partition clustering algorithm based on community discovery is used to iteratively merge the encoded feature vectors of each user, to generate a final community set and calculate a centroid feature matrix. A weight prediction network is used to expand and map a target position, and the centroid feature matrix is spliced, a weight coefficient vector is obtained through a multi-head attention mechanism and expansion mapping, and the channel parameter matrices of each user are weighted and summed to obtain the channel parameter matrix of the target position, so that the channel knowledge map is accurately constructed and used to establish the communication process of the base station and the user.

The invention provides a statistical prior assisted minimum mean square error interference suppression combined receiver method, which comprises the following steps of: firstly, constructing a special channel knowledge map for each interference cell; then, further performing grid division on the channel knowledge map, and realizing dynamic grid merging and hybrid time scale updating through reliability in space and time dimensions; besides, a frequency domain interference covariance matrix sharing mechanism based on channel correlation in a coherent bandwidth and an eigenvalue decomposition and compression method based on beam domain sparsity are introduced, so that the storage requirement and the calculation complexity of the channel knowledge map are further reduced; and finally, based on an interference cell user scheduling result, obtaining a joint interference covariance matrix in combination with the channel knowledge map, and applying the joint interference covariance matrix to a minimum mean square error interference suppression combination receiver. According to the method, the interference suppression performance of the minimum mean square error interference suppression merging receiver can be improved, the robustness and adaptability of the system are enhanced, and the method has relatively high practical value and popularization potential.

The invention provides a knowledge base enhanced semantic communication method and electronic equipment. According to the method, channel parameter characteristics can be determined according to a semantic feature map of a target image, and the channel parameter characteristics can be input into a pre-trained first channel knowledge base to obtain a corresponding channel parameter limit value; performing limitation judgment on the channel parameter characteristics by utilizing the channel parameter limitation value, determining whether characteristic points corresponding to the channel parameter characteristics need to be transmitted by adopting indexes or not, and determining index transmission data which can be transmitted by adopting the indexes; then the semantic feature map is jointly coded through an information source channel to obtain a semantic code word sequence, the semantic code word sequence is transmitted to a receiving end through a traditional channel, and accurate reply of information can be achieved; when the index transmission data is transmitted, the index transmission data is transmitted in an error control mode, and due to the fact that the data size of the index transmission data is small, the occupied transmission bandwidth can be reduced, and the cliff effect under low signal-to-noise ratio transmission can be effectively relieved.