88 results about "Radio map" patented technology

The invention provides an ultra-low-altitude-domain three-dimensional radio map generation system, and the system comprises a three-dimensional grid division and data preprocessing module which is used for dividing a target region into voxel grids containing latitude and longitude and height coordinates; the satellite remote sensing image feature extraction module is used for generating environment high-dimensional image features of the target area; the communication base station parameter encoder is used for generating base station space propagation characteristics of the target area; the two-dimensional feature migration and three-dimensional feature generation module is used for generating initial three-dimensional feature representation; the three-dimensional interpolation and height correction module is used for outputting corrected three-dimensional RSRP features; and the multi-modal fusion and decoding module is used for fusing the corrected three-dimensional RSRP features, the environment high-dimensional image features and the base station space propagation features, and inputting a fusion result into a three-dimensional decoding network to generate a final three-dimensional radio signal power distribution diagram. The three-dimensional radio map is generated for the urban low-altitude scene.

The invention provides a radio map and object distribution map generation method, a switching model and a training method thereof, and the training method comprises the following steps: generating a non-aligned training set: sampling to generate a scatter radio map based on any actual global radio map in an actual global radio map set; inputting the scattered point radio map and the signal transceiver distribution information into a first generator to obtain an estimated object distribution map; inputting the estimated object distribution map and the signal transceiver distribution information into a second generator to obtain an estimated global radio map; inputting any actual object distribution diagram in the actual object distribution diagram set and the distribution information of the signal transceiver into a second generator to obtain a simulated global radio map; inputting the simulated global radio map and the signal transceiver distribution information into a first generator to obtain a simulated object distribution map; and updating model parameters. According to the method, object distribution and global accurate estimation are realized by using scattered RSRP signals.

The invention relates to the technical field of wireless communication, and provides a radio map reconstruction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the environment information and sparse measurement of a target region; inputting the environment information into a trained prediction network to obtain a basic radio map output by the prediction network; inputting the basic radio map and the sparse measurement into a trained correction network to obtain a precise radio map output by the correction network; wherein the prediction network and the correction network are both obtained based on adversarial training between the generator and the discriminator. According to the radio map reconstruction method and device, the electronic equipment and the storage medium provided by the invention, the optimal result can be obtained by utilizing the existing available information, so that the balance among efficiency, cost and precision is realized.

The invention discloses a deep learning-driven multi-base-station radio map rapid construction method and system. The method comprises the steps of generating a radio map original data set of a current environment; processing the radio map original data set to obtain a radio map sample data set; and constructing a neural network model, and training the neural network model according to the radio map sample data set to obtain a radio map construction model. According to the method, the learning ability and generalization performance of the model are enhanced through residual connection and batch normalization by adopting a double-path neural network architecture, so that comprehensive processing of signals of multiple base stations and rapid and high-precision radio map prediction are realized, and the requirements of real-time network planning and optimization are met. Furthermore, by processing the radio map original data set, each pixel data in the image includes path gain information, thereby providing an accurate signal propagation path and improving identification accuracy.

The invention provides a satellite image and actually measured RSRP fused ground radio map generation system, and the system comprises a satellite image feature extraction module which is used for converting a high-resolution satellite image of a target region into high-dimensional image features reflecting the ground feature type distribution of the region; the base station physical characteristic input module is used for converting the physical parameters of the communication base station in the target area into characteristic vectors reflecting electromagnetic propagation characteristics; the multi-modal fusion module is used for fusing the high-dimensional image features and the feature vectors to generate joint feature representation; the radio map decoder is used for generating a radio signal power plane distribution diagram of the target area through a decoding network based on the joint feature representation; and the ground RSRP correction module is used for correcting the radio signal power plane distribution diagram output by the radio map decoder so as to output a final radio signal power distribution result. According to the invention, the space-ground collaborative data source fusion is realized to improve the mapping precision.

The invention discloses a radio map sensing method and device based on adaptive sampling, and the method comprises the steps: obtaining radio map data of a target region, carrying out the Gaussian fitting of the radio map data, and obtaining a radio map model; sampling operation is carried out in a preset unmanned aerial vehicle control space to obtain a sampling control sequence, and a sampling path is obtained based on the sampling control sequence and a preset unmanned aerial vehicle state transition model; predicting each sampling point in each sampling path through a radio map model to obtain signal intensity and variance; on the basis of the signal intensity and the variance, constructing a reward function by using a confidence boundary architecture on a Gaussian process; constructing an optimization objective function based on a radio map model, a preset unmanned aerial vehicle state transition model and the reward function; and solving the optimization objective function to obtain an optimal control sequence of the unmanned aerial vehicle, and generating an optimal sampling path based on the optimal control sequence. And the map precision and the sampling robustness can be improved.

The invention discloses a multi-signal-source scene-oriented generative radio map collaborative estimation method, which comprises the following steps of: S1, sampling received signal strength of a plurality of users on a plurality of frequencies to construct a matrix form, and decomposing by adopting an NMF matrix decomposition method to obtain an NMF matrix; each decomposed received signal strength component corresponds to an independent signal source; s2, constructing and training a radio map inference model of a single signal source; and S3, for each received signal strength component obtained by decomposition in the step S1, estimating correspondence by using a radio map reasoning model of a single signal source. According to the method, the signal intensity components corresponding to multiple signal sources are decomposed by adopting the matrix decomposition theory, and the neural network is trained by utilizing the learning algorithm based on the GAN, so that the accuracy and generalization ability of the system are improved. The method can effectively work under the condition that geographic data is inaccurate or matrix decomposition results have errors.

The invention provides a two-dimensional radio map generation system fusing communication base station and environment information, and the system comprises a base station encoder which is used for converting physical parameters of a communication base station in a target region into feature vectors reflecting electromagnetic rebroadcasting features; the environment encoder is used for converting the map of the target area containing the environment information into environment image features reflecting the global features of the target area; the environment image features and the feature vectors are fused to generate joint feature representation; and the radio map decoder is used for generating a radio signal power plane distribution diagram of the target area through a decoding network based on the joint feature representation. The accuracy of the two-dimensional radio map is effectively improved.

Embodiments of the present disclosure relate to differentiation of ray tracing of radio maps. Systems and methods are disclosed for using path replay backpropagation to efficiently compute a radio map. In an embodiment, an electric field of a propagating wave and its interaction with the environment is represented using the Stokes-Müller formalism. Instead of storing information needed for conventional backpropagation during the forward pass, in an embodiment, only the loss, loss gradients, and optionally information needed to retrace the paths that contribute to the loss are stored because replay backpropagation propagates gradients in a second forward pass. The loss gradients from the first forward pass are used during the second forward pass when paths are replayed to accumulate the loss gradients with additional gradients resulting from interactions with scattering surfaces.

The invention discloses a radio map prediction method and device fusing a physical equation and deep learning, and belongs to the technical field of communication, and the method comprises the steps: inputting an environment map of a to-be-detected environment and the position information of a base station into a trained singular point map prediction network, and obtaining a singular point map of the to-be-detected environment; wherein the singular point map prediction network is trained according to a sample singular point map, and the sample singular point map is calculated according to a Helmholtz equation; and inputting the singular point map, the environment map and the base station position information into the trained radio map prediction network to obtain a radio map of the environment to be measured. The method is low in calculation complexity, wide in application range and good in prediction precision.

The invention belongs to the technical field of radio wave propagation prediction and deep learning, discloses an urban environment radio map prediction method, system and device and a medium, and solves the problem that radio map prediction is difficult and low in efficiency in a complex urban environment. The method comprises the following steps: setting corresponding simulation information by using various environmental element vector data of an urban scene, generating two-dimensional radio maps in different urban environments by using ray tracing simulation, forming a data set for deep learning network training and learning, and disassembling the urban environment into image channels capable of being independently expanded, different actual city scene features are simulated by increasing or combining different image channels, and rapid migration of a small sample new city scene radio map prediction model is achieved; the system, the equipment and the medium are used for implementing the method. Based on multi-channel input feature fusion, a data set is made through ray tracing, and radio map prediction requirements of low cost, low error, high efficiency and strong generalization are met through deep learning training.

The invention provides a path planning optimization method and system based on a radio map. The method comprises the following steps: acquiring an environment distribution map of a target area; the environment distribution map is input into a pre-trained generation model, a deployment potential map corresponding to the region is generated, the intensity value of each pixel represents the contribution degree to the whole target of the target region when one resource is deployed at the corresponding position or the position serves as a path node, and the intensity value is a normalized performance index numerical value; the pre-trained generation model only takes the environment distribution diagram as input and does not depend on any existing resource deployment information; and based on the deployment potential map, identifying peak points of which the intensity values reach a preset value, and determining the peak positions as candidate schemes. The spatial prior for deployment and path planning can be quickly obtained in a complex environment, and the spatial prior can be efficiently fused with constraint factors of different scenes to support large-range and near-real-time planning decisions.

The invention discloses a radio map collaborative estimation method based on a generative adversarial network. The method comprises the following steps: S1, constructing a training data set; s2, adopting a UNet model as a generator, inputting a graphical geographical environment map and RSS information, and outputting a graphical radio map estimation result; s3, adopting a convolutional neural network model, namely a CNN model, as a discriminator for evaluating the authenticity of the radio map output by the generator; s4, in the model training stage, the generator and the discriminator are jointly optimized, that is, the generator and the discriminator are mutually optimized through an adversarial training process, the generator generates a radio map with higher quality, and the discriminator improves the discrimination capability of a generated result; and S5, in a reasoning stage, performing radio map estimation by using a generator. According to the invention, radio map estimation can be carried out based on received signal strength (RSS) information of a plurality of mobile users and a geographical environment map, and signal source information is not needed.

The present invention discloses a communication perception integration method based on SLAM and beam management, wherein the base station and the user first use full-range beam scanning to estimate the optimal beam direction at the current moment. The user uses the angle information obtained in the beam scanning stage and the acceleration measurement output by the inertial measurement unit to execute the SLAM algorithm, obtain the position of the user and environmental features, and complete the user positioning and radio map construction at the current moment. At the next moment, the user uses the inertial measurement unit to predict the user's rough position, generates a priori beam angle search range based on the radio map constructed at the previous moment, and forms an adaptive search codebook for beam tracking. Finally, a switching module is designed to enable the system to switch between the full-range beam scanning and small-range beam tracking modules to ensure the reliability and effectiveness of communication and perception.

The present invention discloses a method and system for planning UAV trajectories based on a radio map in a wind field environment. The method comprises: obtaining radio map initialization parameters and UAV initialization parameters to generate a radio map and a wind vector model; solving a weighted directed graph based on the radio map and the wind field vector model, wherein the weighted directed graph is used to store the energy consumption of each path; initializing the UAV access sequence based on the weighted directed graph, and optimizing the initial UAV access sequence using a genetic algorithm to obtain the optimal UAV access sequence. The present invention initializes the UAV access sequence based on the weighted directed graph, and optimizes the initial UAV access sequence using a genetic algorithm to obtain the optimal UAV access sequence. The energy consumption of UAV tasks in a wind field environment can be obtained through the radio map and the wind field vector model, and then the distribution sequence and UAV trajectory can be reasonably set to improve the distribution efficiency under the premise that the UAV communication quality meets the requirements.

A communication method and a related device, in which a communication device can obtain one or more different radio information through at least two radio map sub-models included in the same radio map model, compared with a mode in which one radio map model can only provide one output, the radio map model can provide one output. The use flexibility of the radio map model can be improved. Besides, different radio map sub-models in the N radio map sub-models contained in the radio map model are used for outputting different radio information, and at least two radio map sub-models in the N radio map sub-models have an incidence relation. By means of the mode, the radio map model can provide various different outputs, and meanwhile the complexity of the radio map model can be reduced through the relevance between the different radio map sub-models.

The invention discloses a multi-agent active radio map estimation method. The method comprises the following steps: establishing an environment map, a radio map and a multi-agent sampling trajectory planning model; the map estimation module periodically trains a Gaussian process model based on wireless map data historically sampled by an intelligent agent, outputs a mean map, a variance map and a variance threshold value, and further calculates an uncertain map; the regional exploration module divides a task environment into a plurality of Voronoi units, allocates an intelligent agent exploration window, calculates the mass center of an unknown connected region based on the exploration window, and evaluates the mass center with the maximum utility as a waypoint; and the neighborhood exploration module calculates a spatial sampling correlation matrix related to neighborhood sampling density and executes multiple rounds of exploration for ensuring neighborhood uniformity. According to the method, the estimation precision under the fixed sampling time concerned by existing work can be improved, the overall uncertainty of the area can be rapidly reduced, namely the confidence degree of map estimation is improved, and therefore efficient and complete exploration is achieved.

The disclosure relates to a 5G or 6G communication system for supporting higher data rates. The disclosure provides a method performed by a base station in a wireless communication system. The method includes receiving, from a plurality of terminals, information on a data set including information on a location of a terminal and information on a channel quality corresponding to the location; determining a target terminal for a training among the plurality of terminals based on the information on the data set, wherein the training is associated with a prediction of a radio map; transmitting, to the target terminal, first information including a parameter related to a network model which is used for the prediction; receiving, from the target terminal, second information including an updated parameter corresponding to a result of the training; and updating the network model based on the second information.

The embodiment of the invention provides a flight agent sampling trajectory planning method and device based on hierarchical reinforcement learning, and relates to the field of flight agent cooperative control, and the method comprises the steps: rasterizing a task area of flight agent sampling, defining a sampling position matrix, and carrying out the dynamic modeling of a flight agent; designing a flight agent sampling principle based on the sampling position matrix; building a layered reinforcement learning model for flight agent cluster sampling trajectory planning based on a flight agent sampling principle and flight agent dynamics modeling; training the hierarchical reinforcement learning model; and planning a sampling trajectory for a flight agent based on the hierarchical enhancement model. The method has generalization and expandability, the collaborative trajectory is planned for the unmanned aerial vehicle by using the hierarchical reinforcement learning framework, and the precision of a radio map construction algorithm can be effectively improved under the condition of the same sampling rate.

This invention relates to the interdisciplinary fields of wireless communication network optimization, electromagnetic environment perception, and artificial intelligence computer vision. Specifically, it is a method and electronic device for generating radio maps of complex environments based on a physical constraint latent diffusion model, comprising the following steps: S1, decoupling and constructing a shadow fading gradient map; S2, predicting the shadow fading gradient distribution; S3, compressing the map to a low-dimensional latent space; S4, training the physical constraint latent diffusion model; and S5, inferring and generating the radio map. This invention constructs a hybrid modeling framework of physical prior guidance and latent space generation, introducing a shadow fading gradient map as a strong physical constraint, effectively eliminating the wall-penetrating effect and false signal artifacts. By using a VAE to map high-dimensional data to a low-dimensional latent space for diffusion denoising, it significantly reduces computational resource consumption and GPU memory usage. It achieves the effect of significantly improving the quality of radio map generation and accurately identifying the signal propagation characteristics of complex environments under sparse sampling.

The present invention discloses a distributed radio map sensing method for privacy protection under a wireless network, comprising the following steps: S1. Each user is assigned an independent local sub-model, and the user uses their respective local sub-models to extract local data features, and finally generates a deep local feature and uploads it to the server; S2. A decision sub-model is configured for the server, and the server receives the local features from all users and uses them as the input of the decision sub-model. The decision sub-model visualizes the transmitter information and forms a two-channel picture with the static geographical environment image; S3. The two-channel picture is input into a pre-trained U-Net model to generate a radio map, and finally a radio map estimation result is obtained. The present invention realizes high-precision real-time radio map estimation on the premise of protecting user privacy, and has better application universality; and assigns importance to the features from different users to improve the estimation accuracy and reduce the model complexity.

The invention discloses a 3D indoor radio map estimation method (R2Net) based on 2D deep learning, a 3D indoor radio map is estimated according to a building layout map, a furniture layout map and a transmitter position, and the influence of the height of an indoor transmitter, the height of a receiver and the height of an obstacle on the radio map is considered. Since the input and output of the 2D deep learning algorithm are 2D pictures, the invention proposes that the heights of buildings, furniture and transmitters are represented by pixel values, and 3D radio maps with different heights are generated by using different output channels. And 3D radio map estimation is converted into a 2D deep learning task, so that the calculation overhead and the memory demand are reduced. The 2D deep learning method R2Net provided by the invention has strong generalization ability, enhances feature extraction according to indoor path loss features, and improves estimation precision and efficiency.

The invention discloses a goods taking and delivering track design method for a multi-network-connected unmanned aerial vehicle logistics system. The method comprises the following steps: acquiring a radio map, unmanned aerial vehicle speeds, the number of unmanned aerial vehicles and access position nodes of the unmanned aerial vehicles; and calculating an optimal path matrix and an effective path matrix through the radio map, the unmanned aerial vehicle speed, the unmanned aerial vehicle number and the access position nodes of the unmanned aerial vehicles, and calculating a multi-unmanned aerial vehicle access sequence track by adopting the unmanned aerial vehicle number, the optimal path matrix and the effective path matrix. According to the method, factors of distance, time and power consumption are comprehensively considered, and an optimal path between multiple unmanned aerial vehicle access position nodes is calculated; and then a multi-unmanned aerial vehicle access sequence trajectory is calculated by adopting an unmanned aerial vehicle user group pairing scheme, so that the multi-unmanned aerial vehicle flight trajectory design is optimized, and the unmanned aerial vehicle logistics efficiency is improved under the limited unmanned aerial vehicle cruising ability.

A method for providing a positioning service according to the present invention provides a positioning service by dividing a service space into a plurality of zones and hierarchically constructing a radio map, wherein a layered radio map is constructed by indexing a communication signal information measured by a measurement terminal at each reference position in each zone divided by layer with respect to one service space, and a similarity between the communication signal information received from a user terminal and the indexing information is calculated so as to search for a zone where the user terminal is located.

The invention relates to the technical field of indoor positioning, and provides a location fingerprint positioning method based on big data mining, which comprises the following steps: extracting sampling point data and base station information from a test area; sampling point preprocessing is carried out by adopting a grid division method, and a cell identifier is obtained; constructing a Radio Map based on a preprocessing result; and based on Radio Map, a WKNN algorithm is combined with a failure strategy to obtain a positioning result. According to the neighbor cell screening method provided by the invention, the accuracy and reliability of the reference points in the positioning process are ensured, and in addition, a double Radio Map construction mode is adopted, so that the comprehensiveness and dynamic updating capability of the model are ensured, and the calculation efficiency and precision during positioning are also improved. And the WKNN algorithm is adopted in the positioning stage, so that the positioning precision can be effectively improved. Meanwhile, by setting a positioning failure strategy, the condition of positioning failure can be identified and avoided in time, and the reliability and stability of positioning are improved.

A method of estimating a radio map from environmental context comprises designating a training area and for each transmitter location in the training area, generating a channel parameter radio map that is centered on the transmitter and is based on a ray-tracing twin model and generating a two-dimensional elevation map, centered on the transmitter, corresponding to the training area. The method may further comprise training a U-Net-based or any other neural network-based model using the channel parameter radio maps and the two-dimensional building elevation maps corresponding to the transmitters in the area, to produce a trained model, and designating an area to be radio-mapped. The method may further comprise providing the trained with a two-dimensional input elevation map derived from a three-dimensional model of the area to be radio mapped, and producing, by the model, an estimate of the radio map based on the two-dimensional elevation map.

This application discloses a method and system for constructing and locating radio maps in a wireless communication network. The method specifically includes the following steps: collecting radio spectrum data; selecting edge users to participate in training in response to the completion of radio spectrum data collection; performing federated learning training to obtain a trained neural network model in response to the selected edge users; outputting and saving the neural network model; and locating the radio map based on the saved neural network model. This application, by protecting user privacy, uses a neural network to accurately generalize and generate a high-precision radio map that meets the requirements, making it more suitable for intelligent scenarios.

The invention provides a geographic environment and electromagnetic environment dual-reconstruction method and system based on sparse sampling. The method comprises the steps of obtaining sparse channel measurement data, a transmitter position and a line-of-sight / non-line-of-sight indication diagram; building a building map through a preset first convolutional neural network based on the sparse channel measurement data, the transmitter position and the line-of-sight / non-line-of-sight indication graph; and constructing a radio map through a preset second convolutional neural network based on the building map. According to the method, the problem that the radio map is difficult to construct accurately under the condition that a fine environment map is lacked at present is solved.

A low-altitude 3D radio map reconstruction method based on scene guidance and sparse measurement is proposed. This method constructs and trains a reconstruction network offline, comprising a sparse data converter module, a radio map data feature extraction module, a multi-scale convolutional pyramid module, and a layer-by-layer radio map reconstruction module. In the online phase, the trained reconstruction network performs real-time map reconstruction. This invention utilizes a deep neural network architecture to fuse environmental scene understanding and sparse measurement data, achieving high-precision, physically consistent 3D reference signal received power (RSRP) field reconstruction, significantly reducing measurement overhead and improving reconstruction quality. By fusing environmental building distribution with sparse RSRP measurements, capturing environmental patterns using feature pyramids, and maintaining vertical dependence through a layered reconstruction strategy, high-precision 3D radio map reconstruction is achieved.

The embodiment of the invention provides a radio map prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of wireless communication. The method comprises the following steps: acquiring a spatial form map of a target area, and selecting a plurality of target observation positions in the target area, the plurality of target observation positions being sparsely distributed; inputting the spatial form map, the observation position coordinate determined by each target observation position and the signal intensity value into a trained prediction model to carry out local feature coding processing to obtain local spatial features, and carrying out global feature coding processing on the spatial form map under a coarse granularity level to obtain global spatial features; and fusing the local spatial features and the global spatial features under different granularity levels to obtain multi-granularity coding features, and performing prediction based on the multi-granularity coding features to obtain a target radio map. According to the method and the device, the accuracy of predicting the radio map can be improved under the condition that the acquired observation information is sparse observation information.