37 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 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 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.

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.

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.

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.

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.

A computing device, method and computer program product are provided to generate or update a radio map based on a plurality of fingerprint data that each respectively includes an obfuscated location reference. The radio map may be provided to another computing device for use in conjunction with positioning services. A computing device is provided that is configured to determine a position based upon a radio map. A computing device, method and computer program product are also provided to determine a position based upon a radio map for which the obfuscated location references are deobfuscated based on the one or more parameters, such as one or more translation and / or rotation parameters.

A communication method and related apparatus, in which, after a first communication device receives first information used for configuring a first signal, the first communication device can determine that there is a correspondence between the first signal and a second signal based on the first information. Thereafter, after the first communication device receives the first signal, the first communication device can determine channel information corresponding to the first signal based on multipath information corresponding to the second signal with which the first signal has the correspondence. In some implementations, the multipath information corresponding to the second signal is determined through a first radio frequency map (RF map). Compared with a way of signal measurement to obtain multipath information, a way of providing multipath information through a radio map can reduce transmission overhead and processing latency, and further improve communication performance.

This invention discloses a method for constructing a 3D radio map based on a conditional generative adversarial network (GAN), comprising the following steps: Step S1: Acquire multimodal input data of the target area; Step S2: Based on generalized anisotropic spatial attenuation field coding technology, map discrete transmitter points to continuous spatial energy distribution features, generate a global radiation prior feature map, and construct a conditional feature tensor; Step S3: Construct a 3D U-Net generator with a self-attention mechanism and establish a GAN loss function based on composite constraints; Step S4: Iteratively train the generator and discriminator using the constructed training dataset; Step S5: Online inference and real-time construction of a high-precision 3D radio map. This invention can integrate complex 3D geographic environment information and real-time multimodal data to achieve 3D radio map construction with high accuracy, deterministic mapping, and low latency.

The application provides a communication method, device, system, storage medium and program product. The method comprises: a terminal device sends first information, the first information is used to indicate a target format of a radio map. Then, a network device sends second information, the second information comprises first data, and the format of the first data corresponds to the target format. In this example, the terminal device sends the first information to enable the network device to know the format of the radio map selected by the terminal device, and then the network device can send data corresponding to the selected format, thereby aligning the formats between the network device and the terminal device and improving interoperability. On the other hand, the need for data conversion is reduced or eliminated, and the cost of data conversion is reduced. Moreover, more advanced applications are supported, and better support is provided for map-based wireless network planning, optimization, simulation and visualization. Furthermore, the development of the industrial ecosystem is promoted, such as the standardization and industrialization of radio map-related technologies.

This invention proposes a radio map construction and non-cooperative radiation source localization method based on agent interaction. The method first utilizes a drone swarm to dynamically sample sparse signals, then uses Gaussian process regression to fuse the signal propagation physical priors to generate a spatial attention weight map. These physical priors are injected as biases into a multi-head attention mechanism, enabling multiple agents to form a consistent joint representation of the radiation source target in a unified semantic space. Next, key semantic features are dynamically compressed and selected through task-driven semantic filtering and interaction. Semantic recovery is performed using masked multi-head self-attention and transposed convolutional networks to suppress noise and output preliminary results for both tasks. Finally, an embodied intelligent feedback closed-loop system is constructed. This system can perceive the environment and task status in real time, dynamically adjust the weights and interaction strategies of both tasks, and store the optimal strategy in long-term memory. This invention improves the accuracy of radio map construction, radiation source localization accuracy, and multi-agent collaborative efficiency in sparse sampling, multi-source aliasing, and dynamic complex electromagnetic environments.

This application provides a method and system for joint optimization of trajectory and task scheduling for logistics drones. The method includes: acquiring environmental and building information of the drone and the logistics task; wherein the environmental and building information includes multiple base stations and measured data of buildings; constructing a radio map based on the environmental and building information; constructing a drone reachable space that satisfies communication constraints based on the radio map; calculating the optimal flight trajectory between each pair of nodes in the logistics task based on the drone reachable space, and determining a reliable flight cost matrix; optimizing the user access order of the drone based on the reliable flight cost matrix, and determining the optimal user access order; and mapping the optimal flight trajectory and the optimal user access order to obtain the final optimization result. This solution effectively improves the overall operational efficiency and value acquisition capability of drones in time-sensitive logistics tasks while ensuring communication reliability and energy feasibility.

The invention provides a radiographic map feature extraction method, system and device and a medium. The method comprises the steps of obtaining a hyperspectral radiographic map data set; performing multi-scale analysis and feature extraction on the hyperspectral radiograph data set to obtain a radiograph feature set; decomposing the hyperspectral radiograph data set to obtain a sub-band coefficient; performing noise reduction processing on the hyperspectral radiograph data set based on the sub-band coefficient to obtain a noise reduction data set; carrying out dimension reduction processing on the noise reduction data set to obtain a principal component feature vector; generating a weight data set according to preset radioactive substance characteristics; performing optimization processing on the weight data set to obtain an optimized weight data set; and performing feature extraction on the principal component feature vectors based on the optimized weight data set to obtain a radiographic map feature set, and performing multi-scale feature extraction on the radiographic map to obtain feature information of the radiographic map from different frequencies and spatial resolutions.

The embodiment of the invention provides a low-altitude communication node deployment method and system based on deep learning and reinforcement learning. The method comprises the following steps: acquiring a city three-dimensional environment map; wherein the urban three-dimensional environment map comprises the terrain, the building height and the building shielding information of the city; through a predetermined radio map prediction model, carrying out signal to interference plus noise ratio distribution prediction on the urban three-dimensional environment map under different node deployments to obtain a signal to interference plus noise ratio prediction distribution map; performing iterative optimization on the signal to interference plus noise ratio prediction distribution diagram through a predetermined reinforcement learning optimization model, and determining an optimal deployment strategy; determining the position of a target base station based on the optimal deployment strategy; wherein the target base station position is the position of the target node in the plurality of different nodes determined based on the optimal deployment strategy. According to the scheme, high-precision propagation prediction and adaptive optimization deployment are realized in a complex low-altitude environment, and the deployment efficiency and the communication performance can be improved.

The embodiment of the invention relates to the field of unmanned aerial vehicles, and provides a radio map construction and distribution-oriented unmanned aerial vehicle flight path planning method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining initial model parameters required by a representation process of a target task scene; the transmission construction and the flight energy consumption of the unmanned aerial vehicle are calculated; based on the initial model parameters and unmanned aerial vehicle transmission construction and flight energy consumption, determining a target function of maximizing the total mass of a high-resolution compressed map constructed by a flight path, and determining constraint conditions associated with the target function to construct an unmanned aerial vehicle flight path planning optimization model; and solving the unmanned aerial vehicle flight path planning optimization model by using a particle swarm algorithm combined with an adaptive transfer function to obtain an unmanned aerial vehicle flight path optimal scheme. By adopting the method, the quality of the radio map obtained by the flight path planning mode can be improved.

This invention presents a multimodal information fusion-based urban air-to-ground radio map redrawing method. By integrating prior knowledge from partially incomplete maps with a small amount of RSS measurements, a more comprehensive and robust information representation can be obtained. Through modal fusion of multi-class occlusion wireless channel models and multi-class virtual obstacle map models, wireless signal features and geographic information features are used together to redraw the radio map, better capturing their correlation and complementarity. The method of this invention is experimentally validated using an open-source RSS measurement dataset and compared with single-modal methods to evaluate its beneficial effects. The evaluation results demonstrate the advantages of this invention in wireless signal reception strength estimation and virtual obstacle map redrawing tasks, and show its effectiveness in reducing the amount of data required for the task and improving redrawing accuracy.

This invention discloses a radio map prediction method and apparatus that integrates physical equations and deep learning, belonging to the field of communication technology. The method includes: inputting an environmental map of the environment to be tested and base station location information into a trained singularity map prediction network to obtain a singularity map of the environment to be tested; wherein the singularity map prediction network is trained based on sample singularity maps, and the sample singularity maps are calculated according to the Helmholtz equation; inputting the singularity map, environmental map, and base station location information into the trained radio map prediction network to obtain a radio map of the environment to be tested. This invention has low computational complexity, wide applicability, and good prediction accuracy.

A method for providing optimal communication performed by a computing system includes constructing a radio map including information for calculating an interference component for each channel, for one or more access points (APs), estimating a location of a user terminal, based on the radio map, and determining at least one of an optimal AP and an optimal channel for the estimated location of the user terminal, based on the interference component for each channel.

The application discloses a terahertz environment semantic map construction method based on a physical information flow matching model, and relates to the technical field of communication.The method comprises the following steps: constructing a sparse directional beam received signal strength dataset of a real scene collection, a real omnidirectional radio map and a training set of a real physical environment; constructing a flow matching model with a built-in three-class propagation region dynamic judgment module, taking the sparse data as a condition, integrating a Helmholtz equation constraint and a regionalized power boundary constraint as double physical constraints, synchronously generating a radio map and a physical environment by adopting a double-output U-Net architecture, and training through a non-classifier guiding mechanism; in actual application, sparse directional beam data is collected to input the trained model, and an omnidirectional radio map and a physical environment of a current scene are generated by reasoning.The application solves the problems of sparse data and poor physical consistency in terahertz communication, reduces the hardware and time cost, and improves the map construction precision and generalization ability.

The invention discloses a radio map reconstruction method and device based on a double diffusion model, and relates to the technical field of communication, and the method comprises the steps: obtaining to-be-processed data; the trained first-stage network model is adopted to process to-be-processed data, spatial features of rapid change of signals are captured, and an electromagnetic singular region is obtained through prediction; processing the electromagnetic singular region and data to be processed by adopting the trained second-stage network model, and generating a radio map by taking the electromagnetic singular region as structural guidance; wherein the trained first-stage network model is obtained by taking a first sample as training data, taking a pre-constructed electromagnetic singular point contour map as a label and training an initial diffusion model, and the trained second-stage network model is obtained by taking a second sample as training data and taking a real radio map as a label. And training the initial diffusion model. According to the method, the map-based positioning and navigation capability can be remarkably enhanced.

The application discloses a non-cooperative interference source indoor positioning method based on a light ray tracing residual graph and belongs to the technical field of wireless communication positioning. The method comprises the following steps: calculating the interference source transmission power estimation value corresponding to each receiver at any candidate position in a target area by modeling the received signal strength and path loss radio map measured by the receiver; constructing a power consistency residual graph of the target area according to the consistency degree between the interference source transmission power estimation values; performing spatial connected domain analysis based on the power consistency residual graph to divide the target area into a plurality of spatial continuous areas; and taking the centroid of the maximum spatial continuous area as the positioning result of the interference source. The application can realize high-robustness and high-precision indoor interference source positioning without the known transmission power and the large-scale field data collection.

The invention discloses a low-energy-consumption channel state determination method and a related device, and relates to the field of wireless communication, grid index data of user equipment in a coverage range of a base station is obtained, and the grid index data comprises position data of a target grid where the user equipment is located and frequency data in the target grid. And based on the grid index data, determining a target equivalent multipath parameter corresponding to the grid index data from a preset radio map. And finally, converting the target equivalent multipath parameters based on the equivalent multipath model to obtain a downlink channel matrix corresponding to the target grid. The preset radio map is constructed by representing the downlink channel matrix through the equivalent multipath parameter corresponding to the grid, so that the scale of the estimated radio map can be effectively reduced, and the energy consumption required for transmitting the radio map is further reduced.

Described is a network entity (501) for use in a mobile network (500), the network entity being configured for communication with a user equipment device (350, 502) in the mobile network (500), the network entity being configured to allocate (1201) to the user equipment device a set of location-associated pilot patterns for uplink communication from the user equipment device, wherein each of the set of location-associated pilot patterns comprises a respective pilot pattern corresponding to a respective geographic location of the user equipment device. This may improve the latency issue for radio-map based channel estimation, while reducing uplink signalling overhead.

The embodiment of the invention provides an unmanned aerial vehicle path planning method and device based on radio map reconstruction, and relates to the technical field of wireless communication and machine learning. According to the embodiment of the invention, by combining deep learning and denoising technologies, the reconstruction precision of the radio map can be remarkably improved under the sparse data condition, and the problems of signal interference and data loss in a complex environment can be effectively solved. According to the embodiment of the invention, through deep denoising regularization processing, high-quality map reconstruction can be realized even if the data is incomplete, and the cost and difficulty of data collection are reduced. In addition, according to the embodiment of the invention, the alternating direction multiplier method is utilized to optimize the calculation process, the iteration speed can be increased, and the overall operation efficiency is improved. According to the embodiment of the invention, the flight path and communication strategy of the unmanned aerial vehicle are dynamically adjusted according to the reconstructed radio map, and the safety and efficiency of task execution of the unmanned aerial vehicle are improved.

The radio wave map updating device includes: a storage unit (201) that stores a radio wave map including reference position information indicating a reference position, reference blockage information indicating a reference blockage state of communication with a first base station at the reference position, and reference communication quality information with the first base station or a second base station at the reference position and the reference blockage state; a reception unit (202) that receives communication quality information between a probe information transmission device mounted on a mobile body and the first base station or the second base station; and an update unit (211) that updates the reference communication quality information stored in the storage unit using the communication quality information.

The invention relates to the technical field of wireless communication, and particularly discloses an air base station radio map generation method based on generative artificial intelligence, which comprises the following steps: S1, constructing an air base station wireless channel model; s2, defining a 9D air base station radio map modeling space; s3, adopting a conditional denoising diffusion probability model to carry out offline generation type modeling on the 9D air base station radio map modeling space; and S4, based on a modeling result, constructing an air base station radio map without large-scale online measurement, and performing channel estimation of an air base station communication system. According to the method for generating the radio map of the air base station based on the generative artificial intelligence, the influence rule of the deployment state and the motion characteristic of the air base station on wireless channel distribution can be learned under the condition of a small number of collected samples, and the high-dimensional radio map is generated, so that reliable channel prior distribution is obtained.

The application discloses a kind of generative radio map collaborative estimation methods for multi-signal source scene, comprising the following steps: S1. the received signal strength sample of multiple users on multiple frequencies is constructed into matrix form, and is decomposed using NMF matrix decomposition method, each received signal strength component decomposed corresponds to an independent signal source;S2. single signal source radio map inference model is constructed and trained;S3. for each received signal strength component obtained in step S1 decomposition, the corresponding is estimated using single signal source radio map inference model.The application decomposes the signal strength component corresponding to multiple signal sources using matrix decomposition theory, and trains the neural network using the learning algorithm based on GAN, improving the accuracy and generalization ability of the system. This method can also work effectively in the case of inaccurate geographic data or matrix decomposition results with errors.