33 results about "Shadow fading" patented technology

The invention provides an unreliable communication-oriented air-ground collaborative online federated learning method. The method comprises the following steps: an unmanned aerial vehicle collects real-time image data according to a track preset in advance and distributes and screens samples according to dynamic data to update a local model; a model parameter error occurs due to the influence of unstable channels such as path loss and shadow fading in a local model uploading process, and the central server screens unmanned aerial vehicles to participate in global aggregation according to the model parameter error; through joint optimization of client scheduling and sample screening, the technical purpose of minimizing training loss under the condition that time delay conditions, energy consumption constraints and decision variable basic limitations are met is achieved. The method provided by the invention has the characteristics of high efficiency and intellectualization, and the precision of unmanned aerial vehicle cooperative target detection is greatly improved.

The invention provides a geometric random channel modeling method for satellite-ocean communication, and the method comprises the steps: S1, building a satellite-ocean communication simulation scene, and setting scene layout parameters; s2, initializing a satellite transmitting end orbit parameter, a receiving end position and a sea wave fluctuation parameter; s3, calculating large-scale parameters with consistent space; s4, calculating path loss, shadow fading, atmospheric absorption loss, ionospheric scintillation loss and rainfall loss according to the set receiving and transmitting end position information; s5, calculating a small-scale parameter according to angle extension in the generated large-scale parameter, initializing the position of a scattering cluster on the sea surface and the surface of the ship body, generating time delay and power of the cluster, and generating a channel coefficient by combining the loss calculated in the step S4; s6, updating the large-scale parameters and the small-scale parameters according to the motion of the transmitting and receiving end and the birth and extinguishment process of the cluster, and generating a new channel coefficient; and S7, calculating channel statistical characteristics according to the obtained channel coefficients.

The invention relates to the field of communication, in particular to a method for delivering cache content of a mobile edge computing network for energy consumption optimization. For a cache-based MEC cellular network, the method reasonably utilizes user movement information, and calculates corresponding path fading, channel gain and shadow fading by utilizing future position information of a user. Determining the minimum transmission power by using the calculated channel gain and path fading, then setting a time slot, carrying out content delivery decision on data packets requested by different users, and allocating wireless communication resources; and with minimization of the total emission energy consumption of the base station as a target, modeling a cache content delivery problem into a mixed integer programming problem to carry out optimization solution so as to obtain an energy consumption minimization decision for the base station to deliver all data packets.

The present invention discloses a method for mapping electromagnetic spectrum in complex three-dimensional urban environments, comprising: determining the scope, frequency points, and signal recovery threshold of the area to be mapped; representing the spectrum data intensity corresponding to each cube obtained after discretizing the area to be mapped in tensor form; determining the number of samples in the area to be mapped, an optimized set of sampling position indexes, and determining the optimal sampling trajectory of an unmanned aerial vehicle; collecting corresponding spectrum data vectors, recovering the electromagnetic target signals in the area to be mapped, and obtaining a preliminary complete spectrum map; calculating the shadow fading components at all locations based on the sparse electromagnetic target signals and the sampled spectrum data, and correcting the spectrum map; correcting the reconstructed spectrum map based on the shadow fading components to obtain a three-dimensional electromagnetic spectrum map of the area to be mapped. The present invention comprehensively considers the characteristics of the scene and the optimization of sampling positions, and only needs to collect spectrum data from a small amount of key locations to achieve high-precision spectrum mapping.

A device may receive real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area, and may receive network topology data associated with the geographical area. The device may utilize, based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data, and may generate, based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will be obstructed. The device may utilize the stochastic data to identify a realistic discoverable spatiotemporal signature, and may train or evaluate a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature.

The present application relates to the field of wireless communication and positioning technology, in particular to a three-dimensional space positioning method based on wireless mesh network and a server; the method comprises determining path loss factor and reference signal strength of free space propagation model by using known distance and signal strength between N reference nodes; selecting M reference nodes with strongest signal received by target node, obtaining coordinates of the M reference nodes and calculating distance from the target node based on the determined free space propagation model; calculating initial coordinate value of the target node by using genetic algorithm based on coordinates of the M reference nodes and measured distance from the target node; calculating final coordinate value of the target node by using improved Gauss-Newton algorithm based on the initial coordinate value of the target node. The present application can effectively reduce the influence of multipath interference and shadow fading on RSSI positioning, and significantly improve the accuracy and stability of three-dimensional positioning.

The geometric statistics-based modeling method for the communication and perception integrated single-station background channel provided by the invention comprises the following steps: acquiring the number of multiple reference points corresponding to a base station in a single-station perception mode and the position parameter of each reference point, the reference points representing virtual anchor points used for receiving signals; according to configuration parameters and position parameters of channels in the pre-configuration scene, path loss, shadow fading and multipath cluster parameters of sub-channels corresponding to the reference points are obtained, and arrival angle parameters in the multipath cluster parameters are determined according to corresponding departure angle parameters; calculating according to the multipath cluster parameter to obtain a corresponding sub-channel impulse response; and linearly superposing the impulse response of each sub-channel according to the path loss and shadow fading to obtain a single-station background channel impulse response corresponding to the base station. The communication perception integrated single station background channel is effectively described, model standard simulation of an existing communication channel is compatible to the maximum extent, and the advantages of low complexity of statistical modeling and high precision of deterministic modeling are integrated.

The invention discloses a wireless communication spectrum space digital twinning method, which comprises the following steps of: generating basic static channel characteristics under a given environment model and a base station deployment condition; wherein the static channel characteristics comprise a path loss distribution characteristic, a shadow fading characteristic and a multipath propagation characteristic; associating the static channel feature with target vehicle information, and generating a single-frame channel feature when a vehicle exists; and capturing dynamic evolution information of channel characteristics in a vehicle movement process, and expanding the single-frame channel characteristics into sequential dynamic channel characteristics to obtain a digital twinborn model of a dynamic scene spectrum space. According to the method, the influence of a dynamic blocking body can be further considered on the basis of static channel generation, channel feature generation in a dynamic environment is realized, and the reality sense and application applicability of digital twinning are remarkably improved.

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.

Disclosed are a transmission power determination method, a related device, a storage medium and a program product, applied to a network device, the method comprising: sending first information to a terminal, the first information being used for the terminal to determine a transmission power of an uplink physical random access channel, the first information at least comprises intercept and slope in a path loss model fitted by all accessed terminals under one beam; identifying a sample set; the sample set identifies a corresponding shadow fading parameter.

The invention provides a 6G-oriented unified channel modeling method and device, and relates to the technical field of wireless communication, and the method comprises the steps: constructing a wireless communication scene, configuring a basic parameter and a first parameter of a 6G channel, and obtaining a large-scale parameter and path loss of channel propagation; the first parameters comprise at least one of the stationary region size, the RIS parameter, the target parameter and the sharing parameter of the E-MIMO antenna array, and the sparse K factor in the high frequency band cluster; generating a cluster diameter parameter of the 6G channel or a sub-channel of the 6G channel according to the basic parameter, the large-scale parameter and the channel type; according to the first parameter, generating a second parameter depicting new characteristics of the 6G channel; and according to the second parameter and the cluster diameter parameter, generating a channel coefficient of the 6G channel, the channel coefficient being used for coupling with path loss and shadow fading to obtain a channel impact response of the 6G channel. Therefore, the constructed channel model can describe new 6G characteristics.

The invention discloses a signal scheduling method based on a 5G base station, and relates to the technical field of 5G communication, and the method comprises the following steps: carrying out the data collection in advance: collecting user equipment data, network state data and environment parameters in real time through a built-in sensor and a network monitoring unit of the base station; the collected data is preprocessed, specifically, the collected data is subjected to cleaning, duplicate removal and normalization processing, and non-standardized parameters are converted into a [0, 1] interval through a mapping formula; according to the method, the intelligent scheduling model driven by the multi-dimensional data is constructed, dynamic optimal configuration of 5G base station signal resources is achieved, environmental parameters such as path loss factors and shadow fading standard deviation and user service characteristics are fused, cooperative improvement of the coverage quality, the resource utilization rate, interference suppression and user experience is achieved through a multi-objective optimization function, and the user experience is improved. And the adaptability limitation of a traditional static scheduling mode in a complex scene is broken through.

Technologies directed to rate adaption based on both the average signal-to-noise ratio (SNR) and standard deviation are described. One method includes determining an average SNR value and a standard deviation value for a first set of RSSI samples. The method determines whether the average SNR value satisfies a first threshold criterion corresponding to a minimum SNR required with a first modulation scheme and a second threshold criterion corresponding to a standard deviation threshold. The method causes a transmitter to use the first modulation scheme for a subsequent packet, responsive to the average SNR value satisfying the first threshold criterion or the standard deviation value satisfying the second threshold criterion. The method causes the transmitter to use the second modulation scheme for the subsequent packet, responsive to the average SNR value not satisfying the first threshold criterion and the standard deviation value not satisfying the second threshold criterion.

This invention relates to the field of electromagnetic spectrum management technology and proposes a spectrum map construction method based on weighted dynamic allocation tensor ring completion. The method includes: modeling multi-frequency spectrum data as a target spectrum tensor; performing tensor ring decomposition on the target spectrum tensor data to extract TR factors, and generating a spectrum map generation model based on the TR factors; establishing different completion algorithms for path loss and shadow fading scenarios to complete the missing spectrum data in the spectrum map generation model; and weighted fusion of the completion results to construct the final spectrum map. This invention employs different algorithms to complete the missing spectrum data in the spectrum map generation model for different scenarios, thereby improving the robustness of the algorithm under different scenario conditions. Furthermore, by weighted fusion of the completion results from the two algorithms to construct the final spectrum map, it can dynamically adapt to two key factors affecting the construction accuracy, thus accurately constructing the spectrum map.

The present invention discloses a method for simulating an air-ground channel integrating the characteristics of unmanned aerial vehicles, including: establishing an air-ground channel model for unmanned aerial vehicles; inputting the types of air-ground communication scenarios for unmanned aerial vehicles, carrier frequencies, position vectors and velocity vectors of unmanned aerial vehicles / receiving ends / scatterers at the initial moment, flight attitudes, antenna half-power beam widths, and fuselage structure data sets; calculating the Rice factor; calculating the power coefficients of the line-of-sight path and the n th non-line-of-sight path; obtaining the path loss of the ground segment through neural network training; calculating the shadow fading between the receiving end and the transmitting end, and calculating the body shadow coefficients of the line-of-sight path and the non-line-of-sight path by using the fuselage structure, flight attitude and half-power beam width of the antenna; inputting the Rice factor, time delay, phase, power coefficient, path loss and shadow fading into the air-ground channel model for unmanned aerial vehicles to output channel fading. The present invention can achieve efficient and accurate simulation and reproduction of the air-ground channel, and support the evaluation and optimization of unmanned aerial vehicle communication systems.

The invention provides an unknown environment-oriented electromagnetic spectrum map mapping method, medium and equipment, which introduces a dynamic learning mechanism, obtains and optimizes cognition of an unknown spectrum environment through real-time analysis of collected spectrum data, and designs an optimal sampling flight path planning strategy in combination with information gain amounts of different spatial positions, so as to obtain an electromagnetic spectrum map. Under the condition of limited sampling time and sampling quantity, efficient spectrum mapping of a complex unknown environment is realized. According to the method, the characteristics that three-dimensional environment sampling is difficult and available spectrum data are sparse are considered, based on a sparse Bayesian learning theory and a dictionary learning method, limited spectrum data are utilized, estimation of a radiation source signal and updating of a channel dictionary are iteratively optimized, and accurate modeling of shadow fading is further combined, so that the optimal spectrum data is obtained. And the recovery precision of the three-dimensional spectrum data is effectively improved.

The invention relates to the technical field of wireless communication and positioning, in particular to a three-dimensional space positioning method based on a wireless mesh network and a server. The method comprises the following steps: determining a path loss factor and reference signal strength of a free space propagation model by utilizing known distances and signal strength among N reference nodes; m reference nodes with the strongest target node receiving signals are selected, coordinates of the reference nodes are obtained, and the distance between the reference nodes and the target node is calculated based on the determined free space propagation model; calculating an initial coordinate value of the target node by using a genetic algorithm based on the coordinates of the M reference nodes and the measurement distance between the M reference nodes and the target node; and calculating a final coordinate value of the target node by using an improved Gaussian-Newton algorithm based on the initial coordinate value of the target node. According to the method, the influence of multipath interference and shadow fading on RSSI positioning can be effectively reduced, and the precision and stability of three-dimensional positioning are remarkably improved.

The invention belongs to the technical field of underwater acoustic transmission, and particularly discloses an MIMO communication cooperative detection method based on hybrid electromagnetic induction and acoustics, and the method comprises the steps: carrying out the signal exchange between a master node and a slave node in a transmitting cluster through a magnetic induction technology, so as to achieve the time-frequency synchronization; generating an MIMO signal adaptive to the underwater mixed cooperation scene based on the synchronized point cooperation relationship between the nodes in the transmitting cluster; transmitting the MIMO signal from the transmitting cluster to the receiving cluster in a k-mu shadow fading channel environment through an acoustic transmission medium, wherein the k-mu shadow fading channel environment is an underwater channel model used for representing a composite line-of-sight effect and a cluster scattering effect; and carrying out signal exchange among nodes in the receiving cluster through a magnetic induction technology to realize time-frequency synchronization, and carrying out signal detection and combination on signals received by the receiving cluster to recover original signals. According to the invention, the reliability and throughput of underwater complex environment communication can be effectively improved.

Technologies directed to rate adaption based on both the average signal-to-noise ratio (SNR) and standard deviation are described. One method includes determining an average SNR value and a standard deviation value for a first set of RSSI samples. The method determines whether the average SNR value satisfies a first threshold criterion corresponding to a minimum SNR required with a first modulation scheme and a second threshold criterion corresponding to a standard deviation threshold. The method causes a transmitter to use the first modulation scheme for a subsequent packet, responsive to the average SNR value satisfying the first threshold criterion or the standard deviation value satisfying the second threshold criterion. The method causes the transmitter to use the second modulation scheme for the subsequent packet, responsive to the average SNR value not satisfying the first threshold criterion and the standard deviation value not satisfying the second threshold criterion.

The present application belongs to the technical field of electronic countermeasure, and particularly relates to a single-radiation-source joint detection and tracking method based on received signal strength. The present application establishes a measurement model containing normal shadow fading parameters based on received signal strength, introduces particle filtering technology to estimate the shadow fading parameters, and designs a Bernoulli filter by taking signal strength as a measurement index, thereby providing a solution for single-target radiation source joint detection and tracking in complex scenes. The present application can realize joint detection and tracking of single-target radiation sources, and uses soft decision, i.e. calculation of existence probability, to replace hard decision to extract states and detect whether a target exists, thereby avoiding intermediate errors generated by the traditional two-step tracking method. The method has strong robustness and good effect.

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 relates to an RSS (Received Signal Strength) positioning method based on directional gain correction, and aims to solve the problem that a traditional positioning method based on Received Signal Strength performs target positioning under the condition of neglecting antenna directional gain and easily generates system errors in a three-dimensional space. The invention designs an RSS (Received Signal Strength) positioning method based on directional gain correction, which comprises the following steps of: firstly, introducing an antenna directional gain item into a classic logarithmic path loss model so as to more accurately describe the relationship between signal intensity and a space direction; an optimization problem is solved by using an iterative algorithm, and in each iteration, local fixed parameter optimization is performed on the directional gain, and an original non-convex positioning problem is converted into a solvable convex SDP problem. Simulation experiment results show that the positioning precision of the method can approach the Cramer-Rao lower bound, and high robustness is still kept under the condition of serious shadow fading.

The present invention proposes a method and system for measuring the speed of a moving target based on Wi-Fi signals, which relates to the field of wireless sensing technology. The problem addressed is that existing technologies cannot overcome the problems of noise interference and signal attenuation in complex environments, are not suitable for multi-scenario speed measurement, and have low speed estimation accuracy. The method obtains the Wi-Fi dynamic signal caused by the moving target; extracts the automatic gain control value and Doppler spectrum from it; calculates the shadow fading parameter based on the automatic gain control value; divides the Doppler spectrum and calculates the multi-scale speed; detects the shadow fading area based on the shadow fading parameter; determines whether it is within the shadow fading area, and if so, reconstructs the attenuation speed to obtain the reconstructed scale speed, and fuses the reconstructed scale data with the unreconstructed scale data to obtain the speed of the moving target. The present invention improves the accuracy and robustness of speed estimation, can effectively deal with the problems of noise interference and signal attenuation in complex environments, and is suitable for multi-scenario speed measurement.

The invention discloses a 6G full-band full-scene universal large-scale fading channel modeling method, which relates to the technical field of wireless communication, and comprises the following steps: step 1, representing the path loss of large-scale fading as the sum of basic loss, atmospheric fading loss, flicker loss, building penetration loss and rainfall fading, the shadow fading of large-scale fading is expressed as Gaussian distribution of a zero mean value; 2, calculating basic loss by adopting a double-slope model; 3, representing the atmospheric fading loss as the sum of the oxygen fading loss and the water vapor attenuation loss; 4, representing the scintillation loss as the sum of the ionosphere scintillation loss and the troposphere scintillation loss; 5, calculating the penetration loss of the building by adopting a double logarithmic normal distribution combination model; and step 6, calculating rainfall fading by adopting a Crane model. The large-scale fading modeling precision in the terahertz frequency band, the indoor scene and the unmanned aerial vehicle scene is higher than that of an existing standard model.

The invention is applicable to the field of communication, and provides a network slice resource allocation method with synergy of communication and sensing and computer equipment. The method comprises the following steps: constructing a channel model based on a 3GPP standard based on set simulation parameters and service type parameters, calculating basic path loss, shadow fading and a signal-to-noise ratio according to the simulation parameters and the service type parameters, and constructing a TSN synchronization error model and a model for calculating a sensing target distance; calculating the service weight of the equipment according to the set service type parameter, and correcting the channel quality; carrying out priority enhancement on services, calculating normalized weights, carrying out K-means clustering on weighted channel gains, calculating a proportion of perception services, and designing a power distribution rule; in a resource allocation stage, an application scene slice bandwidth and a sensing service slice bandwidth are calculated and dynamically allocated, and resource allocation, precision, time delay and a resource reuse rate are calculated. The method can significantly improve the utilization efficiency of network resources, reduce the time delay, and improve the sensing precision.

In order to solve the spectrum sensing problem of cognitive radio network under the condition of irregular node distribution and local observation data missing, the present application provides a spectrum sensing method and device fusing graph signal reconstruction and deep graph convolution network (GCN). The method firstly models the distributed sensor network as an irregular weighted graph, and uses graph signal processing technology (such as kernel ridge regression, Laplacian interpolation or GSP low-pass reconstruction) to pre-reconstruct the signal of the unsampled nodes in the sensing blind area, and restores the signal features of the whole network. Subsequently, a multi-layer deep graph convolution network is designed and constructed, and the reconstructed whole network received signal strength (RSS) is used as the input feature, the spatial topological correlation is captured through the residual graph convolution layer, and the dynamic weighted balanced focal loss function is used for offline training. Thus, high-reliable spectrum sensing under low sampling overhead and strong shadow fading environment is realized, and the robustness of the system to irregular network topology and local data missing is improved.

The present application relates to the field of wireless communications, and discloses a method, device and equipment for reducing signal shadow fading by using dual antennas. The method includes: setting a polling cycle through a TDMA networking system, wherein at least two special time slots are preset; the ground end sends the same data packet in the special time slot; the sky end receives the data packet through the first antenna and the second antenna respectively, and calculates the signal-to-noise ratio of each antenna; compares the signal-to-noise ratios of the two antennas, and selects the antenna with a larger signal-to-noise ratio as the working antenna in the subsequent time slot; at the end of each polling cycle, repeats the antenna selection process. The device includes two antennas, a T / R component, a single-pole double-throw switch and a baseband circuit, and the baseband circuit is responsible for analyzing the signal-to-noise ratio, controlling antenna switching and executing dynamic antenna selection logic. The present invention can effectively reduce the impact of signal shadow fading, improve the stability of the communication system, adapt to the dynamic channel environment, and optimize the cost, power consumption and reliability of communication equipment.

The invention discloses a multi-scale time-varying multipath amplitude calculation method of a mobile underwater acoustic communication channel, a program, equipment and a storage medium, and belongs to the field of underwater communication. A channel path amplitude obtained through channel estimation is decomposed into three scales, namely large-scale propagation loss based on a sound propagation physical rule, medium-scale shadow fading based on a hidden Markov model and small-scale scattering fading based on measurement statistics, and finally, time-varying sequences with multi-path amplitudes are subjected to cascade superposition after direct sound judgment. The problems that the propagation attenuation trend, continuous slow shadow fading and the like caused by rapid movement of a transceiving platform cannot be captured by an existing underwater sound multi-path channel time-varying amplitude calculation method are solved. The underwater acoustic channel multipath amplitude calculation method has the advantages of high precision and strong dynamic adaptability.

This invention provides an air-ground collaborative online federated learning method for unreliable communications. The method involves: drones collect real-time image data based on pre-set flight paths and select samples based on dynamic data distribution to update local models. During local model upload, model parameter errors may occur due to unstable channels such as path loss and shadow fading. A central server then selects drones for global aggregation based on these errors. By jointly optimizing client scheduling and sample selection, the method achieves the technical goal of minimizing training loss while satisfying latency requirements, energy consumption constraints, and basic decision variable restrictions. This method is highly efficient and intelligent, significantly improving the accuracy of drone collaborative target detection.

The invention relates to a broadband high-frequency ionospheric channel modeling method based on large and small scale dynamic combination. The method comprises the following steps: fitting a path loss model by utilizing actually measured ionospheric channel impulse response of a current link and a least square method; shadow fading is fitted into normal distribution, and a time-frequency related large-scale fading model is constructed in combination with a path loss model; extracting channel parameters of the actual measurement channel impulse response in a time delay domain and a Doppler domain, and constructing a statistical distribution function; generating channel parameters by using a statistical distribution function, constructing a single-mode channel impulse response, and adjusting a multi-mode channel impulse response to obtain a small-scale fading model; and constructing a broadband high-frequency ionosphere channel model by combining a large-scale fading model and a small-scale fading model. According to the method, ionosphere channel large-scale fading time-frequency dynamic analysis is considered, small-scale fading parameter statistical distribution modeling is supported, and the channel modeling precision is improved.