60 results about "Cognitive radio" patented technology

The invention discloses an abnormal signal detection and signal transmission protocol identification method and device, relates to the field of cognitive radio, and is used for improving the practicability and robustness of signal identification in an open set environment. According to the method, the generative adversarial network is utilized to train the first classifier model to classify the time-frequency characteristics of the air interface signals, and the rejection threshold value determined based on the extreme value theory is utilized to perform abnormal signal discrimination. On the basis, a deep convolutional neural network fused with a multi-head self-attention mechanism is used for identifying the transmission protocol type of a normal transmission signal. According to the method, generalization ability and robustness of unknown signal identification in an open environment are effectively achieved.

The invention relates to the related field of unmanned aerial vehicle communication, and discloses an unmanned aerial vehicle relay communication link rapid establishment and optimization method in a three-network full-disconnection scene, and the method comprises the following steps: S1, carrying out the dynamic spectrum sensing and anti-interference channel distribution based on cognitive radio; s2, adaptive power-energy consumption joint optimization control is carried out; s3, quickly reconstructing the distributed intelligent route; according to the method, a new-generation anti-interference architecture is constructed based on a Conv-LSTM dynamic spectrum sensing system, the system has the capability of autonomously identifying and avoiding complex electromagnetic interference through real-time spectrum analysis driven by deep learning, an intelligent power regulation and control mechanism guided by a link stability index is adopted, and the reliability of the system is improved. Dynamic optimization of communication energy consumption is achieved, an innovative node dormancy strategy is combined with a gradient descent algorithm, the problem of energy waste in a traditional scheme is effectively solved, and the energy utilization efficiency of the system is remarkably improved.

The invention discloses an electric power private network unmanned equipment cooperation intranet and extranet data interaction processing method, and belongs to the technical field of power grid intranet and extranet cooperation data processing. The method is used for solving the technical problem of poor data interaction efficiency and quality of an intranet and an extranet during cooperation of unmanned equipment in a power private network in an existing scheme. Task instructions, historical electromagnetic environment data and real-time working condition parameters of the unmanned aerial vehicle and the unmanned vehicle are collected and processed, data analysis is performed through a pre-constructed intention-electromagnetic coupling prediction model, and a preliminary prediction result and an anti-interference resource reservation amount are output; performing unmanned aerial vehicle-unmanned vehicle cooperative resource block division based on the output prediction result, optimizing channel and bandwidth allocation, and outputting an equalization strategy; dynamically switching the working mode of cognitive radio by using an equilibrium strategy, and realizing intelligent combination of repeated data requests in combination with intention similarity clustering; and analyzing the prediction result and the actual interaction behavior of the unmanned equipment, and dynamically implementing resource redistribution.

The invention provides a joint channel selection and power control method for a cognitive radio network, researches a scene of a plurality of primary users and a plurality of secondary shared spectrums in an underlay mode of the cognitive radio network, and provides a joint channel selection and power control MA-JCSPC method based on multi-agent deep reinforcement learning. According to the method, a spectrum access problem of primary and secondary users is modeled as a Markov decision process executed by multiple agents, and deep reinforcement learning is used for solving. In order to solve the problems of sparse rewards and invalid action exploration in the multi-agent training process, a penalty term is introduced to design a nonlinear reward function, and a new initial action selection strategy is used in the initial stage of agent action selection. The method can effectively coordinate all the secondary users to reasonably utilize the frequency spectrum, and is excellent in the aspect of the total throughput of the secondary users.

A multi-agent reinforcement learning-based optimal energy sensing threshold control method in distributed cognitive radio networks includes: (a) constructing a state space for a network environment including a plurality of primary terminals and a plurality of secondary terminals, the state space including each state about whether the primary terminals are occupied; (b) selecting an action in accordance with a policy by applying partial observation of each secondary terminal to a reinforcement learning-based actor-critic network model by means of an agent, and calculating a reward on the basis of a sensing result of the primary terminals on the basis of the selected action in the environment, the action being a sensing threshold; (c) storing the partial observation, the selected action, the reward, and next observation into a replay buffer as experiences; and (d) updating the actor-critic network model on the basis of the experiences stored in the replay buffer.

The invention discloses a cognitive radio network spectrum efficiency optimization system and method based on a clamped antenna, belongs to the technical field of wireless communication, and solves the problems that in a downlink cognitive radio network, a primary network and a secondary network share the same frequency band, so that serious same-frequency interference exists between a primary user and a secondary user, and the service life of the primary user is influenced. The problem that a traditional fixed antenna array cannot simultaneously realize the maximized coherent gain of an expected user and the interference suppression of an interference user is solved. The method comprises the following steps: constructing a cognitive radio network model; constructing an optimization problem with maximization of the sum of the spectrum efficiencies of the primary user and the secondary user as a target; optimizing the antenna clamping position of the main network; the antenna clamping position of the secondary network is optimized, specifically, coarse-scale position optimization is executed to determine the initial position, and then fine-scale position optimization is executed to adjust the phase; and configuring the cognitive radio network based on the optimized antenna clamping position to improve the total spectrum efficiency. The method is suitable for the spectrum efficiency optimization scene of the cognitive radio system.

The invention provides an L-band-oriented broadband communication interference suppression method, system, equipment and medium, and relates to the technical field of aeronautical communication and navigation.The method comprises the steps that at a receiving end, interference detection based on continuous variance rejection is conducted on received OFDM frequency domain signals, an interference detection threshold is dynamically set, and interfered subcarriers are recognized; at a transmitting end, an anti-interference OFDM signal is generated by adopting a frequency diversity mode, and interference suppression is performed at a receiving end by utilizing a selection and combination method so as to recover a signal at the transmitting end; a compressed sensing model is constructed by using empty subcarriers in received signals, DME pulse interference signals are reconstructed based on the compressed sensing model, and DME interference is eliminated in a time domain. According to the method, the characteristic that a civil aviation route is fixed is fully utilized, the cognitive radio technology is adopted, the purpose of eliminating in-band interference is achieved through in-band interference detection and diversity suppression, and the influence on a DME system is reduced by adopting a boundary optimization interference elimination algorithm.

The invention belongs to the technical field of frequency spectrum prediction, and particularly relates to a frequency spectrum state prediction method based on a hybrid deep learning model, the hybrid deep learning model is fused with a long short-term memory (LSTM) network and a multi-layer perceptron (MLP), and through three-dimensional frequency spectrum data sensing, self-adaptive dual-threshold energy detection and hybrid model prediction, the frequency spectrum state is predicted. And the accuracy of idle channel spectrum prediction is further improved. According to the method, the secondary user (SU) in the cognitive radio system (CRS) can quickly select the channel with the highest idle probability for access, the repeated sensing frequency is reduced, the total sensing time is reduced by 30%, and the effective data transmission time is improved by 30%. Compared with the prior art, the method provided by the invention is higher in frequency spectrum state prediction precision in a low signal-to-noise ratio (SNR) scene, the throughput of the system is remarkably improved, and the energy consumption of the system is lower.

The invention belongs to the technical field of mobile communication, and particularly relates to a cognitive Internet of Vehicles spectrum allocation method in an uncertain environment, which comprises the following steps: constructing a cognitive vehicle network system model comprising a base station, a plurality of cellular users and a plurality of cognitive vehicles; based on a cognitive vehicle network system model, constructing an imperfect CSI model, a spectrum sensing error model and a communication model; setting constraint conditions, and constructing a target optimization function according to the cognitive vehicle network system model, the imperfect CSI model, the spectrum sensing error model, the communication model and the constraint conditions; establishing a Markov decision process for the target optimization function; based on a Markov decision process, a multi-agent reinforcement learning algorithm is adopted to solve and obtain an optimal spectrum allocation strategy of the cognitive vehicle; according to the invention, under the scene of the Internet of Vehicles, the cognitive radio is used to solve the problem of spectrum scarcity of the Internet of Vehicles.

The invention discloses a notch reconfigurable ultra-wideband antenna for cognitive radio, and belongs to the technical field of wireless communication and antenna design. The antenna comprises a dielectric substrate, a main radiation patch, a microstrip feeder line and a notch structure patch are arranged on the front side of the substrate, a floor with a defected ground structure is arranged on the back side of the substrate, and a micro stepping motor system is further arranged; the copper column is embedded into the longitudinal through groove of the notch structure patch in a clearance fit mode, the motor drives the copper column to linearly slide, inductance-capacitance parameters of a resonance circuit are changed, and an adjustable resonance circuit is constructed; the main radiation patch optimizes ultra-wideband impedance matching, the defected ground structure expands high-frequency bandwidth, continuous reconstruction of 4.2-7.45 GHz trapped wave center frequency can be realized, and related electromagnetic interference can be accurately suppressed. Zero static power consumption is achieved through physical displacement tuning, high linearity and high robustness are achieved, and the method has high practical value in the fields of cognitive radio, short-distance communication and the like.

This invention proposes a power allocation method for a non-cooperative game-based cognitive wireless network for energy harvesting, comprising the following steps: Step S1: During a time interval of τT within a time slot T, the primary user transmitter PT transmits data to the primary user receiver PR via an antenna, and each secondary user receiver SU is equipped with an omnidirectional antenna for energy harvesting to collect energy from the PU radio frequency signal; Step S2: During the data transmission process of a time interval of (1-τ)T within a time slot T, a defined minimum signal-to-noise ratio threshold and an interference power threshold I for PU are set; Step S3: During the time interval of (1-τ)T within a time slot T, SU... i (i = 1, 2, ..., n) Power allocation is performed through a non-cooperative game; Step S4: Solve for SU within a time interval (1 - τ)T of a time slot T. i The utility function (i = 1, 2, ..., n) eventually reaches Nash equilibrium, maximizing the throughput of secondary users and the revenue of primary and secondary users. This invention improves the throughput and revenue of secondary users by finding the optimal power allocation strategy in energy harvesting cognitive radio networks while satisfying all constraints.

The invention discloses a distributed spectrum sensing noise stripping method based on wavelet gradient, and belongs to the technical field of communication and artificial intelligence crossing. The method comprises the following steps: acquiring original spectrum data through a distributed cognitive radio node; performing sliding window segmentation, Hanning window weighting and power spectrum density calculation on the data; the preprocessed data are input into a differentiable wavelet noise separation layer, the layer serves as a learnable one-dimensional convolution kernel through a parameterized biorthogonal wavelet filter, and end-to-end trainable multi-resolution analysis is achieved in a deep learning framework; calculating the power spectrum density gradient of the high-frequency detail coefficient in real time, and quantifying the noise abrupt change intensity based on a central difference method; dynamically generating a gating weight, and selectively suppressing the wavelet coefficient of the high-gradient frequency point; inputting the processed data into a one-dimensional convolutional neural network with residual connection for classification; and results are fused by adopting a clustering type collaborative decision-making mechanism.

The application discloses a method for interference utilization of a reconfigurable intelligent surface assisted cognitive non-orthogonal multiple access network, and comprises the following steps: constructing a reconfigurable intelligent surface assisted downlink cognitive radio non-orthogonal multiple access system; a superposition coding is adopted by a base station to send signals to all users, and a joint optimization of an active beamforming vector of the base station and a passive reflection phase shift matrix of the reconfigurable intelligent surface is performed to construct a constructive superposition signal for direct detection of a secondary user; the secondary user directly demodulates own data through a single user detector without performing serial interference cancellation (SIC) based on the constructive superposition signal; by utilizing the intelligent reconstruction of a wireless propagation environment by the reconfigurable intelligent surface, the application adopts a constructive interference technology to convert strong interference of a primary user into a constructive signal of a secondary user; the mechanism realizes direct detection through the constructive interference design, greatly simplifies the receiving and processing of the secondary user and reduces the time delay.

The invention discloses a throughput optimization method in a multi-channel cognitive radio network under AoI constraint. The method comprises the following steps: firstly, constructing a cognitive radio network comprising a main network and a secondary network; secondly, based on the cognitive radio network, defining a state space, an action space and a reward function of the improved soft actor-commentator C-SAC algorithm; constructing a reinforcement learning network model according to a state space, an action space and a reward function of the C-SAC algorithm, deploying the reinforcement learning network model on a receiver, and performing training according to a SAC algorithm mode; and finally, interacting with the environment according to the trained reinforcement learning network model, and performing training in stages to continuously improve the long-term total throughput of the secondary network. According to the invention, the time complexity is extremely low, the long-term throughput is optimized, the freshness of the received data is ensured, and the data mastered by the secondary receiver is prevented from being too old.

A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency window, and operate the RF transmitter at the new hopping frequency window.

The invention discloses a covert communication method under an interleaving cognitive radio network based on assistance of an unmanned aerial vehicle, and belongs to the technical field of wireless communication and secret communication. According to the method, the covert communication quality under the interleaving cognitive radio network is improved by optimizing the flight path, the transmitting power and the user association constraint of the unmanned aerial vehicle; the system model comprises a main user Warden, an unmanned aerial vehicle and a plurality of ground users; the optimization method comprises the steps of deducing the optimal block length of communication between the unmanned aerial vehicle and a ground user to improve the covert communication quality, providing a joint optimization problem of the flight track structure, the transmitting power and the user association constraint of the unmanned aerial vehicle, converting a target and constraint function into a convex function, and solving the convex function to maximize the minimum communication throughput. Through implementation of the optimization method, the minimum communication throughput between the unmanned aerial vehicle and the ground user can be improved, and the method can be widely applied to key industries such as electric power facility inspection, strong-side fixed defense and emergency communication and the public safety field.

The invention provides a multi-threshold cognitive wireless optical communication spectrum sensing system and method, and relates to the technical field of wireless optical communication, the system comprises a transmitting terminal, terminals of a plurality of secondary users and a terminal of a fusion center: the transmitting terminal processes signals and transmits optical signals through modules such as an arbitration module and an information source module; the terminal of the secondary user receives a signal through a photoelectric detection module, a three-threshold judgment threshold calculation module and the like, and outputs a local judgment result; the terminal of the fusion center determines the weight according to the historical reputation value of the secondary user, a final judgment result is obtained through weighted combination and fed back to the transmitting terminal and the terminal of the secondary user, and the reputation is updated. According to the invention, the sensing performance is effectively improved, the detection time is shortened, and the anti-interference capability and robustness of the system are enhanced.

This invention belongs to the field of cognitive radio technology based on compressed spectrum sensing, specifically relating to an adaptive compressed spectrum sensing method based on a deterministic evaluation model. Firstly, in each sensing interval of the adaptive compressed spectrum sensing, a novel signal reconstruction algorithm is proposed. This algorithm incorporates prior knowledge into the l-axis of the block-sparse signal. 2，1 The method involves norm minimization. Secondly, the CDF of the reconstructed signal error is derived and used as the stopping criterion for observation sample acquisition. Finally, the energy detection method is used to process the currently reconstructed spectral signal to obtain the corresponding spectral usage status. This method improves the accuracy of spectral sensing while providing deterministic assurance for the sensing results.

The invention provides a wireless signal modulation identification method, and belongs to the field of wireless communication technology and deep learning. The long-sequence high-order modulation signal can be efficiently and accurately identified by parallelly capturing the local features of the modulation signal and global time sequence dependence, and the method is used for the fields of cognitive radio, spectrum monitoring and the like, so that the signal identification capability in a complex electromagnetic environment is improved. The method of the invention comprises: converting a received I / Q signal into normalized amplitude (A) and phase (P) signals; adopting a segmented replacement data enhancement strategy for the AP signal; performing preliminary feature extraction on the AP signal by using a multi-channel collaborative convolution module; noise is suppressed by using a self-adaptive soft threshold denoising module; layered feature extraction is carried out through a plurality of stacked feature extraction stages, and each stage comprises a collaborative feature extraction module (CNN-Mamba2) and a feature fusion and downsampling module; and outputting a probability value of each modulation mode through a global average pooling layer and a full-connection classification layer of the deeply fused features.

This invention discloses a cognitive radio frequency (RF) signal digital fusion processing method, belonging to the fields of RF communication, software-defined radio, and radar signal processing technology. Based on a star-shaped decoupled architecture of "RF front-end - fusion processing center - digital processing back-end," this method achieves unified access and resource sharing of multiple heterogeneous RF front-ends through multi-source fusion access scheduling; adaptive analog conditioning regulates levels and bandwidth; end-to-end synchronous synchronization establishes a unified timing reference for the entire process; synchronous digitization and digital domain fusion processing complete signal standardization conversion; and intelligent routing and multi-protocol adaptation enable fusion distribution of multiple types of digital back-ends. This method fundamentally solves the pain points of traditional systems, such as high coupling, weak multi-source fusion capability, poor compatibility, low resource utilization, and insufficient synchronization, and can be widely applied to general signal processing scenarios such as communication, spectrum monitoring, radar detection, and electronic reconnaissance.

The application discloses a power special network unmanned equipment cooperative internal and external network data interaction processing method, and belongs to the technical field of power grid internal and external network cooperative data processing; is used for solving the technical problem that the data interaction efficiency and quality of the unmanned equipment in the power special network are poor in the existing scheme; task instructions, historical electromagnetic environment data and real-time working condition parameters of unmanned aerial vehicles and unmanned vehicles are collected and processed, data analysis is carried out through a pre-constructed intention-electromagnetic coupling prediction model, and initial prediction results and anti-interference resource reservation quantities are output; the collaborative resource block division of the unmanned aerial vehicle-unmanned vehicle is carried out based on the output prediction results, the channel and bandwidth allocation are optimized, and the balancing strategy is output; the working mode of cognitive radio is dynamically switched by using the balancing strategy, and intelligent merging of repeated data requests is realized by combining intention similarity clustering; the prediction results and the actual interaction behavior of the unmanned equipment are analyzed, and resource reallocation is dynamically implemented.

This invention discloses a blind detection method for broadband spectrum signals in non-cooperative reception scenarios, belonging to the field of communication signal processing and spectrum sensing technology. Taking the power spectral density estimate of the broadband received signal as input, the method first preprocesses the power spectrum using small-scale closing operations; then, it constructs a multi-scale structuring element set and performs scale-by-scale morphological opening operations and residual peak-shaving iterations on the preprocessed spectrum data to obtain and correct the baseline estimation result; next, it applies one-dimensional total variational denoising to the baseline-corrected spectrum; finally, it uses a hysteresis dual-threshold mechanism to complete the candidate signal region screening and statistics. This invention effectively overcomes the problems of non-flat noise baseline fluctuations in complex electromagnetic environments and signal edge blurring under low signal-to-noise ratio conditions. It has advantages such as high baseline estimation accuracy, high detection probability, low false alarm rate, and strong robustness, and is suitable for signal detection scenarios in cognitive radio, broadband reconnaissance receivers, and spectrum monitoring equipment.

The present application relates to a kind of wireless spectrum state image classification method based on small sample learning, belong to cognitive radio field.The method includes: the training set support set S tr With training set query set Q tr Input to embedding module, obtain the tensor set of each category;In the training set support set S tr Tensor set, the class prototype of each category of the training set support set S tr It is calculated by the K tensor closest to each category;The class prototype is input into measurement module with the training set query set Q tr Tensor set, similarity measurement is carried out, and small sample image classifier is obtained.The method used in the present application is different from the category used in the classification task of traditional machine learning algorithm and test, and such setting is conducive to training a small sample image classifier that can extract sample general features and specific features, solves the dependence problem of traditional machine learning-based spectrum state perception algorithm to data, improves the perception accuracy in blind perception, fast perception;Meanwhile, several feature vectors are used to represent image samples, and local features are used to classify images, eliminate the problem of intra-class difference and background confusion.

The invention relates to the technical field of radio signal detection, in particular to an anti-interference perception radio front-end radio frequency signal enhancement detection system, which comprises the following modules: a radio frequency signal receiving module for converting a front-end radio frequency signal into a digital sampling signal; the frequency spectrum texture recognition module is used for carrying out short-time Fourier transform on the sampled signals to generate a time-frequency spectrogram and extracting local gradient direction histogram features and wavelet packet energy entropy features to form a frequency spectrum texture feature matrix; and the interference feature modeling module is used for generating interference weight information by utilizing a probability model based on the frequency spectrum texture feature matrix, and distributing interference probability weights for each intrinsic mode function component. According to the invention, interference weight is calculated by combining a probability model based on Bayesian or Markov random fields in an interference feature modeling module, and interference probability estimation and weighting of different frequency components are realized; therefore, fine identification of low-power, intermittent and spread spectrum interference is realized.

The application provides a data transmission method and device, electronic equipment, storage medium and program product, relates to the technical field of block chain, and the method comprises the following steps: in the case that a second node uses a cognitive radio network to transmit data to a first node and uses a UAV for auxiliary transmission, acquiring a first system secrecy rate and a second system secrecy rate, the first system secrecy rate is the channel capacity difference of a first communication link and a second communication link in the case that the UAV is used as a relay, and the second system secrecy rate is the channel capacity difference of the first communication link and the second communication link in the case that the UAV is used as an interferer; determining the role of the UAV according to the first system secrecy rate and the second system secrecy rate; and sending the role information of the UAV to the UAV. According to the application, the role of the UAV is determined by calculating the system secrecy rate of the UAV under different roles, so that the role of the UAV is dynamically adjusted, and the safety of data transmission is improved.

A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF jammer having a selectable jamming frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF jammer to detect an RF transmission, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected RF transmission, process the QUBO inputs with a QUBO objective function to determine a new jamming frequency window, and operate the RF jammer at the new jamming frequency window.

A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency decoy window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency decoy window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency decoy window, and operate the RF decoy transmitter at the new hopping frequency decoy window.

The invention discloses a cognitive radio interference prediction method and device, and relates to the field of signal prediction, and the method comprises the steps: deploying a sensing sensor node sampling target frequency band, constructing a space-time frequency spectrum matrix, and extracting a time-frequency domain three-dimensional feature tensor; constructing a spatial relation graph, fusing the time-frequency features and the spatial domain topology by using a graph neural network, and generating a fusion feature vector; performing scene classification and labeling through an improved fuzzy C-means algorithm; a parallel dual-channel neural network is adopted, and a lightweight convolutional neural network and a multi-layer perceptron processing feature are combined to predict interference intensity and position an interference source; interference propagation path loss is calculated, a coverage thermodynamic diagram is generated, a spectrum access decision is optimized, and transmission parameters are dynamically adjusted. The method has the advantages that interference characteristics are accurately captured through multi-dimensional data fusion, the spectrum access decision is optimized, and the anti-interference capability and the spectrum utilization rate of a wireless communication system are remarkably improved.

The application discloses a kind of cognitive radio spectrum sensing device and method based on quantum compressed sensing, belong to cognitive radio technical field.Aiming at the problems in prior art, by designing a kind of cognitive radio spectrum sensing device based on quantum compressed sensing, the device includes broadband antenna module, electro-optic modulation microwave photon conversion module, data acquisition module and cognitive radio signal detection algorithm module, each module is sequentially connected, and the whole process from signal reception to spectrum analysis is completed jointly;The present application utilizes quantum compressed sensing to realize sub-Nyquist sampling and high compression ratio broadband multi-frequency microwave signal spectrum measurement, while achieving a detection probability approaching 100% when the photon counting rate is increased to 2 Mcps and the integration time is 40 ms, providing a new strategy for cognitive radio spectrum sensing, decision-making and switching.

The invention provides a low-interception satellite communication method and system of a satellite hopping mode based on cognitive radio, and relates to the technical field of satellite communication, and the method comprises the steps: obtaining system parameters of a target satellite communication system, and constructing a satellite map; monitoring and scanning the satellite star map to obtain available frequency resource information of each candidate satellite; determining the use priority of each candidate satellite based on the available frequency resource information of each candidate satellite, and establishing a satellite available frequency table; selecting a candidate satellite with a first priority in the satellite available frequency table as an initial communication satellite for communication; and when a preset satellite switching mechanism is satisfied, reselecting the candidate satellite from the available satellite frequency table to serve as a new communication satellite for switching. According to the invention, the satellite communication is switched by performing priority ranking according to the link quality of the satellites, so that the low-interception and anti-interference performance of the satellite communication signals is effectively improved.