134 results about "Cognitive radio" patented technology

The invention discloses an information encryption method for wireless electronic countermeasure, and relates to the technical field of wireless transmission. According to the method, the wireless channel state is monitored in real time, the channel quality evaluation model is established, and the adaptive frequency adjustment algorithm and the cognitive radio technology are combined, so that whether the channel meets the communication quality requirement or not can be accurately judged, the influence of interference and attack on communication is effectively avoided, the continuity and stability of the communication process are ensured, and the communication quality is improved. The adaptability and the reliability of a communication system in a complex electromagnetic environment are remarkably improved, the quantum key is generated based on an encryption communication protocol of quantum key distribution, extremely high security of the key is ensured, communication data are difficult to crack in the encryption transmission process, security threats such as eavesdropping and tampering are effectively resisted, and the security of the communication system is improved. And through a secret key pre-distribution mechanism and a secret key updating process, the insufficient secret key distribution rate and the potential security risk are dealt with, the security of communication is further enhanced, and the confidentiality and integrity of information transmission are ensured.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to an anti-unmanned aerial vehicle detection method and system, and the method comprises the following steps: scanning a target airspace in real time through broadband cognitive radio, and constructing an environment feature matrix; based on the environment feature matrix, double-flow feature extraction is carried out, and a protocol fingerprint vector is generated; activating a corresponding group behavior analysis model according to the protocol fingerprint vector, and constructing a behavior topological graph; constructing a dynamic threat vector by adopting a game theory in combination with the behavior topological graph and the real-time motion parameters; generating a multi-dimensional interference strategy set according to the dynamic threat vector; according to the invention, an omnibearing and real-time updated threat vector is constructed, a node threat value is calculated through a non-cooperative game model, accurate evaluation is carried out in combination with spatio-temporal information, potential threats are effectively identified, accurate interference on an unmanned aerial vehicle group is realized, the perception capability of unmanned aerial vehicle behaviors in a complex scene is enhanced, and the risk of the unmanned aerial vehicle behavior in the complex scene is reduced. And the execution precision and efficiency of the interference strategy are obviously improved.

The invention provides a radio signal modulation classification method based on a deep integrated network, and the method comprises the steps: S1, constructing an attention CNN module, inputting an IQ signal sample into the attention CNN module, and converting the IQ signal sample into multi-channel feature mapping; s2, constructing a time sequence information extraction module, and extracting a sequence mode from the local features by using the time sequence information extraction module; s3, constructing a visual converter module, and inputting an output result of the time sequence information extraction module into a global feature extractor module to extract global features so as to obtain feature vectors representing modulation categories; and S4, constructing a classifier, mapping the feature vector representing the modulation type from a high-dimensional space to a low-dimensional space representing the signal type, and then performing activation by using softmax to obtain the probability distribution of the modulation type of the signal. The method provided by the invention shows excellent accuracy and expandability under different signal-to-noise ratio levels and modulation modes, and provides favorable support for cognitive radio and other wireless communication systems in practical application.

Aspects of this disclosure relate to a radio may include a transceiver configured to communicate signal packets. A radio may include a memory configured to store a multilayer process. A radio may include hardware processors in communication with the memory. The hardware processors may be configured to identify transmission parameter data at a time period between successful communications of the signal packets, store the transmission parameter data in a buffer in the memory, pass the transmission parameter data to the multilayer process to determine a plurality of potential operating configurations comprising at least one of a power operational setting, a frequency operational setting, a multiple-input multiple-output operational setting, or a time operational setting, arbitrate the plurality of potential operating configurations to determine a final set of operating configurations; and update operation of the radio based on the final set of operating configurations.

A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.

The invention relates to a non-cooperative communication signal modulation identification method based on multi-scale feature fusion extraction, which belongs to the technical field of cognitive radio communication, and comprises the following steps: generating time domain signals of communication signals in different modulation patterns, and preprocessing to obtain a plurality of baseband IQ signals as sample data; forming a communication signal data set with the corresponding modulation pattern as a sample label; initializing a multi-scale feature fusion extraction network model, and training the multi-scale feature fusion extraction network model by using the modulation signal data set to obtain a trained multi-scale feature fusion extraction network model; and obtaining a time domain signal of a non-cooperative communication signal in real time, performing preprocessing, inputting the trained multi-scale feature fusion extraction network model, and obtaining a recognition result of a corresponding time domain signal modulation pattern. Accurate and efficient identification of the non-cooperative communication signal modulation pattern is realized.

The invention belongs to the technical field of cognitive radio, and particularly relates to an open set modulation identification method based on time-frequency domain feature learning and fusion. The method comprises the following steps of: receiving a signal by adopting a receiving antenna through electromagnetic spectrum monitoring equipment, capturing a space electromagnetic radiation signal, converting the space electromagnetic radiation signal into an electric signal, amplifying the signal through a low-noise amplifier, performing frequency conversion and filtering through an analog receiver, and converting the received signal into an intermediate-frequency signal. Then, the signal is digitalized through AD sampling, and the intermediate frequency signal is converted into a baseband complex signal through frequency mixing and digital filtering in a digital receiver. Then performing time domain characterization calculation and frequency domain characterization calculation on the signal sampling data to form a plurality of time domain characterization vectors and a plurality of frequency domain characterization vectors; and then the vectors are sent to a trained deep neural network model, reasoning is carried out by using the network model, a reasoning result is post-processed, and a signal modulation identification result is finally obtained. According to the invention, the recognition accuracy of unknown signals is improved.

The invention discloses a wireless authentication and privacy infrastructure (WAPI) seamless roaming optimization system and method for a mobile scene. Data collaboration and intelligent decision are realized through a three-layer structure of a mobile terminal, a WAPI access point and a switching control center. A mobile terminal collects a motion state and channel information in real time, a WAPI access point monitors a wireless condition in an area while meeting a WAPI security authentication requirement, a switching control center integrates data of all parties, a candidate access point utility value is dynamically calculated through a built-in strategy decision unit by adopting reinforcement learning, cognitive radio and robust control technologies, and the candidate access point utility value is calculated through a wireless network. Therefore, optimal roaming switching is realized. A pre-authentication and certificate caching mechanism is introduced into the system before switching, so that the authentication time delay is greatly reduced, and stable network connection and seamless transition during high-speed movement are ensured. According to the invention, the structure is simple, the adaptability is high, the WAPI security requirement is met, and the network roaming performance in a mobile scene is remarkably improved.

The invention relates to the technical field of wireless communication, and discloses a sensing radio front-end radio frequency signal detection system, which comprises a broadband radio frequency front-end module used for receiving radio frequency signals in an environment and supporting multi-band signal capture and anti-interference preprocessing; the analog-to-digital conversion module is used for converting the radio frequency signal into a digital signal by adopting an ADC (Analog to Digital Converter) with 14-bit resolution and 2GSPS sampling rate; the signal processing module is connected to the output end of the analog-to-digital conversion module and is used for performing spectrum analysis, feature extraction and signal detection on the digital signal and outputting a detection result; and a synchronous control bus. According to the method, energy detection, cyclostationary feature analysis and a deep learning classification algorithm are fused, a time-frequency domain joint analysis framework is constructed, and the time domain transient feature extraction capability of the 1D-CNN and the frequency domain long-range dependence modeling advantage of Transform are combined, so that high-precision recognition of complex modulation signals is realized, and the detection sensitivity and the anti-interference capability are improved.

The invention belongs to the technical field of cognitive radio, and particularly relates to a broadband multi-signal detection method based on a large-size convolution kernel. The method mainly comprises the following steps of: receiving a signal by adopting a receiving antenna through electromagnetic spectrum monitoring equipment, capturing a space electromagnetic radiation signal and converting the space electromagnetic radiation signal into a baseband complex signal, carrying out large FFT point short-time Fourier transform on signal sampling data to form a high-resolution and large-size time-frequency matrix, and carrying out time-frequency spectrum detection along a frequency axis of the time-frequency matrix; a time-frequency matrix is cut into a plurality of time-frequency matrix fragments adapted to neural network processing, and parameters such as confidence probability, center frequency, bandwidth, arrival time, duration and the like of existence of signals in each time-frequency matrix fragment are automatically calculated and generated by using a specially designed and trained deep neural network model. The method has the advantages that the operation of monitoring equipment is simplified, the signal detection speed is high, the detection accuracy is high, the detection recall rate is high, and the time-frequency occupancy parameter estimation precision is high.

The present invention provides a method and device for optimizing cognitive edge computing node parameters based on reinforcement learning. The method includes: determining a partially observable Markov decision model based on the frequency band usage status of the current time slot of the main user end, using the partially observable Markov decision model to determine the belief probability corresponding to the main user end in each future time slot and the observation probability and reward corresponding to the secondary user end in each future time slot; constructing a Bellman optimization model based on the belief probability, the observation probability and reward corresponding to each future time slot, and the target state probability of the secondary user end, and maximizing the average reward within the preset future time slot range under the corresponding target state based on the Bellman optimization model, and determining the corresponding cognitive edge computing node parameter optimization strategy. The method disclosed in the present invention can maximize the long-term benefits of the secondary user end in cognitive radio scenarios, improve performance optimization efficiency, and save costs.

The invention discloses an STAR-RIS-assisted cognitive radio network and a rate optimization method, and relates to the technical field of radio network rate optimization, and the method comprises the following steps: obtaining channel state information between nodes of the STAR-RIS-assisted cognitive radio network; the STAR-RIS assists the SBS to transmit a signal to the secondary user, and calculates the transmission rate of the secondary user and the interference suffered by the primary user; constructing a secondary user and rate maximization problem of joint optimization of SBS emission beam forming and STAR-RIS reflection and transmission coefficients; and decomposing the optimization problem into two sub-problems, and iteratively solving the two sub-problems through an alternative optimization algorithm to obtain the optimized secondary user and rate. According to the STAR-RIS assisted cognitive radio network and the rate optimization method provided by the invention, the signal coverage range is expanded through the STAR-RIS-ES assisted scheme, and the spectrum efficiency of secondary users of the cognitive radio network is improved.

The invention provides a spectrum sensing internet of things communication fair access method and system, and the method comprises the steps: building a spectrum sensing internet of things communication system model which is a model integrating cognitive radio, energy collection and environment backscatter communication; according to the spectrum sensing-based internet-of-things communication system model, energy constraints, time resource constraints and QoS constraints of a main communication link are considered, and a fair access problem of throughput maximization of a spectrum sensing internet-of-things communication system is constructed; and converting the fair access problem into a convex optimization problem, and solving the convex optimization problem by using a convex optimization solver to obtain the fair access parameter of the Internet of Things communication. According to the Internet of Things communication system model based on spectrum sensing, the utilization rate of spectrum resources can be improved, and fair access of Internet of Things communication is achieved under the condition that multiple constraints are comprehensively considered.

The invention provides a spectrum detection-based denial environment unmanned aerial vehicle network congestion control method and system, the system is composed of a plurality of unmanned aerial vehicles and a single control station, and the method comprises the following steps: the unmanned aerial vehicles collect and generate interference sensing information and send the interference sensing information to the control station; the control station gathers the information and maintains an interference sensing information pool; the sender unmanned aerial vehicle starts data transmission and sends an interference condition detection request to the control station; the control station generates a detection result according to the information pool and the detection request, and notifies the sender of the unmanned aerial vehicle; and the unmanned aerial vehicle of the sender analyzes the detection result and performs congestion control according to the detection result, so that a congestion window adapts to available bandwidth change caused by frequent interference. According to the method, the cognitive radio technology is combined, control station cooperation is introduced, semi-adaptive congestion control with the interference sensing capacity is achieved, invalid transmission is avoided, the transmissible time period is fully utilized, the reasonable transmission rate is kept, and therefore the higher throughput and the lower packet loss rate are achieved.

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 discloses a cognitive radio-oriented high-level semantic information acquisition method, which comprises the following steps of: performing spectrum situation sensing based on a received original electromagnetic signal, judging whether an idle authorized spectrum exists or not, and acquiring frequency equipment information in a current network; performing high-level semantic information reasoning based on the frequency spectrum situation sensing information to obtain a topological structure of a current network and service type information transmitted between nodes; performing interference estimation on the filtered original electromagnetic signal, performing interference cognition based on an interference estimation result, and extracting interference type information; and performing interference suppression on the filtered original electromagnetic signal based on an interference estimation result, performing channel estimation on an interference suppression result, performing channel cognition based on a channel estimation result, and extracting channel type information. According to the method, the multi-domain cognitive ability of the cognitive radio station is enhanced, and more decision information is provided for the cognitive radio station to adaptively adjust the internal state and the transmitting parameters according to the external environment change.

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 present invention proposes a RIS-assisted cognitive radio wireless security communication transmission method using partial CSI. In the RIS-assisted large-scale multi-input multi-output cognitive radio wireless security communication system, in order to enable the secondary transmitter and the secondary user to communicate with the main transmitter and the main user in the same frequency band, and to ensure that the interference of the secondary transmitter to the main user is within a certain threshold, the initial value of the RIS reflection coefficient matrix is ​​set, and the beamforming matrix is ​​designed using the partial CSI; the RIS reflection coefficient matrix is ​​designed using the partial CSI according to the beamforming matrix; the above operations are repeated until the system security rate converges. The present invention can effectively improve the spectrum efficiency under the premise of knowing the partial CSI, alleviate the spectrum scarcity problem in actual communication scenarios, and improve the security rate of the system.

The present invention provides a modulation recognition method based on Markov switching fields and deep learning, which relates to the field of cognitive radio technology and includes the following steps: S1, receiving a radio signal and generating Markov switching field features by using the in-phase / quadrature data of the radio signal; S2, reducing the dimension and reshaping the Markov switching field features, and then splicing them with the in-phase / quadrature data into hybrid data; S3, establishing and training a convolutional assisted Transformer model to obtain a trained Transformer model; S4, using the trained Transformer model to identify the modulation mode of an unknown signal, using the test set of the hybrid data as the input and the modulation mode label as the output. The present invention uses two complementary data, namely the I / Q sequence and the Markov transition graph, as the input of the model, greatly improving the recognition accuracy. The convolutional assisted Transformer model proposed by the present invention can simultaneously extract the local and global features of the data, enhancing the recognition accuracy.

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 discloses a wireless signal automatic modulation identification method based on a CGAF and a residual error identification network model, and the method comprises the steps: firstly, enabling the I, Q and I / Q one-dimensional time features of a wireless signal to be mapped to a two-dimensional space image domain through a complex number field Gramb angle field algorithm; therefore, the technical bottlenecks of high time domain feature similarity and low feature space separability of a modulation mode are effectively solved. On the basis, a channel segmentation residual neural network is designed as a classifier, feature extraction is performed through channel segmentation and an attention mechanism, and information is simplified by using a residual module, so that the classification precision of a modulation mode and the system robustness are remarkably improved. According to the method provided by the invention, the accuracy and robustness of modulation recognition are remarkably improved in a complex wireless channel environment, the problems of single feature and insufficient model learning ability of a traditional method are solved, and reliable technical support is provided for application of automatic modulation recognition in the fields of cognitive radio, military reconnaissance and the like.

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 present invention belongs to the field of cognitive radio technology, and specifically relates to a broadband signal reconstruction method based on sub-Nyquist sampling. Under the condition of known signal sparsity, the present invention aims to solve the problem that the OMP algorithm cannot reflect the relationship between the residual and the atom through the inner product under low signal-to-noise ratio. In addition, the present invention selects the correlation coefficient to replace the inner product in a mathematical sense, and combines the characteristics that the sub-band signal of the MWC system occupies at most two spectrum fragments, so that the number of iterations is halved, thereby improving the reconstruction probability of the support set and reducing the reconstruction time. The present invention replaces the inner product with the correlation coefficient as a method for finding the atom with the highest correlation, taking into account the characteristics that the signal brought by the MWC sampling structure occupies at most two spectrum fragments, thereby improving the reconstruction probability under low signal-to-noise ratio and reducing a certain reconstruction time.

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 present invention relates to a spectrum allocation method for a cognitive radio network based on a binary dragonfly optimization algorithm. The method takes maximizing network benefits as an optimization goal, takes a graph theory coloring model as a spectrum allocation model, and designs a spectrum allocation scheme with fast convergence and higher precision based on an improved binary dragonfly algorithm; Halton sequences are used in the dragonfly algorithm instead of random initialization to construct a more uniform initial distribution of the population, enhance the diversity of the population, and accelerate the convergence speed; the introduction of nonlinear inertia weights helps to dynamically adjust the search step size during iterations, improve the algorithm's global search capability and convergence accuracy; and the dragonfly algorithm is extended to the field of binary allocation problems using a V-type binary code conversion formula. The spectrum allocation scheme provided by the present invention has the characteristics of fast convergence and high precision, and can greatly improve the utilization efficiency of spectrum resources.

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 low-complexity multi-user MIMO (Multiple Input Multiple Output) cognitive radio system beam forming design method based on methods such as an improved subgradient algorithm and a classical WMMSE (Weighted Minimum Mean Square Error) algorithm, and compared with the existing MIMO multi-user cognitive radio beam forming technology, the method provided by the invention has the advantages that: on the premise of ensuring the weighted sum rate (WSR) of secondary users, the beam forming design of the MIMO multi-user cognitive radio system is realized; compared with the prior art, the method has the advantages that the calculation complexity is greatly reduced, especially when the number of antennas is large, the method has obvious advantages in the aspect of complexity, and application of an actual system is easier.