19 results about "Cognitive user" patented technology

The application discloses a decision fusion method, device and system for wideband spectrum sensing. The decision fusion method comprises judging the type of cognitive user. If the cognitive user is a reference cognitive user, the original decision symbol sequence of the cognitive user is kept and sent to a fusion center so that the fusion center makes a fusion decision. If the cognitive user is a non-reference cognitive user, the original decision symbol sequence of the cognitive user is extended to obtain an extended decision symbol sequence. Then, the extended decision symbol sequence is subjected to segmentation and majority voting operation in sequence to obtain a compressed decision symbol sequence, wherein the compressed decision symbol sequence has the same length as the original decision symbol sequence of the reference user. Finally, the compressed decision symbol sequence is sent to the fusion center so that the fusion center makes a fusion decision. The application can ensure the reliability of spectrum sensing, significantly reduce the system sensing and transmission overhead, and improve the energy efficiency and realizability of cooperative spectrum sensing.

The invention discloses an Internet of Vehicles spectrum allocation method, apparatus and device, and a medium, and belongs to the technical field of vehicle communication. The method comprises the following steps: acquiring the total bandwidth of a frequency spectrum of a base station, the number of terminal users connected with the base station, the idle bandwidth of an idle frequency spectrum, an alternative coefficient of the frequency spectrum, and the service security weight, the unit frequency spectrum income and the transmission frequency spectrum efficiency of each cognitive user; the service security weight is used for representing the security of the service; according to the total bandwidth, the number of the terminal users, the idle bandwidth, the replaceable coefficient, the security weight of each service, the income of each unit spectrum, the efficiency of each transmission spectrum, a preset authorized user profit function and a preset cognitive user profit function, the bandwidth of each service is obtained; and determining the optimal shared bandwidth rented to each cognitive user in the idle spectrum and the optimal unit spectrum renting price given by each cognitive user. According to the method, the idle spectrum of the authorized user can be allocated to the cognitive user, and the utilization rate of the spectrum is improved.

The present invention provides a method and device for resource allocation of a hybrid access cognitive radio network slice. The method includes: constructing an agent for cognitive users, where the state corresponds to whether interaction is required or not, and the action corresponds to transmission power and channels; inputting the agent state into a graph convolutional neural network model of a hybrid access cognitive radio network scenario to calculate an attention head for neighbor cognitive users, and after connecting all attention heads, outputting action-related information of the agent via a convolutional layer; based on the transmission power prediction result and occupied channel prediction result of cognitive users, determining whether the action meets the requirements of communication delay, transmission rate, and interference temperature threshold, and calculating the value of a reward function; if not, punishing the reward function; using the trained neural network model and agent to allocate network slice resources for cognitive users in the hybrid access cognitive radio network. Through the above solution, the spectrum utilization efficiency is improved.

The present invention discloses a composite criterion rapid spectrum allocation method for multi-user arbitrary bandwidth access, and the composite criterion rapid spectrum allocation method for multi-user arbitrary bandwidth access comprises: S1: determining a main user and a cognitive user group, wherein the cognitive user group comprises a plurality of cognitive users; S2: constructing a spectrum allocation model for an arbitrary bandwidth access scenario according to frequency band information occupied by the main user and cognitive user information; S3: obtaining a plurality of available sub-frequency bands corresponding to the working bandwidth according to the working frequency band information and inherent frequency band of the main user and using the working bandwidth; S4: solving the spectrum allocation model according to the plurality of available sub-frequency bands and the priority of each cognitive user using a greedy algorithm to obtain the working frequency band of each cognitive user; and S5: allocating spectrum to each cognitive user according to the working frequency band of each cognitive user.

The present application discloses a spectrum cognitive collaborative anti-interference method, which includes: S1 all cognitive users establish an initialized spectrum perception neural network; S2 cognitive users perform full-band perception within the frequency band, and at the same time label all cognitive users according to the perceived spectrum data and store them in a data pool; randomly batch sample spectrum data from the data pool to train the spectrum perception neural network; S3 repeat step S2 to a preset number of cycles; S4 cognitive users select channels based on the greedy criterion to obtain an average frequency utilization success rate; S5 cognitive users obtain a loss function based on the average frequency utilization success rate, update the initially trained spectrum perception neural network, and obtain the cognitive user's anti-interference strategy based on the trained spectrum perception neural network. The present invention also discloses a spectrum cognitive collaborative anti-interference device and storage medium. The present invention adopts an intelligent perception method, and can obtain an efficient and reliable collaborative anti-interference communication strategy through independent learning.

The invention discloses a decision fusion method, device and system for broadband spectrum sensing. The decision fusion method comprises the following steps: judging the type of a cognitive user; if the cognitive user is a reference cognitive user, keeping an original judgment symbol sequence of the cognitive user, and sending the original judgment symbol sequence to a fusion center, so that the fusion center performs fusion judgment; if the cognitive user is a non-reference cognitive user, carrying out symbol extension on an original decision symbol sequence of the cognitive user to obtain an extended decision symbol sequence; then, sequentially performing segmentation and majority voting operation on the extended decision symbol sequence to obtain a compressed decision symbol sequence, the compressed decision symbol sequence being equal to an original decision symbol sequence of a reference user in length; and finally, sending the compression judgment symbol sequence to the fusion center, so that the fusion center carries out fusion judgment. According to the invention, on the premise of ensuring the reliability of spectrum sensing, the sensing and transmission overhead of the system is obviously reduced, and the energy efficiency and realizability of cooperative spectrum sensing are improved.

The present application relates to the security application field of cognitive radio spectrum sensing, and discloses a cognitive radio anti-PUE attack solution method based on an authentication center, which comprises two steps of registration and authentication processes, and the participating roles of the protocol process are a primary user network, a cognitive user, a malicious user and a third-party trusted authentication center; in the cognitive network, the number of base stations of the primary user network is far less than that of the cognitive user; the cognitive user is a user of spectrum holes, needs to access the cognitive network conveniently and quickly, and its main function should be placed on spectrum sensing and channel switching, so that the whole authentication process has the minimum modification on the function of the cognitive user and has high feasibility.

The invention discloses an SVM (Support Vector Machine) cooperative spectrum sensing method and system based on PSO (Particle Swarm Optimization), and relates to the technical field of cognitive radio. The system at least comprises a cognitive user, an authorized user and a fusion center, an authorized user, namely a primary user (PU), transmits a signal in an authorized frequency band, and the signal is influenced by path loss and fading in a propagation process; a cognitive user is a secondary user SU, a plurality of SUs are randomly distributed in a network, energy sampling is periodically carried out on a target frequency band, and local observation data are obtained; the fusion center, namely, FC, receives observation data uploaded by each SU and is used for unified judgment; and a PSO module and an SVM judgment module are deployed at the fusion center. According to the method, a hyper-parameter automatic optimization module based on particle swarm optimization (PSO) is introduced into a fusion center, so that a kernel parameter and a penalty factor of an SVM classifier can be adaptively adjusted under the conditions of a dynamic channel and different signal-to-noise ratios, and a better compromise is obtained among detection precision, robustness and calculation efficiency.

The invention relates to cybersecurity systems leveraging neuro-cognitive user profiling for real-time anomaly detection. It introduces a stealth-mode endpoint agent designed to collect encrypted micro-interaction telemetry—including cursor dynamics, typing cadence, window focus ETC—without significantly impacting system performance or requiring elevated privileges. Captured signals are transmitted to a centralized AI engine that constructs individualized behavioral baselines and detects deviations using adaptive learning algorithms. The system supports both agent-based and agentless deployments, enabling hybrid use in cloud, on-premise, and BYOD environments. Privacy compliance is addressed through local preprocessing and encryption, aligned with frameworks such as GDPR and HIPAA. The invention is designed to improve behavioral anomaly detection while minimizing operational impact and supporting compatibility across enterprise environments.

The present invention relates to the field of radio communication technologies. Specifically, it is a method for identifying malicious users in cognitive radio based on graph neural networks. The graph neural network is introduced into the identification of malicious users in a centralized multi-user cognitive radio. First, the degree of abnormality of each cognitive user in the cognitive radio network is obtained through the graph neural network. Then, the fusion center combines the global decision and the degree of conflict between each cognitive user and its neighbor users to establish an abnormality model. The malicious user identification method of the present invention can solve the problem of misjudgment caused by malicious users launching Byzantine attacks when judging the presence of primary users in cognitive radio, and can avoid malicious users preempting the frequency bands of primary users, enabling the frequency spectrum to be used by normal users.

The present invention relates to a robust resource scheduling method for cognitive backscatter communication for information security, belonging to the field of the Internet of Things. The method includes the following steps: S1: Establish a transmission model for a multi-user cognitive backscatter communication system; S2: Considering the constraints such as the quality of service of cognitive users, reflection coefficient, energy harvesting, transmission time, interference power, etc., construct a resource scheduling problem for maximizing the throughput of the cognitive backscatter communication system; S3: Considering spectrum sensing errors and bounded channel uncertainties, establish a multi-variable coupled non-linear robust resource scheduling model; S4: Use the worst-case criterion, successive convex approximation, and alternating optimization method to transform the original problem into a deterministic convex optimization problem, and use convex optimization tools to solve it to obtain the scheduling methods for transmit power, transmission time, and reflection coefficient. The present invention can effectively improve the transmission security and robustness of the passive Internet of Things system, and has high practical value.

The invention belongs to the technical field of wireless communication, and particularly relates to a method for avoiding multi-user spectrum access conflicts in an interference environment. The invention provides a dynamic feedback sectional reward mechanism combining cognitive user spectrum access accuracy and resource allocation, which is applied to a centralized training distributed decision deep reinforcement learning framework so as to cope with multi-user spectrum access conflicts. External malicious interference can be avoided in a complex electromagnetic environment, the defect that a centralized algorithm is prone to malicious attacks is overcome, and the robustness of a communication system is improved while the communication requirement is guaranteed. A dynamic feedback sectional reward mechanism can adjust decisions of the cognitive users according to environmental changes, collision and mutual interference of the cognitive users are avoided under the condition that spectrum utilization is high in load, and the spectrum resource utilization rate of the system is improved.

The invention relates to the technical field of cognitive radio, in particular to a self-adaptive perception threshold setting method and device based on main user power detection, and the method comprises the following steps: 1, calculating a high threshold and a low threshold of cognitive users SU, sending a signal-to-noise ratio of a received signal to a fusion center by each SU, and calculating a weighting factor of each user, each SU adaptively adjusts a decision threshold according to the weighting factor; 2, the SU compares the current energy with a decision threshold value, if the current energy is larger than a high threshold value, it is judged that a primary user PU exists, and if the current energy is between the high threshold value and a low threshold value, the average value of the historical energy queue is taken as the current energy, and the next round of detection is started; 3, if the average value of the historical energy is between the high threshold value and the low threshold value, calculating a single threshold value; and step 4, determining that the PU exists as long as one cognitive node detects the signal. According to the method, the processing efficiency of the algorithm is improved, and the detection performance of energy detection can be remarkably improved.

The present invention belongs to the field of radio communication technology, specifically, it is an optimization method for maximizing energy efficiency in a cognitive drone edge computing network, which uses drones as both cognitive users and source nodes for joint spectrum perception, and as relay nodes for auxiliary computing and data transmission. Under information causality constraints, interference power constraints, and shared data bit constraints, the energy efficiency in the cognitive drone edge computing network is taken as the optimization goal, and a particle swarm algorithm is used to update and iterate the speed and position, optimize the perception time, computing resources, and communication resources, and obtain the optimal perception time, data bit amount, and central processing unit frequency, so as to maximize the energy efficiency in the cognitive drone edge computing network. The present invention can improve the energy efficiency of the edge computing network system.

The invention relates to a spectrum sensing method under S [alpha] S noise based on parameter estimation and logarithmic moment preprocessing. The method comprises three stages of S [alpha] S distribution noise parameter estimation, received signal logarithmic moment preprocessing and detection judgment. In an S alpha S distribution noise parameter estimation stage, a cognitive user estimates a mean value and a variance of an absolute value logarithm form based on offline S alpha S distribution noise data received by multiple antennas; in the logarithmic moment preprocessing stage, a cognitive user firstly carries out logarithmic operation processing on received signal data and then further carries out normalization processing; in the detection and judgment stage, the difference between the maximum characteristic value of a preprocessed sample covariance matrix and the mean value of reciprocals of all characteristic values is used as a perception judgment quantity, meanwhile, a theoretical judgment threshold value is calculated in combination with asymptotic processing and extreme value characteristic value probability distribution, prior statistical information of S alpha S distribution noise is not needed, the blind detection characteristic is achieved, and the detection accuracy is improved. The method is wide in application range, and has good practicability for the main user signal detection problem in the S alpha S distribution noise.

The present invention discloses a method for cognitive user adaptive access in a transmission deadline communication scenario. In this solution, cognitive users can independently observe the channel to obtain the latest estimate of the number of active cognitive users in the current time slot. When the licensed user does not occupy the channel, the cognitive user determines the access probability that maximizes the current expected reward under the multi-packet reception mechanism according to the confidence state distribution of the number of active cognitive users obtained from the network observation. For cognitive users, the packet arrival probability conforms to the binomial distribution and has a strict transmission deadline. The system uses an acknowledgment and retransmission mechanism, and the receiver has the multi-packet reception ability (i.e., can successfully receive multiple packets simultaneously). A method for cognitive user adaptive access in a transmission deadline communication scenario also gives the access probability calculation method, which can make the network reliability close to the theoretical optimal reliability while ensuring a low computational complexity.