84 results about "Cognitive radio" patented technology

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 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 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 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 broadband frequency reconfigurable non-reflection filter loaded with an inductance lumped element, a filter main circuit is formed by connecting a high-impedance structure of series inductors and a coupling microstrip line, one end of the coupling microstrip line is grounded, the other end of the coupling microstrip line is loaded with a varactor, and an auxiliary absorption channel absorption circuit is mainly formed by coupling microstrip lines with unequal line widths. Through synchronous tuning of the complementary branches, resonant frequencies of the filtering main circuit and the auxiliary channel absorption circuit are kept in synchronous matching, passbands of the two circuits are complementary, and the broadband non-reflection characteristic is achieved. The broadband frequency reconfigurable non-reflection filter is large in tuning range, simple in overall circuit structure, small in size, good in absorption effect, wide in tunable bandwidth and suitable for multi-band wireless application such as 5G / 6G communication, radar and cognitive radio.

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.

Disclosed is a cognitive radio, CR, communication device, including a CR transceiver and a digital twin of the CR transceiver. The CR transceiver includes a radio scene analyser for analysing state information of a radio scene involving the CR communication device, and a cognitive engine for controlling a radio performance of the CR communication device. The cognitive engine includes a radio performance analyser for analysing a radio performance of the CR transceiver or a radio performance of the digital twin; a radio performance optimizer for optimizing the radio performance of the CR transceiver or the radio performance of the digital twin in accordance with the radio performance analyser and the radio scene analyser; and a radio configurator for configuring the CR transceiver or the digital twin in accordance with the radio performance optimizer.

The invention discloses a spectrum sensing analysis method in an STAR-RIS (Short Time Assisted Recognition-Rare Information System) assisted cognitive radio system, which is characterized in that the spectrum sensing analysis method in the STAR-RIS assisted cognitive radio system is provided with a spectrum sensing module, a spectrum sensing module and a spectrum sensing module, and the spectrum sensing module and the spectrum sensing module are used for sensing and analyzing the spectrum sensing in the STAR-RIS assisted cognitive radio system. A multi-antenna base station is considered, a system model is established, statistical channel information is used for optimizing the phase of STAR-RIS, and a theoretical expression of the correct detection probability of the cognitive radio system is deduced; through simulation verification, the performance analysis method provided by the invention is effective for evaluating the system performance. According to the method, the spectrum sensing of the STAR-RIS assisted cognitive radio network is researched, the performance characteristics of a multi-antenna base station are considered, the phase of the STAR-RIS is optimized, statistical analysis of the correct detection probability is obtained, and effective guidance is provided for performance evaluation of systems of the same type.

The invention discloses a reactive interference-oriented secure cognitive radio network power allocation method, and belongs to the field of communication resource allocation and anti-interference secure communication of a cognitive radio network. The implementation method comprises the following steps: analyzing secondary user communication requirements and disturber behavior characteristics, constructing a CR network model containing time-varying channel gain and a random reactive interference model, and deducing an optimal dynamic energy detection threshold of a disturber; constructing a CR network power distribution optimization problem by taking maximization of a secondary user communication rate and avoiding of reactive interference attacks as targets; aiming at the dynamic uncertainty of channel gain and interference behaviors, constructing a Markov decision process based on a deep reinforcement learning framework, and giving definitions of basic elements such as states, actions, rewards and the like; and constructing a double-depth Q network, and solving to obtain a CR network power distribution scheme, thereby realizing the power distribution of the CR network in the reactive interference scene. The method has the advantages of high communication security, high robustness in a complex environment and the like.

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.

The invention discloses a collaborative spectrum sensing system and a collaborative spectrum sensing method based on semantic communication, which are applied to the technical field of cognitive radio and aim at solving the problem of how to reduce the communication burden of sensing information reporting while ensuring the spectrum sensing accuracy and robustness. According to the method, the spectrum semantic features are extracted and compressed at the secondary user side, and the joint source channel coding and decoding mechanism is utilized at the fusion center, so that the detection accuracy is ensured, the communication overhead of sensing information reporting is greatly reduced, and the robustness of the system in a severe channel environment is enhanced.

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.