51 results about "Radio spectrum" patented technology

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention belongs to the technical field of integrated circuits, and particularly relates to a time mismatch extraction method based on parallel windowing autocorrelation. According to the method, firstly, clustering processing is carried out on signal points; thirdly, preliminarily screening out legal signals based on the time occupancy rate of the signals, and constructing an initial legal signal library; a similarity score between the signals is further calculated through an IDK algorithm, and adjacent frequency bands are divided through a Pettitt method; and finally, continuation of legal signals is realized by means of a DBSCAN clustering algorithm, and automatic construction of a legal signal library is completed. The method is not only suitable for scenes without prior information, but also can be applied under the condition with partial prior information, and can be dynamically updated according to actual requirements. The method is based on radio frequency spectrum monitoring big data, can construct a legal signal library with high reliability and strong generalization ability, provides effective support for subsequent illegal signal monitoring, and has important application value.

The invention discloses an unmanned aerial vehicle detection and directional interference system based on an artificial intelligence technology, which belongs to the technical field of unmanned aerial vehicle detection interference and comprises a data acquisition module, an unmanned aerial vehicle identification module, a geographic information correction module, a trajectory prediction module and a directional interference module. The data acquisition module is used for acquiring geographic information data of a target area based on a geographic information system, comprises a radar, an acoustic sensor and a radio frequency spectrum detection sensor, and is used for acquiring feature data of a moving object in the target area; and the unmanned aerial vehicle identification module obtains feature data of a moving object in the target area based on the data acquisition module. According to the unmanned aerial vehicle detection and directional interference system based on the artificial intelligence technology, the obstacle in the target area is identified, the personalized behavior of the unmanned aerial vehicle is analyzed, the trajectory prediction and interference strategy of the unmanned aerial vehicle is optimized in combination with topographic features, and the trajectory prediction precision and the directional interference effect are improved.

The utility model discloses a tower crane radio environment monitoring device which comprises a shell installed at the position consistent with the height of a tower crane communication antenna, and a multi-frequency-band signal receiving module, an active frequency spectrum scanning module, a signal filtering processing module and a data aggregation and output module which are arranged in the shell. The multi-frequency-band signal receiving module is used for receiving radio signals of a plurality of preset frequency bands used for normal communication of the tower crane; the active frequency spectrum scanning module is isolated from the multi-band signal receiving module in a physical structure and is used for actively scanning a radio frequency spectrum in a tower crane operation area; the signal filtering processing module is in signal connection with the multi-band signal receiving module and the active spectrum scanning module; the data aggregation and output module is in signal connection with the signal filtering processing module, and the communication interface is in signal connection with the data aggregation and output module. The radio environment monitoring device can sense the radio environment experienced by the tower crane communication antenna in the actual operation process in real time.

The application relates to the technical field of radio signal detection, and discloses a method and system for detecting radio frequency spectrum of an unmanned aerial vehicle based on difficult example mining. The method comprises the following steps: performing time-frequency analysis on a received radio signal to obtain one-dimensional spectrum sequence data; inputting the one-dimensional spectrum sequence data into a target detection model based on an SSD architecture to perform detection, wherein the model adopts multi-scale features for detection and is trained through an improved adaptive loss function and a negative sample enhancement strategy; and determining the starting and ending frequency points of a target subsequence corresponding to the spectrum of the unmanned aerial vehicle signal according to the model output. The system comprises a double-antenna array, a radio frequency receiving unit, an industrial control processing unit and a front-end device for signal receiving and processing. The application effectively improves the detection precision and robustness of the unmanned aerial vehicle signal in a complex electromagnetic environment, significantly reduces the false alarm rate, and is suitable for real-time monitoring and spectrum management of the unmanned aerial vehicle.

A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an radio spectrum binding service. The service may include generating and provisioning binding information. The binding information may include radio frequencies correlated to applications or categories of applications associated with packet data unit sessions or radio frequencies correlated to applications or categories of applications associated with packet data unit sessions, and network slice identifiers. The service may include selecting and using the binding information for packet data unit sessions.

The invention relates to the technical field of signal intensity detection, and discloses a signal intensity detection visualization processing method based on unmanned aerial vehicle three-dimensional data acquisition, and the method comprises the steps: installing a radio frequency spectrum sensor on an unmanned aerial vehicle, and carrying out the flight path planning of the unmanned aerial vehicle for a target region after grid division; the unmanned aerial vehicle collects signal data in real time in the flight process; based on the unmanned aerial vehicle positioning information and the signal intensity, positioning a signal source by using an extended Kalman filtering algorithm; carrying out fitting and signal diffusion processing on the signal intensity based on the signal source position; and visualizing the signal intensity of the target area based on the signal intensity distribution detection result. According to the invention, the extended Kalman filtering algorithm is used to filter signal data of each cluster in combination with timestamp information, signal source positioning is realized, signal intensity fitting results of different signal source positions are generated, signal intensity calculation is carried out, and signal intensity visualization is carried out on different positions of a target area.

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products related to multi-radio spectrum sharing (MRSS) cell access are described. One method may include receiving, in a cell access broadcast channel, a cell access broadcast transmission associated with a first radio access technology, determining that cell access information associated with the first radio access technology is received in the cell access broadcast transmission, determining that cell access information associated with a second radio access technology is received in the cell access broadcast channel, determining the cell access information associated with the second radio access technology based on reserved bits in the cell access broadcast channel and the cell access information associated with the first radio access technology, and receiving cell system information associated with the second radio access technology using the determined cell access information associated with the second radio access technology.

The invention discloses an energy efficiency self-adaptive regulation and control device and method for frequency spectrum detection equipment, and relates to the technical field of radio frequency spectrum monitoring. The device comprises a dynamic sleep wake-up module, an energy efficiency self-adaptive adjustment module, a task priority management module and a feedback optimization module, during standby, environment radio frequency signals are sampled at a low rate, intensity, bandwidth and period characteristics are extracted, and a wake-up threshold is dynamically adjusted based on target characteristic judgment and noise and leak detection to trigger wake-up; during working, the gain, the sampling rate, the quantization precision and the algorithm complexity are jointly adjusted according to task requirements and energy efficiency constraints, and a performance accepting and rejecting instruction is generated according to task priorities when resources are limited; and collecting detection, power consumption and performance data in an idle state, and updating a wake-up threshold value and operation parameters for a next period through reinforcement learning or regression analysis. By means of the device and method, invalid wakeup can be restrained, the missing detection risk can be reduced, and self-adaptive balance of energy efficiency and task performance is achieved.

The present application relates to the field of data processing, in particular to a radio spectrum monitoring method and system of low altitude defense system, comprising: performing short-time Fourier transform on signal time sequence data collected by any monitoring point to obtain a time-frequency matrix, taking the time-frequency matrix of any monitoring point as a target matrix, calculating the frequency domain reliability of the target matrix according to the energy proportion of each frequency point and the correlation degree of each frequency point, combining with information entropy theory, calculating the periodicity degree of the target matrix, obtaining the monitoring reliability of the monitoring point corresponding to the target matrix according to the frequency domain reliability and the periodicity degree, inputting the time-frequency matrix of each monitoring point into a preset prediction model of the probability of existence of unmanned aerial vehicle to obtain the prediction probability of each monitoring point, taking the monitoring reliability of each monitoring point as a weight, and weighting and summing the prediction probability of all monitoring points to obtain the total probability of existence of unmanned aerial vehicle. The present application can improve the accuracy of unmanned aerial vehicle monitoring.

The application relates to a wireless communication test method, system and medium based on a portable spectrum analyzer, and belongs to the technical field of radio spectrum analysis. The wireless communication test method comprises the following steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the electromagnetic signal original data; performing modulation mode identification, protocol analysis and terminal identification extraction on the pretreated signal matrix, generating a multi-dimensional feature vector, and performing convolution similarity matching with a legal signal feature template to assign a threat level label; dynamically reconstructing the scanning parameters of the portable spectrum analyzer according to the threat level label, generating a device parameter adjustment record, and updating an electromagnetic fingerprint library; and generating a spectrum optimization control instruction based on the threat level label and the device parameter adjustment record, and delivering the spectrum optimization control instruction to a target base station or a terminal device. The application improves the identification accuracy of threat signals and the control closed-loop linkage ability in a complex electromagnetic environment.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may determine resource selection priority levels associated with available resources for sidelink signaling in an unlicensed radio frequency spectrum band, the resources selection priority levels may be determined based on resource reservation information of the available resources, a time domain location of the available resources, or both. For example, a first resource of the available resources may be associated with a higher priority than a second resource of the available resources in resource selection. The UE may select one or more resources for the sidelink signaling from the available resources based on the resource selection priority levels associated with the available resources. The UE may transmit the sidelink signaling on the unlicensed radio frequency spectrum band using the one or more selected resources.

This invention discloses a mobile radio spectrum monitoring system and method based on a quadruped robot platform, belonging to the field of radio direction finding technology. The system includes a quadruped robot body and a monitoring module mounted on it. The monitoring module includes: a seeker head, mounted above the robot body via an elevated frame, with a built-in array antenna for acquiring radio frequency signals; a signal processing chamber, containing an SDR receiver connected to the array antenna to receive signals; a pose sensor module to acquire the robot's pose; a data processing module to perform spatial spectrum direction finding on the signals and fuse the pose data, outputting the coordinates of the radiation source and transmitting them through a communication module; and a power supply chamber to power the system. This invention effectively integrates a quadruped robot with a radio monitoring system, improving the accuracy of transient signal capture and monitoring response efficiency. It can meet the needs of radio management departments for large-scale deployment and routine inspections in complex scenarios, contributing to the maintenance of electromagnetic space safety and order.

The application discloses an energy efficiency adaptive regulation device and method for a spectrum detection device, and relates to the technical field of radio spectrum monitoring. The device comprises a dynamic sleep-wake module, an energy efficiency adaptive regulation module, a task priority management module and a feedback optimization module. When on standby, low-rate sampling of environmental radio frequency signals is performed, and intensity, bandwidth and periodicity characteristics are extracted. Based on target characteristic determination, noise and missed detection are dynamically adjusted to trigger wake-up. When working, gain, sampling rate, quantization accuracy and algorithm complexity are jointly adjusted according to task requirements and energy efficiency constraints, and performance trade-off instructions are generated according to task priority when resources are limited. When idle, detection, power consumption and performance data are collected, and wake-up threshold and operating parameters are updated for the next period through reinforcement learning or regression analysis. Through the device and method, invalid wake-up can be inhibited, the risk of missed detection can be reduced, and adaptive balance of energy efficiency and task performance can be achieved.

The present invention relates to an antenna device comprising a low band (LB) cavity slot antenna and a high band (HB) cavity slot antenna on a single dielectric substrate. The LB cavity slot antenna comprises a LB resonant cavity and one or more 3D slots extending through a top surface and side surfaces of the LB resonant cavity. The HB cavity slot antenna comprises a HB resonant cavity and one or more 2D slots extending through a top surface of the HB resonant cavity. Each of the LB cavity slot antenna and the HB cavity slot antenna can be fed by one or more feed elements. The antenna device can efficiently operate in both the HB and the LB of the radio spectrum. Due to the one or more 3D slots, the size of the LB cavity slot antenna can be reduced (without affecting the performance of the antenna device), such that the antenna device can be integrated in a housing of a handheld electronic device.

The present application belongs to the technical field of radio spectrum monitoring, and provides a wide-frequency unmanned aerial vehicle spectrum detection and rapid identification method based on deep learning, which comprises the following steps: collecting wide-frequency radio signals to form a composite signal sequence and converting the composite signal sequence into a time-frequency feature map, performing cepstrum transformation on the original signal to extract a cepstrum time delay peak, constructing an environment compensation parameter vector, identifying frequency selective fading notches in the time-frequency feature map, dividing to form a spectrum fragment set, constructing a fragment association model in combination with a frequency domain spacing and an inverse frequency position matching rule, and completing physical constraints, inputting the spectrum fragment spatial features and the environment compensation parameter vector into the model, calculating a coherence weight through a deep fragment association model based on a Transform architecture, completing logical reorganization of the spectrum fragments in a vector space and generating a logical reorganization vector, inputting the vector into a double-branch parallel classification network, and realizing accurate identification of unmanned aerial vehicle target models and communication protocols.

Methods, systems, and devices for wireless communication are described. A wireless communication device may receive a control message via a first radio spectrum band, the control message comprising an uplink grant for transmitting one or more backscatter uplink messages via a second radio spectrum band, the second radio frequency spectrum band is higher in frequency than the first radio frequency spectrum band and is separated from the first radio frequency spectrum band by at least a threshold frequency gap. The wireless communication device may then receive the continuous waveform via the second radio spectrum band and modulate the continuous waveform with uplink data of the one or more backscatter uplink messages. The wireless device may then transmit a backscatter signal of the continuous waveform via a second radio spectrum band based on modulating the continuous waveform with the uplink data.

This invention discloses a method and system for radio signal monitoring and positioning based on big data, belonging to the field of radio spectrum monitoring technology. This invention constructs an integrated air-space-ground multi-source monitoring network, processes massive signal data in a layered manner through edge-cloud collaboration, corrects non-line-of-sight and multipath effects using a spatiotemporal knowledge graph, and combines a fully automatically updated dynamic fingerprint database to achieve TDOA-AOA-RSSI-fingerprint multi-source fusion positioning, complemented by closed-loop incremental learning and full-process data security protection. This invention effectively eliminates monitoring blind spots, significantly improves positioning accuracy and signal processing efficiency in complex environments, reduces fingerprint database maintenance costs, achieves an abnormal signal identification accuracy of ≥98.5%, and provides outdoor positioning accuracy of 8.2m and indoor accuracy of 2.6m. It is applicable to scenarios such as spectrum management, illegal signal crackdown, and civil aviation frequency band protection, demonstrating significant engineering practical value.

This invention discloses a spectrum background noise prediction method based on multi-scale attention mechanisms and domain adaptation, aiming to provide a method capable of accurately predicting background noise in complex spectrum environments. Based on an improved convolutional neural network, it integrates multi-scale attention mechanisms and domain adaptation technology. Through four core steps—data preprocessing, model building, model training, prediction, and post-processing—and through innovative model structure and optimized training strategies, it achieves accurate prediction of background noise in complex spectrum environments. The advantages of this invention are: strong adaptability across all frequency bands, good multi-site versatility, outstanding robustness, and a smooth and natural prediction curve. It is suitable for scenarios such as radio spectrum monitoring, signal detection, signal-to-noise ratio calculation, and spectrum situational awareness, and can effectively adapt to complex and ever-changing spectrum environments and multi-site monitoring needs.

Disclosed are methods for allocating spectrum by a Mobile Satellite Service (MSS) satellite to a User Equipment (UE). The method includes monitoring radio spectrum designated as guard bands of a terrestrial Broadband Wireless Access (BWA) network to generate a spectrum usage map of guard band frequencies of the guard bands. The spectrum usage map includes available guard band frequency bands including unlicensed frequency bands. The method includes selecting a guard band or a sub-band within the available guard band frequency bands for uplink communication based on the spectrum usage map, and receiving, by the MSS satellite from the UE, uplink signals on the guard band or the sub-band that was selected. Related wireless electronic devices and systems are also discussed.

A computer-implemented method providing a neural network for identifying radio technologies employed in an environment. The neural network includes an autoencoder having an encoder, and a classifier. The method has the steps of sensing a radio spectrum of the environment thereby obtaining a set of data samples, labelling a subset of the data samples by a respective radio technology thereby obtaining labelled data samples, training the autoencoder in an unsupervised way by unlabelled data samples, training the classifier in a supervised way by the labelled data samples, and providing the neural network by coupling the output of an encoder network of the autoencoder to an input of the classifier.

The present application relates to the field of network security, in particular to a wireless spectrum anomaly monitoring device based on CNN and block chain, the system includes five modules of spectrum sensing, feature processing, anomaly detection, lightweight inference and block chain storage, first, the spectrum sensing module collects multi-band signals to generate an energy density matrix; then, the feature processing module applies adaptive wavelet transform to reduce noise and construct a two-dimensional feature topology graph. The anomaly detection module uses dynamic convolution kernel and frequency domain attention mechanism to deeply extract features and generate an anomaly probability distribution graph; the lightweight inference module integrates expert knowledge and deep learning results to accurately determine the attack type and confidence; finally, the block chain storage module records the determination results, ensures data security through distributed consensus verification, and stores and updates parameters. This device uses multi-modal perception, combines network traffic and spectrum features, and improves attack detection difficulty.

Provided are a method for sharing a radio spectrum with high-orbit communication satellites, which comprise a geosynchronous satellite and operate in a near-equatorial orbit, on the basis of a beam co

Provided is a process, including receiving, from a spectrum access system (SAS), an authorization to transmit on a radio spectrum band, wherein the radio spectrum band comprises frequencies between 3550 MHz and 3700 MHz, does not comprise frequencies lower than 3550 MHz, and does not comprise frequencies greater than 3700 MHz; and based on the authorization, instructing a plurality of radio access network (RAN) nodes to transmit communications on the radio spectrum band.

The invention relates to the technical field of radio frequency spectrum monitoring, in particular to an ultra-wideband unmanned aerial vehicle spectrum detection system and method based on sectional compressed sensing, and the system comprises a radio frequency front-end module, a parallel compressed sensing processing array, a multi-channel synchronous data acquisition module and a data processing and spectrum reconstruction module which are connected in sequence; when a signal is monitored, firstly, an ultra-wideband radio frequency signal is divided into N independent sub-frequency bands in an analog domain through a band-pass filter bank; sending the N sub-band signals into N parallel modulation broadband converters, and carrying out independent compressed sensing processing and sub-Nyquist rate synchronous sampling; and finally, parallel spectrum reconstruction is carried out on the data of the N sub-bands by using parallel computing resources such as a GPU on a data processing platform, and results are fused into a complete broadband spectrum. According to the invention, the dynamic range and the anti-interference capability of the system are improved through the strategy of'division and treatment ', and meanwhile, the performance requirement on a core radio frequency device is reduced.

This disclosure describes methods, systems, and devices for wireless communications in which a transmitting device using a shared radio frequency spectrum can perform a listen-before-talk (LBT) procedure for at least a minimum defer time and, after successfully completing the LBT procedure, can occupy the spectrum for a duration of a channel occupancy time (COT). The transmitting device can interrupt transmission for a gap period during the COT and resume transmission after the gap period without performing another LBT procedure. The minimum defer time can be based on a periodicity of channel reservation signals transmitted by one or more other devices using the shared radio frequency spectrum. In some cases, the transmitting device can schedule one or more receiving devices with resources for transmission during the COT, and the receiving devices can perform LBT that is not adjacent to associated transmissions.

This invention discloses a low-altitude UAV threat classification response and countermeasure management system based on big data, belonging to the field of UAV countermeasure technology. The system includes a multi-source perception data acquisition module, a big data preprocessing and fusion module, a threat classification assessment module, a classification response decision-making module, a countermeasure execution control module, and a full-process closed-loop management module. This invention comprehensively utilizes multi-source perception data such as radar, photoelectric, and radio spectrum, achieving accurate detection of UAV targets through multi-source data fusion technology; employs a threat classification algorithm based on dynamic weight adjustment for scientific target assessment; utilizes an improved genetic algorithm for intelligent scheduling of countermeasure resources; and achieves continuous self-learning and optimization of the system through full-process closed-loop management. This invention effectively solves the problems of low detection accuracy, high false alarm rate, rigid response strategies, and poor coordination capabilities of existing systems, and is suitable for low-altitude security protection of important targets such as airports, nuclear power plants, and government agencies.

The application discloses a kind of unmanned aerial vehicle target signal tracking monitoring method and system, belong to radio frequency spectrum sensing and passive direction finding positioning technical field, including the following contents: through the synchronous acquisition of multiple element antenna array in environment radar, communication equipment radiated radio frequency signal, pre-processing obtains multiple channel intermediate frequency digital sampling signal;Capture effective radiation signal and output signal frequency domain parameter;Multi-channel angle measurement data are weighted and fused to obtain accurate DOA data;Based on multi-channel DOA direction finding line, cross solution is carried out, and target original latitude and longitude point sequence is generated;Candidate effective point set is obtained using FCM fuzzy clustering;In candidate effective point set, based on Mahalanobis distance wave door, the pairing processing of point and existing track is completed;Smooth continuous target time sequence track is output after filtering iteration;Data storage and track visualization plotting are completed.The application uses the above method, guarantees the continuity and stability of moving target track tracking under passive monitoring scene.

Provided is a process, including receiving, from a spectrum access system (SAS), an authorization to transmit on a radio spectrum band, wherein the radio spectrum band comprises frequencies between 3550 MHz and 3700 MHz, does not comprise frequencies lower than 3550 MHz, and does not comprise frequencies greater than 3700 MHz; and based on the authorization, instructing a plurality of radio access network (RAN) nodes to transmit communications on the radio spectrum band.

A spectrum forecasting system, including a spectrum receiver to monitor a radio spectrum and collect live signal data; a spectrum forecasting deep learning model communicatively coupled to the spectrum receiver to receive the live signal data as input data, infer future vacancies, and chart a path through the future vacancies for assignment. A spectrum forecasting method, including receiving live signal data from a radio spectrum; inputting the live signal data into a spectrum forecasting deep learning model; inferring, using the spectrum forecasting deep learning model, future vacancies; and charting, using the spectrum forecasting deep learning model, a path through the future vacancies for assignment.