195results about "Modulation type identification" patented technology

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

The invention relates to an automatic modulation identification method, device and equipment based on a lightweight neural network, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a multi-stream lightweight global-local collaborative network, firstly adopting a multi-stream input architecture in the network, and performing fusion processing by taking parallel joint I / Q streams, independent I streams and independent Q streams as inputs, thereby effectively reducing feature preprocessing redundancy; secondly, a reverse residual module is introduced, parameter quantity is greatly reduced, and feature extraction efficiency is improved; and finally, developing a lightweight global-local collaboration module, and carrying out modulation type identification and classification by utilizing a classification module after local feature extraction and global relation modeling are deeply fused. According to the method, higher modulation type recognition accuracy can be achieved with lower calculation complexity, and higher robustness is shown in a low signal-to-noise ratio environment.

The invention provides a semi-supervised signal modulation identification method based on multi-codebook discrete virtual adversarial training, and the method comprises the steps: carrying out the high-dimensional potential representation coding of input labeled and unlabeled sample signals through employing a vector quantization generative adversarial network, so as to obtain a continuous potential representation; mapping the continuous potential representation into a discrete vector sequence through a quantizer; virtual confrontation disturbance meeting the condition that the L2 norm does not exceed epsilon is applied to the unlabeled sample at the input end, and a disturbance sample is generated; respectively inputting a labeled sample, a label-free sample and a disturbance sample into a classifier, calculating cross entropy loss and KL divergence consistency loss, and jointly optimizing parameters of an encoder, a codebook and the classifier in a weighted sum form; therefore, the discrete potential representation is obtained through the vector quantization generative adversarial network, disturbance is constructed for the unlabeled sample at the input end, and joint optimization is performed in combination with the cross entropy and the KL divergence loss, so that the recognition accuracy and robustness under the conditions of few labels and low signal-to-noise ratio are improved.

The invention discloses a complex electromagnetic signal demodulation method based on multi-task learning, and relates to the technical field of electromagnetic signal demodulation, and the method comprises the steps: employing a ResNet-18 and Transform mixed architecture as a backbone network, combining a dynamic weight distribution mechanism, and constructing a multi-task learning model; a complex electromagnetic signal to be detected is input into the multi-task learning model, a modulation type recognition result and a code element sequence prediction result are obtained, collaborative optimization of the two tasks is achieved, and the negative migration effect caused by task conflicts is effectively restrained through a dynamic weight distribution mechanism. In an electromagnetic signal demodulation process, a modulation mode adopted by a signal is firstly judged, then an original code element sequence is predicted, modulation type identification is the basis of code element sequence prediction, and the accuracy of the modulation type identification directly affects a final result of code element sequence prediction. The multi-task learning model improves the accuracy of modulation type identification through collaborative optimization of two tasks, and improves the accuracy of code element sequence generation at the same time.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an indication of one or more waveforms supported by a base station, wherein the one or more waveforms include at least one of an orthogonal frequency division multiplexing (OFDM) waveform, a single carrier frequency domain (SC-FD) waveform, or a single carrier time domain (SC-TD) waveform. The UE may determine whether the UE is capable of communicating with the base station based at least in part on the indication. The UE may selectively communicate with the base station using at least one waveform of the one or more waveforms based at least in part on the determination. Numerous other aspects are provided.

The invention discloses an underwater acoustic signal modulation mode identification method and a related device, and relates to the field of signal modulation mode identification in non-cooperative underwater acoustic communication, and the method comprises the steps: firstly obtaining an original passband signal, and processing the original passband signal to obtain an I-path complex signal and a Q-path complex signal; extracting spatial features of the I-path complex signal and the Q-path complex signal by using a residual convolutional network module to obtain a spatial feature sequence; then, a bidirectional gating circulation unit module is used for learning information of the spatial feature sequence from the front direction and the back direction respectively, and the information obtained through learning is spliced to obtain a time sequence feature vector; and performing feature mapping and classification on the time sequence feature vectors to obtain classification confidence coefficients of different modulation modes, and determining the modulation mode corresponding to the maximum classification confidence coefficient as a classification result. According to the invention, high-precision and automatic underwater acoustic signal modulation mode identification can be realized.

A multi-representation domain feature fusion signal modulation identification method based on mask reconstruction driving comprises the following steps: 1) in a pre-training stage, respectively applying random mask operation to an I channel, a Q channel, an amplitude component and a phase component generated by decomposing an input signal; 2) then inputting into an encoder for encoding processing, extracting a feature vector through the encoder, dynamically integrating feature information through a gating fusion module guided by residual connection, and reconstructing a masked area by using a reconstruction head to realize self-supervised learning so as to obtain robust general representation; 3) in a fine tuning stage, inputting an unmasked downstream task data set, loading the weight obtained in the pre-training stage, merging signals of four representation domains (in-phase component, orthogonal component, amplitude and phase), inputting the merged signals into an encoder, and dynamically integrating cross-representation domain feature information through a gating fusion module guided by residual connection; and 4) finally inputting the fusion features into a classification head, and outputting a modulation category identification result. According to the method, the training cost and the over-fitting risk when the model migrates to the specific identification task are greatly reduced, effective unification of high precision, strong generalization and low cost is also realized, and the practicability and deployment efficiency of the method are remarkably enhanced.

The invention discloses a small sample automatic modulation identification method and system based on a multi-stage regularization Y-shaped frame in the technical field of wireless communication signal processing. The method comprises the following steps: carrying out Gramer angle field transformation on a received I / Q signal sequence to obtain a two-dimensional GAF image set; performing data enhancement on the two-dimensional GAF image set, dividing the enhanced GAF images with known modulation category labels in the enhanced GAF image set into a support set, and dividing all the remaining enhanced GAF images with unknown modulation category labels into a query set; performing feature extraction on the support set and the query set by using a trained feature extraction model based on a channel space attention relation network CSARN to obtain a to-be-identified feature vector of the support set and a to-be-identified feature vector of the query set, and performing modulation mode classification by using a nearest neighbor category mean classifier to obtain a to-be-identified feature vector of the support set and a to-be-identified feature vector of the query set; and obtaining modulation identification results of the enhanced GAF images of all the unknown modulation category labels.

The invention provides a target identification method and system. The method is applied to target identification equipment and a marking device. Target identification equipment controls a signal transmitting and receiving device to transmit an identification signal to a target detection range; after the marking device in the target detection range receives the identification signal, generating a modulation signal corresponding to the identity code of the target object, and adding the modulation signal into the identification signal to obtain a response signal; and the marking device sends the response signal to the signal transceiving device of the target identification equipment, and the target identification equipment decodes the response signal to obtain the identity information of the target object. By applying the method provided by the embodiment of the invention, the identity recognition process of the target object can be simplified, the combination mode of the target recognition device and the marking device is simple, excessive devices and structures do not need to be arranged, operation and application are convenient, and meanwhile, the cost of the device is also saved.

The invention discloses a LoRa signal demodulation and decoding method and system based on blind identification, and the method comprises the steps: carrying out the down-conversion processing of a LoRa signal, and generating a baseband signal corresponding to the LoRa signal; identifying linear sweep frequency characteristics of the baseband signal based on short-time Fourier transform, and judging whether the baseband signal is a CSS modulation signal according to the linear sweep frequency characteristics; if the signal is a CSS modulation signal, detecting the signal bandwidth based on spectrum estimation, and identifying a spreading factor by adopting a peak detection and zero-cross detection method; and configuring parameters of a preset LoRa demodulation and decoding module in real time according to the signal bandwidth and the spreading factor, and demodulating and decoding the LoRa signal according to the configured LoRa demodulation and decoding module to obtain LoRa communication signal content. Through development of a deep learning-based adaptive detection algorithm, blind identification and parameter estimation of LoRa signals in a complex electromagnetic environment are realized, and then robust demodulation and decoding are completed.

The invention relates to the technical field of unmanned aerial vehicle remote identification signal detection equipment, and discloses an unmanned aerial vehicle RID signal heaven and earth end complementary anti-signal collision decoding method, which comprises the following steps: an unmanned aerial vehicle simultaneously broadcasts RID signals at a certain moment in a certain area on the ground; receiving and demodulating the broadcasted RID signal through a ground receiver in the area, and transmitting the data back to a ground data center; receiving an RID signal broadcasted by the unmanned aerial vehicle by using an air receiver, and transmitting a mixed signal back to a ground data center; performing reconstruction processing on the received data to obtain a reconstructed RID signal; subtracting the reconstructed RID signal from the mixed signal to obtain a residual signal; and performing demodulation processing on the residual signal. The invention also discloses a system for realizing the space and ground end complementary anti-signal collision decoding method for the RID signal of the unmanned aerial vehicle. According to the invention, stable and reliable operation of the receiving decoding system is ensured.

A method implemented by a Wireless Transmit / Receive Unit (WTRU) includes receiving a DeModulation Interference Measurement (DM-IM) resource, determining an interference measurement based on the DM-IM resource, and demodulating a received signal based on the interference measurement. An interference is suppressed based on the interference measurement. At least one DM-IM resource is located in a Physical Resource Block (PRB). The DM-IM resource is located in a PRB allocated for the WTRU. The DM-IM resource is a plurality of DM-IM resources which form a DM-IM pattern, and the DM-IM pattern is located on a Physical Downlink Shared Channel (PDSCH) and / or an enhanced Physical Downlink Shared Channel (E-PDSCH) of at least one Long Term Evolution (LTE) subframe. The DM-IM resources are different for different Physical Resource Blocks (PRB) in the LTE subframe. The DM-IM is located in a Long Term Evolution (LTE) Resource Block (RB), and the DM-IM pattern is adjusted.

The application discloses a modulation format and optical signal-to-noise ratio joint monitoring method based on an amplitude analytic complex plane, and relates to the field of communication, and comprises the following steps: performing constant modulus equalization processing on a signal to be identified to obtain a preprocessed digital signal; generating an amplitude analytic complex plane based on the preprocessed digital signal; and identifying the modulation format and the optical signal-to-noise ratio of the amplitude analytic complex plane through a trained multi-target neural network.The application can simultaneously perform modulation format identification and optical signal-to-noise ratio monitoring through the multi-target neural network, share bottom layer feature extraction, avoid redundant calculation, reduce the complexity of the scheme, and improve the joint identification efficiency.Through verification, the method can achieve 100% correct identification rate of five kinds of modulation formats when the optical signal-to-noise ratio is not lower than the threshold corresponding to 20% FEC, and the maximum error of optical signal-to-noise ratio monitoring of the five kinds of modulation formats is controlled within 1 dB.

A communication method and apparatus are provided, are applied to the field of communication technologies, may be applied to a wireless local area network system supporting 802.11 series protocols such as a next-generation Wi-Fi protocol of IEEE 802.11ax like 802.11be, Wi-Fi 7, or EHT, and 802.11bn, Wi-Fi 8, UHR, Wi-Fi AI, or millimeter wave (MMW), and may be further applied to a WPAN system, a sensing system, and the like supporting UWB. A first communication apparatus generates a PPDU, and sends the PPDU. A second communication apparatus receives the PPDU, and parses the PPDU. The PPDU includes first indication information. The first indication information may indicate a first modulation scheme set and a coding rate. The first modulation scheme set is one of N modulation scheme sets obtained through classification based on W modulation schemes. Q modulation schemes in the first modulation scheme set are used to modulate the PPDU.

In a radio system which allocates resources using as units resource blocks which are formed by frequency components and time components, control information for mobile station devices, and identification information which is used to identify a format for a control information transmission channel which transmits the control information is transmitted from the base station device to the mobile station devices by means of the control information transmission channel.

This application discloses a method and related apparatus for identifying underwater acoustic signal modulation patterns, relating to the field of signal modulation pattern identification in non-cooperative underwater acoustic communication. The method first acquires the original passband signal and processes it to obtain two complex signals, I and Q. Using a residual convolutional network module, the spatial features of the I and Q complex signals are extracted to obtain a spatial feature sequence. Then, using a bidirectional gated recurrent unit module, information from the spatial feature sequence is learned from both forward and backward directions, and the learned information is concatenated to obtain a temporal feature vector. The temporal feature vector is then subjected to feature mapping and classification to obtain classification confidence scores for different modulation patterns. The modulation pattern corresponding to the highest classification confidence score is determined as the classification result. This application enables high-precision, automated identification of underwater acoustic signal modulation patterns.

The present application relates to the technical field of signal modulation and coding joint identification, and particularly relates to a modulation and coding joint identification method and system based on a multi-channel attention network, which encodes a modulation information sequence by simulating a communication environment with a randomly generated radio information sequence, generates a digital modulation signal and an analog modulation signal, and assembles signal sample data; constructs a multi-channel convolutional gated deep attention network model for modulation and coding joint identification, and trains and optimizes the multi-channel convolutional gated deep attention network model using the signal sample data; inputs a signal to be identified into the trained and optimized multi-channel convolutional gated deep attention network model, and obtains the modulation and coding category of the signal to be identified using the trained and optimized multi-channel convolutional gated deep attention network model. The present application can improve the efficiency of modulation and coding joint identification, can realize the task of signal modulation and coding joint automatic identification, and is convenient for deployment and implementation.

The application discloses a method for automatically identifying radio signal modulation under restricted conditions, comprising the following steps: S1, collecting radio I / Q signals of different modulation types as original feature data sets, and converting each I / Q sample signal into an A / P signal through feature transformation, all A / P signals forming a transformed feature data set; wherein, the I / Q and A / P features of the same sample signal have the same label. The method focuses on two practical application scenarios of radio signal sample restriction and computing resource restriction.

Disclosed are systems and techniques for wireless communications. For instance, a network entity may obtain a first plurality of bits from a first buffer, wherein a start position for obtaining the first plurality of bits from the first buffer is based on a redundancy version order. In some cases, the network entity may determine, based on a modulation order and a probability distribution associated with the first plurality of bits, at least one scaling factor for constellation mapping the first plurality of bits. In some examples, the network entity may map, based on the at least one scaling factor, the first plurality of bits to one or more symbols.

Methods, systems, and devices for wireless communication are described. A wireless device may generate a set of shaping bits based on performing a constellation shaping operation on a set of information bits. Further, the wireless device may perform a first error correction encoding operation based on the set of shaping bits and a second error correction encoding operation based on the set of information bits to generate a first set of coded bits and a second set of coded bits, respectively. Moreover, the wireless device may transmit a control message that indicates an offset that identifies a first resource mapping for the first set of coded bits and a second resource mapping for the second set of coded bits and transmit the first set of coded bits in accordance with the first resource mapping and the second set of coded bits in accordance with the second resource mapping.

The invention discloses a method for automatically modulating and identifying a black box migration attack based on a multi-boundary crossing strategy. The method comprises the following steps of: 1, selecting an agent model for replacing black box attack, and initializing the agent model and a weight; 2, outputting and obtaining an original label based on the agent model and the weight integration; 3, constructing a negative class set based on the original label, and calculating a score difference and a gradient difference thereof; step 4, carrying out gradient adaptive scaling on the gradient difference; 5, selecting a nearest category based on gradient adaptive scaling, calculating and superposing disturbance, and obtaining a temporary update sample; step 6, carrying out concealment correction on the temporarily updated sample; step 7, executing a multi-boundary crossing strategy, stopping iteration if the maximum number of iterations is reached, and otherwise, repeating the steps 2-6; and finally, outputting the iterated adversarial sample. According to the method, the attack success rate, the concealment and the signal fidelity are improved at the same time in a black box migration scene, and the parameter robustness and the convergence stability of the algorithm are enhanced.

Methods, systems, and devices for wireless communications are described. In some examples, a wireless communication device (such as a network entity) may output a downlink control channel message using at least a first resource element (RE) of a first control resource set (CORESET) and using at least a second RE of a second CORESET. The first CORESET and the second CORESET may be linked and may be associated with different transmission configuration indicator (TCI) states. Additionally, each of the first RE and the second RE may be associated with a same payload of the downlink control channel message, and modulation of a first set of symbols mapped to the first RE may be linked to modulation of a second set of symbols mapped to the second RE based on the first CORESET and the second CORESET being linked.

The invention provides a modulation pattern identification method and device for an orthogonal frequency division multiplexing signal based on adaptive modulation, which can be applied to the technical field of wireless communication. The method comprises the following steps: acquiring an orthogonal frequency division multiplexing signal, wherein the orthogonal frequency division multiplexing signal comprises N symbols and K subcarriers; performing fast Fourier transform on the orthogonal frequency division multiplexing signal to obtain a frequency domain matrix of an NxK dimension; extracting a plurality of frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix according to the identifiers of the K subcarriers based on the subcarrier dimensions to obtain K target data sequences; according to any target data sequence in the K target data sequences, performing at least one operation of clustering or accumulation on the target data sequences to obtain a plurality of modulation pattern recognition features associated with the target data sequences; and determining a target modulation pattern according to the plurality of modulation pattern recognition features and a preset modulation pattern mapping relationship.

A system includes one or more data processors. The system further includes a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations. The operations include receiving a stream of in-phase components and quadrature phase components as IQ data. The IQ data indicates which subcarriers each of the IQ data is to be sent. The operations further include determining a maximum absolute value associated with the IQ data, determining a constellation measure of a largest bounding box covering all subcarriers of the IQ data, and compressing the IQ data to obtain compressed IQ data based on the maximum absolute value and the constellation measure.

This disclosure provides a method and apparatus for modulation pattern recognition of orthogonal frequency division multiplexing (OFDM) signals based on adaptive modulation, which can be applied to the field of wireless communication technology. The method includes: acquiring an OFDM signal, which comprises N symbols and K subcarriers; performing a fast Fourier transform on the OFDM signal to obtain an NxK-dimensional frequency domain matrix; based on the subcarrier dimension and according to the identifiers of the K subcarriers, extracting multiple frequency domain symbol vectors associated with each subcarrier from the frequency domain matrix to obtain K target data sequences; performing at least one operation, such as clustering or accumulation, on any of the K target data sequences to obtain multiple modulation pattern recognition features associated with the target data sequences; and determining the target modulation pattern based on the multiple modulation pattern recognition features and a preset modulation pattern mapping relationship.

This application discloses wireless devices, multi-scan PHY blocks thereof, and associated methods. A multi-scan PHY block might include a first number of front-end blocks and a second number of back-end blocks. The multi-scan PHY block might enable detection by the front-end blocks of packets on a plurality of wireless channels and demodulation by the back-end blocks of the packets on the plurality of wireless channels without requiring a separate back-end block for each wireless channel.

This invention discloses a method and system for counter-reconnaissance of wireless communication channels based on known structure and desired transformation-enhanced perturbations, belonging to the field of wireless communication technology. Addressing the problems of traditional jamming methods being easily exposed and inefficient, and the insufficient robustness of existing counter-perturbation channels, this invention employs a desired transformation enhancement framework to generate low-power counter-perturbations with channel pre-adaptability. Dual-channel coordinated transmission ensures accurate signal superposition, and an air interface closed-loop adaptive mechanism provides real-time feedback of channel parameters and dynamically adjusts the perturbation. Simultaneously, the receiving end recovers the original signal based on the known perturbation structure through equalization and scrambling operations. This effectively misleads the enemy's intelligent identification system at low power while ensuring the reliability of friendly communication.

This application provides a method and apparatus for detecting channel modulation schemes. The method includes: hard demodulating the modulation data to be detected according to each modulation scheme in a candidate modulation scheme group to obtain demodulated bit sequences corresponding to each modulation scheme, forming a first demodulated sequence set; modulating the sequences in the first demodulated sequence set according to corresponding modulation schemes to obtain a modulation sequence set; obtaining error feature values ​​corresponding to each modulation scheme based on the modulation data to be detected, the modulation sequence set, and the feature value correction factors corresponding to each modulation scheme; and selecting the modulation scheme corresponding to the smallest error feature value as the modulation scheme of the modulation data to be detected. The technical solution of this application embodiment can improve the efficiency of the channel modulation scheme detection process.

The invention discloses a high-speed frequency hopping signal tracking interference method based on a receiving and transmitting integrated platform, and belongs to the technical field of communication countermeasure. Comprising the following steps: acquiring a broadband radio frequency signal, moving the acquired signal to a zero intermediate frequency state to form baseband data in an effective bandwidth, and performing sampling rate reduction processing; carrying out real-time fast Fourier transform on the baseband data after the downsampling rate processing to obtain transformed spectrum data; analyzing a frequency hopping signal in the converted frequency spectrum data to obtain a frequency hopping rate and a frequency hopping pattern of the frequency hopping signal, and respectively presenting the frequency hopping rate and the frequency hopping pattern as a frequency modulation period and a frequency hopping frequency table; inherent signals in the electromagnetic space and interference signals released by the platform are eliminated in a frame-by-frame difference comparison mode, and a channel number corresponding to a frequency hopping frequency is obtained by comparing a frequency hopping frequency table; and a noise modulation signal is generated based on the frequency channel number, digital-to-analog conversion is realized through a digital-to-analog converter, interference signal output is formed, and tracking interference output of the signal is completed.

The invention provides a non-cooperative measurement and control signal parameter identification and analysis method and device and related equipment, and the method comprises the steps: carrying out the modulation identification and demodulation of a non-cooperative measurement and control signal, and obtaining a demodulation bit stream; decoding the demodulated bit stream to obtain a decoded bit stream; the decoding bit stream is analyzed according to a frame structure of a CCSDS specification, a plurality of transmission frame identifiers are obtained, and each transmission frame identifier at least comprises a spacecraft identifier and a virtual channel identifier; and obtaining the APID data in the decoding bit stream based on each transmission frame identifier and a packet header structure of the CCSDS specification. By applying the method provided by the invention, the non-cooperative measurement and control signal can be automatically debugged and analyzed, and the operation and maintenance cost is reduced.