318results about "Modulation type identification" patented technology

The invention discloses a satellite signal modulation recognition method based on an enhanced multi-scale feature attention network, and the method comprises the steps: obtaining a to-be-recognized satellite signal, inputting the to-be-recognized satellite signal into a trained enhanced multi-scale feature attention network, and obtaining a satellite signal modulation recognition result. The training process of the enhanced multi-scale feature attention network comprises the following steps: acquiring a satellite communication data set, and performing sample expansion to obtain an augmented sample; performing noise removal processing and feature extraction on the augmented sample to obtain denoised data features; inputting the de-noised data features into a global context sensing module, extracting modulation features of different frequencies and time scales, and performing feature fusion on the modulation features and the de-noised data features to obtain fusion features; and performing feature reconstruction and channel recovery on the fusion features, inputting the fusion features into a Softmax classifier, performing classification prediction, calculating classification loss, and optimizing network parameters through the classification loss. Under a complete blind recognition scene, the accuracy of a satellite signal modulation recognition result can be obviously improved.

The invention discloses a semi-supervised learning method based on a self-adaptive threshold value, belongs to the field of radio communication, and aims to extract general semantic features of signals through comparative learning so as to improve generalization of downstream modulation identification tasks. A self-adaptive threshold mechanism is introduced to generate a high-quality pseudo tag, and confirmation deviation is reduced through additional category information, so that the robustness of the model is improved; besides, a hierarchical encoder capable of learning Fourier filtering is designed to capture multi-scale semantic features of large-scale long-sequence signals, improve the calculation efficiency of a model and research and verify an algorithm based on a plurality of large-scale data sets, so that the accuracy of automatic modulation recognition is remarkably improved, and particularly, under the condition of less label data, the accuracy of automatic modulation recognition is greatly improved. Compared with a mainstream method, the method has higher generalization ability and robustness, and can effectively cope with signal diversity and noise interference in a real communication environment.

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 a signal-to-noise ratio adaptive signal modulation identification method. The method comprises the following steps: inputting IQ information and a signal-to-noise ratio (SNR) estimated value of a signal into a signal modulation identification model, and identifying to obtain a modulation mode of the signal; the signal modulation identification model is an MCLDNN of which the feature fusion module is provided with an SNR attention module; the SNR attention module can receive the SNR estimated value and obtain a signal-to-noise ratio attention weight according to the SNR estimated value; receiving a signal multi-channel fusion feature map output by the multi-channel fusion convolutional layer of the MCLDNN; weighting the signal multi-channel fusion feature map by using a signal-to-noise ratio attention weight, connecting the weighted signal multi-channel fusion feature map with a signal multi-channel fusion feature map residual error, and outputting a multi-channel fusion feature combined with SNR attention; and the multi-channel fusion features combined with the SNR attention are input to a time sequence module of the MCLDNN.

The invention relates to the technical field of dual-mode interphones, and discloses an AI-based dual-mode interphone satellite communication noise reduction method and system, and the method comprises the steps: carrying out the orbit equation solving of satellite ephemeris data, and obtaining a satellite elevation angle and a channel fading parameter; receiving a first voice signal through a dual-mode interphone, and identifying a modulation mode according to the first voice signal; calculating a first noise reduction mask according to the channel fading parameter and the modulation mode; compressing the standard noise reduction neural network to form a lightweight noise reduction model; and starting an FM spectrum subtraction branch or a TETRA filtering branch in the lightweight noise reduction model according to the modulation mode, and performing frequency domain processing on the first noise reduction mask and the first voice signal to obtain a second voice signal, thereby providing stable and reliable real-time voice noise reduction capability for the portable dual-mode interphone in a dynamic satellite environment.

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 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 discloses a sub-Nyquist joint broadband spectrum sensing and automatic modulation classification method based on deep learning. The method comprises the following steps: generating a generation data set for joint broadband spectrum sensing and automatic modulation classification; the method comprises the following steps: modeling a cognitive wireless network combined broadband spectrum sensing and automatic modulation classification system, and carrying out multi-coset preprocessing and analog low-pass filtering on a signal received by a cognitive wireless end; constructing a broadband spectrum sensing and automatic modulation classification neural network and training a model; and the local client combines the off-line broadband spectrum sensing training model and the off-line automatic modulation classification training model to online predict the spectrum occupation condition of the broadband signal and the modulation type of each occupied sub-band signal. According to the method, organic fusion of spectrum detection and modulation identification is realized, the processing efficiency and the intelligent degree of the system are remarkably improved, and the method has good robustness and high accuracy and is particularly suitable for a joint identification task of a multi-user broadband communication signal in a dynamic electromagnetic environment.

Techniques are described that provide for waveform selection for uplink (UL) and / or downlink (DL) wireless transmissions based on one or more factors associated with the wireless transmission, a transmitter that is to transmit the wireless transmission, or combinations thereof. UL and DL transmissions may use one of a number of different waveforms, such as single-carrier waveforms that use a single carrier for information transmission of a wireless channel, and multi-carrier waveforms that use multiple carriers at different frequencies to transmit some of the bits on each channel. Multi-carrier transmission waveforms or single-carrier waveforms may be selected for wireless transmissions based on a bandwidth allocated for the transmissions, a capability of a transmitter that is to transmit the transmissions, or combinations thereof.

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 present invention relates to a three-channel automatic modulation recognition method. When a single-input single-output communication system receives an unknown signal, the time domain signal is discretely sampled in the time domain to form a time domain discrete signal, and one-dimensional complex data is formed. The Fourier time-frequency domain transform obtains frequency domain data. The frequency domain data is combined with the time domain discrete signal to obtain time-frequency domain discrete signal data. The frequency data in the time-frequency domain discrete signal data is fed into the two-dimensional convolution layer of the time-space frequency fusion network model. The in-phase data and orthogonal data in the time domain data are respectively fed into the corresponding one-dimensional convolution layer. The model determines the modulation format type of the unknown signal based on the characteristics of various modulation formats. In a positive signal-to-noise ratio environment, the present invention greatly improves the recognition accuracy of the modulation type of unknown electromagnetic spectrum signals, and the training speed is several times faster than that of existing models. For high-dimensional modulated signals, it can better solve the confusion problem between similar signals and achieve higher recognition accuracy.

The present application discloses an uplink data transmission method, apparatus, terminal and medium, which belongs to the field of communication technology. The uplink data transmission method of an embodiment of the present application includes: when a UE is configured to adopt a first waveform to transmit uplink data, the UE receives target downlink control information DCI from a network side device, and the target DCI is used to schedule target uplink data; when the indication information included in the target DCI meets a first preset condition, the UE adopts a second waveform to transmit the target uplink data; wherein the above-mentioned indication information is used to indicate: transmission parameters of the target uplink data.

The invention provides a blind identification and intelligent classification prediction method and system for a signal modulation mode, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a multi-scale time domain feature through wavelet packet decomposition, obtaining an instantaneous frequency feature through Hilbert transform, and constructing a feature correlation matrix; blind identification of a signal modulation mode is realized by using a dynamic routing algorithm and a self-correction mechanism; distinguishing discrete and continuous modulation based on the feature matrix and the signal-to-noise ratio; and signal modulation mode prediction is realized by adopting hybrid filtering and layered Markov decision. According to the invention, the recognition accuracy and prediction stability of the signal modulation mode 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.

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 present invention discloses a communication signal detection and recognition method based on improved YOLOv5. The method uses a lightweight improved YOLOv5, that is, a lightweight structure is used in the backbone network to reduce the amount of calculation, and the recognition ability is enhanced by combining a targeted scale feature fusion module and a feature attention module. The real-time recognition and extraction of features such as the existence, bandwidth, carrier frequency, duration and modulation mode of the communication signal can be achieved through the signal time-frequency diagram. The method proposes a signal detection and recognition scheme that can realize a series of functions such as signal acquisition, processing, training, real-time detection and recognition, and is easy to migrate and deploy as a whole. The present invention has high recognition performance in real-time detection and recognition of multiple parameters of communication signals, and has low difficulty in application and deployment in various terminal devices.

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.

The invention discloses a modulation format and optical signal-to-noise ratio joint monitoring method based on an amplitude analysis complex plane, which relates to the field of communication and comprises the following steps of: performing constant modulus equalization processing on a signal to be identified to obtain a preprocessed digital signal; generating an amplitude analysis complex plane based on the preprocessed digital signal; and performing modulation format and optical signal-to-noise ratio identification on the amplitude analysis complex plane through the trained multi-target neural network. According to the invention, through the multi-target neural network, modulation format identification and optical signal-to-noise ratio monitoring can be carried out at the same time, and bottom-layer feature extraction is shared, so that redundant calculation is avoided, the complexity of the scheme is reduced, and the joint identification efficiency is improved. Through verification, the method can realize 100% correct recognition rate of five modulation formats when the optical signal-to-noise ratio is not lower than a corresponding threshold value of 20% FEC, and the maximum monitoring error of the optical signal-to-noise ratio of the five modulation formats is controlled within 1dB.

The invention discloses an automatic modulation classification method for an OTFS system, and the method comprises the steps: embedding a differential pilot symbol in a time delay-Doppler domain, and dynamically adjusting the value of the pilot symbol according to a modulation mode; obtaining a time domain receiving signal; a time delay-Doppler domain receiving signal is obtained; carrying out virtual-real separation and splicing processing on the time domain receiving signal and the time delay-Doppler domain receiving signal, and constructing a dual-channel input data set; forming a complete data set based on the dual-channel input data set and the modulation type label data set; training the modulation classification model based on the double-flow convolutional neural network to obtain an automatic modulation classification neural network model; and forming a dual-channel input data set based on a signal to be modulated and classified, and inputting the dual-channel input data set into the automatic modulation classification neural network model to obtain a modulation classification result. Through multi-domain fusion and differentiation embedded pilot frequency assistance, high-accuracy modulation mode identification is realized, and powerful technical support is provided for a communication system in a high-speed mobile environment.

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.

There is disclosed a method of operating a transmitting radio node in a wireless communication network. The method includes transmitting data signaling utilising a plurality of transmission sources, wherein the data signaling represents a plurality of code blocks. Modulation symbols of a modulation symbol sequence are mapped to the plurality of transmission sources in c-tuples, the modulation symbol sequence representing the plurality of code blocks in which c is dependent on the modulation used for transmitting the signaling. The disclosure also pertains to related devices and methods.

Embodiments of the present disclosure relate to devices, methods, apparatuses and computer readable storage media for supporting various constellations. According to embodiments of the present disclosure, a first device receives, from a second device, configuration information indicating at least one constellation available for a modulation and coding scheme, MCS. The first device receives, from the second device, control information for a communication between the first and second devices. The control information indicating the MCS. The first device performs the communication with the second device based on the control information using a constellation of the at least one constellation.

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.

The invention discloses a communication signal identification method based on a distributed Swin Transform and LSTM fusion model, and belongs to the field of radio communication technology and deep learning, and the method comprises the steps: obtaining a communication signal iq data set; performing continuous wavelet transform on the data set to obtain a wavelet time-frequency diagram; the method comprises the following steps: constructing a distributed Swin Transform model guided by an LSTM (Long Short Term Memory), and fusing an iq sequence feature output by an LSTM module and a wavelet time-frequency graph feature extracted by the distributed Swin Transform model by a distributed Window Transform module through a cross attention mechanism; and identifying a communication signal by using the trained and verified optimization model. According to the method, the time-frequency domain characteristics of the iq signals can be fully explored, the signals can be classified by fully utilizing the wavelet time-frequency graph characteristics, and the identification accuracy of the communication signals is effectively improved.