580results about "Modulation type identification" patented technology

The invention discloses an unmanned aerial vehicle signal identification method and system in a complex electromagnetic environment, and belongs to the field of unmanned aerial vehicles. According to the invention, the form of the liquid metal channel of the liquid metal antenna is adjusted to adapt to the target frequency band, and regional electromagnetic signals are collected in all directions. And generating spatial distribution data including the unmanned aerial vehicle and the interference direction based on the phase difference and preset features. The deformation of the liquid metal droplets is regulated and controlled through the electrowetting voltage, so that the antenna main lobe is aligned with the unmanned aerial vehicle, and the side lobe suppression area covers an interference source. Then, a target direction signal set is collected, and time domain fluctuation, frequency domain distribution and spatial domain direction multi-dimensional features are extracted; and inputting the multi-dimensional features into a joint probability model constructed by a Bayesian network, and outputting posterior probability information. Markov chain Monte Carlo self-adaptive sampling is utilized, the classification confidence coefficient is calculated through iterative convergence, the signal category is judged according to a threshold value, and the unmanned aerial vehicle signal recognition accuracy can be improved in the complex electromagnetic environment.

Accordingly, there are disclosed herein receivers and receiving methods that provide a graceful transition from PAM2 to PAM4 signaling. One illustrative method includes: negotiating a link speed having PAM4 signaling; performing adaption of at least one gain or filter coefficient during PAM2 signaling; switching to PAM4 detection before receiving PAM4 signaling; disabling said adaptation before said switching to PAM4 detection; detecting PAM4 signaling using at least one statistic of detected PAM4 symbols; and enabling said adaptation after PAM4 signaling is detected. Another illustrative method includes: negotiating a link speed having PAM4 signaling; adapting at least one of gain and filter coefficients during PAM2 signaling; monitoring for a change in at least one signal characteristic while performing PAM2 detection; and transitioning to PAM4 detection after detecting said change.

The invention discloses an unmanned aerial vehicle radio communication frequency band countering method for automatic frequency identification and frequency writing, and relates to the technical field of unmanned aerial vehicle interference, and the method comprises a frequency band identification module which is used for scanning a 300MHz-6GHz frequency band in real time and identifying an enemy unmanned aerial vehicle communication signal; the signal analysis module is connected with the frequency band identification module, comprises a digital signal processor and an FPGA (Field Programmable Gate Array), and is used for analyzing signal modulation types and spectrum characteristics; the automatic frequency writing module is used for adjusting an interference frequency band through a direct digital frequency synthesis technology according to an analysis result; the multi-frequency-band interference transmitter supports synchronous interference in frequency bands of 1.2 GHz, 2.4 GHz, 5.2 GHz and 5.8 GHz; the intelligent control terminal integrates an adaptive interference algorithm, and the response time is less than or equal to 1 second. According to the unmanned aerial vehicle radio communication frequency band countering method capable of automatically identifying and writing the frequency, the radio communication frequency band used by the enemy unmanned aerial vehicle is automatically identified, and the corresponding frequency band is written in real time for accurate interference, so that the interference effect of an interference system facing the enemy unmanned aerial vehicle is improved.

The invention discloses a modulation signal identification method based on continuous wavelet transform and deep learning, and the method comprises the steps: firstly modulating a to-be-transmitted sequence at a transmitting end, obtaining a wireless continuous time signal at a receiving end, sampling the continuous time signal, carrying out the wavelet decomposition of the sampled data, and filtering out the high-frequency noise; carrying out reconstruction by using wavelet inverse transformation, and carrying out median filtering on the reconstructed signal to obtain a new data set; performing continuous wavelet transform on the new data set to obtain a time-frequency graph, and extracting feature information of a time domain and a frequency domain of the time-frequency graph to obtain a two-dimensional feature graph; constructing a convolutional neural network prediction algorithm model, and training the convolutional neural network prediction algorithm model by using the two-dimensional feature map extracted by continuous wavelet transform; and finally, classifying the modulation modes of the signals by using the trained convolutional neural network prediction algorithm model. According to the method, the modulation signal identification accuracy in a complex environment in reality is improved, the network parameter quantity is reduced, and the modulation signal identification efficiency is improved.

The invention discloses a signal identification method based on broadband spectrum detection, and the method comprises the steps: carrying out the power spectrum estimation and signal detection of broadband IQ data through an FPGA, obtaining a signal frequency point parameter, and configuring a DDC module to complete digital down conversion; performing power spectrum density estimation and noise floor estimation on the narrowband IQ signal on the upper computer; preliminary classification is carried out by calculating a ratio parameter C1 of a maximum value and a second maximum value of a frequency spectrum, and when C1 is greater than a threshold, AM and a single carrier signal are discriminated by combining a frequency spectrum characteristic parameter C2 and an amplitude spectrum characteristic parameter C3; when C1 is smaller than or equal to a threshold, signal detection is carried out by adopting a double-threshold algorithm, and merging and screening in a multi-signal scene are completed; classifying based on a normalized zero center instantaneous amplitude spectrum density maximum value parameter C4, judging FM, MSK and FSK signals when the C4 is less than a threshold, and judging BPSK, QPSK, 8PSK and 16QAM signals when the C4 is greater than or equal to the threshold; according to the method, a multi-parameter joint judgment strategy is adopted, and the accuracy and the real-time performance of signal recognition are remarkably improved.

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.

A communication method and device are applied to the technical field of communication, can be applied to a wireless local area network system of a next generation Wi-Fi protocol of IEEE 802.11 ax, such as 802.11 be, Wi-Fi 7 or EHT, and 802.11 series protocols such as 802.11 bn, Wi-Fi 8, UHR, Wi-Fi AI, millimeter wave (MMW) and the like, and can also be applied to a WPAN system supporting UWB, a sensing system and the like. A first communication device generates a PPDU and transmits the PPDU. The second communication device receives the PPDU and analyzes the PPDU. The PPDU comprises first indication information, the first indication information can be used for indicating a first modulation mode set and a coding rate, and the first modulation mode set is one of N modulation mode sets divided based on W modulation modes. The Q modulation modes in the first modulation mode set are used for modulating the PPDU.

The invention discloses a ship instant messaging method and system, and belongs to the technical field of data processing, and the method comprises the steps: carrying out the signal intensity detection of each communication channel related to a ship, determining a signal intensity detection result corresponding to each communication channel, and determining a target channel according to the signal detection results corresponding to all communication channels, according to the embodiment of the invention, a better channel can be determined for communication, so that the quality of instant communication is ensured, and then the communication signal identification model improved by the information fusion search algorithm is adopted to perform blind identification on the instant communication signal, so that the modulation mode of the instant communication signal can be rapidly identified, and thus rapid demodulation is realized, and the communication quality is further 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.

The invention discloses a radio modulation signal identification method and system based on a hybrid neural network, and the method comprises the steps: obtaining a radio modulation signal data set, and dividing the radio modulation signal data set into a training set, a test set and a verification set according to a proportion; the method comprises the following steps of: constructing a hybrid neural network model on the basis of a frequency interaction module (FIM), an interaction gate Transform and a Bi-LSTM (Bidirectional Long Short Term Memory); and training and verifying the hybrid neural network model through the training set and the verification set to obtain an identification model, and inputting the test set into the identification model to complete identification of the radio modulation signal in the test set. Denoising is carried out through FIE, the global feature extraction capability of Transform and the time sequence feature extraction capability of Bi-LSTM are effectively combined, the recognition precision is effectively improved through reasonable design, meanwhile, the robustness of noise influence is enhanced, and the model can complete accurate recognition and classification of modulation signals in a complex noise 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 non-cooperative communication signal modulation identification method based on multi-scale feature fusion extraction, which belongs to the technical field of cognitive radio communication, and comprises the following steps: generating time domain signals of communication signals in different modulation patterns, and preprocessing to obtain a plurality of baseband IQ signals as sample data; forming a communication signal data set with the corresponding modulation pattern as a sample label; initializing a multi-scale feature fusion extraction network model, and training the multi-scale feature fusion extraction network model by using the modulation signal data set to obtain a trained multi-scale feature fusion extraction network model; and obtaining a time domain signal of a non-cooperative communication signal in real time, performing preprocessing, inputting the trained multi-scale feature fusion extraction network model, and obtaining a recognition result of a corresponding time domain signal modulation pattern. Accurate and efficient identification of the non-cooperative communication signal modulation pattern is realized.

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 discloses an interpretable learning-based information importance cognitive defense method in automatic modulation identification, which comprises the following steps of: generating a trace data set with a label, and dividing the data set; constructing an SHAP gradient interpreter based on the training set and an original modulation classification model; an FGSM attack is applied to the test set to generate a trace of confrontation samples, the trace of confrontation samples are input into an interpreter to be interpreted, negative information carried by the confrontation samples is analyzed according to an output shapley value, and the transferability guidance of the SHAP method is verified; performing addition and dimension reduction processing on the shapley value to obtain sampling points playing a negative role in a classification result, deleting the corresponding sampling points in the training set and the verification set, and performing fine tuning on the model to obtain a new model; and for data attacked in reality, deleting corresponding negative sampling points, and inputting the data into the new model to test the classification performance. According to the method, the defense capability of the model in an attack scene is remarkably improved, and meanwhile, consumption of data calculation and storage resources is reduced.

The invention discloses a low signal-to-noise ratio conditional modulation identification method based on wavelet transform and channel attention. The method comprises the following steps: acquiring a received signal; and inputting the received signal into an identification model of a pre-trained neural network, and outputting a classification identification result and classification accuracy. In the recognition model based on signal reconstruction and the neural network, a wavelet threshold estimation module and a wavelet reconstruction module form a signal reconstruction path, a multi-scale feature extraction module and a prediction classification module form a recognition path, and the two paths are mutually enhanced, so that the combination of the advantages of a digital signal processing technology and the neural network is realized; a wavelet threshold estimation module is introduced to predict a denoising threshold through a neural network, and network parameters of the wavelet threshold estimation module are updated according to back propagation; a signal reconstruction path is combined with a recognition path, the interpretability of the network is enhanced, and the accuracy of modulation recognition in a low signal-to-noise ratio scene is remarkably improved by using multi-scale modulation characteristics.

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 the technical field of data intelligent testing and traceability, and provides a civil aviation radio parameter intelligent testing and traceability method and system, and the method comprises the steps: obtaining target spectrum feature data obtained by scanning a target frequency band through a phased-array antenna array in a dynamic spectrum interference environment, and adjusting the phased-array antenna array; and converting a civil aviation radio receiving signal in the target spectrum data after anti-interference optimization into time-frequency domain data, generating a signal spectrum, generating a modulation parameter characteristic and a frequency deviation parameter characteristic, and generating a civil aviation radio parameter traceability result containing an equipment identity identifier and a parameter deviation value. According to the invention, through optimization adjustment and signal feature analysis of the phased-array antenna in a dynamic spectrum interference environment, the problem of unstable anti-interference effect in the prior art is solved; the intelligent level and the practicability of the whole system are not high, and intelligent testing and accurate traceability of civil aviation radio parameters are achieved.

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 belongs to the technical field of signal processing, and provides a blind demodulation method and device for a wireless signal, and the method comprises the steps: screening out a candidate modulation type from a plurality of modulation types according to the time domain features in the wireless signal; for each candidate modulation type, estimating a target parameter of the IQ data in the wireless signal corresponding to the candidate modulation type to obtain a target parameter estimation result; performing symbol synchronization processing on the target parameter estimation result to obtain a symbol synchronization point; calculating EVM (Error Vector Magnitude) corresponding to the candidate modulation type according to the symbol synchronization point, and determining the candidate modulation type corresponding to a minimum value as a target modulation type under the condition that the minimum value in the EVM corresponding to each candidate modulation type is smaller than or equal to an EVM threshold value; and demodulating the wireless signal based on the target modulation type to obtain a demodulation result. According to the method provided by the invention, the blind demodulation efficiency and accuracy of the composite modulation signal in a complex channel or a low signal-to-noise ratio environment are improved.

The invention belongs to the technical field of cognitive radio, and particularly relates to an open set modulation identification method based on time-frequency domain feature learning and fusion. The method comprises the following steps of: receiving a signal by adopting a receiving antenna through electromagnetic spectrum monitoring equipment, capturing a space electromagnetic radiation signal, converting the space electromagnetic radiation signal into an electric signal, amplifying the signal through a low-noise amplifier, performing frequency conversion and filtering through an analog receiver, and converting the received signal into an intermediate-frequency signal. Then, the signal is digitalized through AD sampling, and the intermediate frequency signal is converted into a baseband complex signal through frequency mixing and digital filtering in a digital receiver. Then performing time domain characterization calculation and frequency domain characterization calculation on the signal sampling data to form a plurality of time domain characterization vectors and a plurality of frequency domain characterization vectors; and then the vectors are sent to a trained deep neural network model, reasoning is carried out by using the network model, a reasoning result is post-processed, and a signal modulation identification result is finally obtained. According to the invention, the recognition accuracy of unknown signals is improved.

The invention 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 frequency spectrum monitoring and interference analysis method for power grid wireless communication, and particularly relates to the technical field of wireless frequency spectrum monitoring. Outputting an eddy current distribution parameter set based on the power frequency magnetic field intensity distribution data of the monitoring point; synchronously acquiring a frequency spectrum signal and a power frequency magnetic flux density signal; calculating high-frequency harmonic frequency point characteristics of secondary radiation generated by the frequency spectrum monitoring equipment in the power frequency magnetic field, and generating an eddy current characteristic spectral line graph; extracting a frequency band energy value matched with the eddy current characteristic spectral line diagram in the frequency spectrum signal, and calculating a pollution proportion of secondary radiation to the frequency spectrum signal in combination with the power frequency magnetic flux density signal; eliminating pollution signal components according to the pollution proportion, and outputting a real spectrum; and performing joint scanning on the real frequency spectrum, extracting modulation characteristics and spatial orientation of the residual interference signal, and generating an interference source positioning report. The problem of real spectrum signal distortion caused by eddy current secondary radiation in a power frequency magnetic field environment is solved, and the accuracy and reliability of spectrum monitoring and interference analysis 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.

The invention discloses an adaptive modulation and radio frequency fingerprint combined identification method based on multi-task learning. A dual-task joint recognition model used in the method comprises a feature extraction expert network and a multi-head classifier. The feature extraction expert network comprises a plurality of shared convolutional neural network modules serving as expert networks, and further comprises two gating networks in one-to-one correspondence with two recognition tasks; the gating network obtains probabilities in one-to-one correspondence with the expert networks according to the input multi-label signals, and then performs weighted fusion with the output features of all the expert networks according to the probabilities; and the output features after weight fusion are sent to the corresponding classifiers to obtain recognition results of the corresponding tasks. According to the method, a multi-task architecture of a multi-gate hybrid expert network is adopted, two tasks of modulation mode and radio frequency fingerprint identification can be completed at the same time, meanwhile, technical means such as an attention mechanism and loss weight dynamic optimization are introduced, and the identification accuracy is improved.

The invention provides a low-voltage communication demodulation platform, which relates to the technical field of communication and comprises a demodulation main system. The demodulation main system comprises a signal acquisition module, a data storage module, a clock management module, an interface module, a fault detection and recovery module, a protocol conversion module, a signal processing module, a frequency spectrum management module, a safety communication module, a power grid environment adaptation module and an intelligent demodulation module. The intelligent demodulation module realizes adaptive modulation identification and demodulation through a content convolutional neural network (CNN) and a support vector machine (SVM), the CNN is used for extracting features of carrier signals, and the SVM performs modulation mode classification; the intelligent demodulation module is combined with a Kalman filter through a generative adversarial network (GAN) to realize channel noise processing and enhancement, the GAN is used for generating a low-noise signal, and the Kalman filter further optimizes the signal quality.

The invention belongs to the technical field of radio electronics and signal processing, and particularly relates to a color noise environment signal frequency domain detection method and system based on spectrum envelope extraction, and the system comprises a signal acquisition unit, a signal conversion unit, a signal judgment unit and a signal output unit. The method is a research summary of a color noise environment signal frequency domain detection algorithm based on spectrum envelope extraction, and the algorithm is used for blind signal detection under the conditions of uneven noise spectrum and broadband reception, has good robustness, can be used for low signal-to-noise ratio occasions, and is high in signal detection accuracy.

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.

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.

Various aspects of the present disclosure generally relate to wireless communications. 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, based at least in part on the indication, whether the UE is able to communicate with the base station. The UE may selectively communicate with the base station using at least one of the one or more waveforms based at least in part on the determination. Numerous other aspects are provided.

The invention relates to a multi-rate wireless frame adaptive architecture method and device, equipment and a storage medium, and the method comprises the steps: receiving channel state information, and determining pilot frequency configuration according to the channel state information; receiving service data to be transmitted, selecting a corresponding modulation mode based on the channel state information, and performing symbol modulation on the service data according to the modulation mode to obtain a service modulation symbol; carrying out frequency hopping distribution on the service modulation symbols based on the pilot frequency configuration, and carrying out frequency hopping transmission on the service modulation symbols according to the frequency hopping distribution; and receiving the frequency hopping data after the frequency hopping transmission, and performing data recovery on the frequency hopping data to obtain decoded service data. The method and the device have the effect of improving the adaptability of the multi-rate architecture.