Receiver signal correction method, device and medium based on terahertz wireless communication

Abstract

The present invention discloses a receiving-end signal correction method, device and medium based on terahertz wireless communication, which belongs to the field of signal processing technology and is used to solve the technical problem that the existing receiving-end signal correction method of wireless communication ignores the existence of noise and interference, and the accuracy of signal correction needs to be improved. The method includes: obtaining a first signal received by the receiving end, and converting the first signal from the time domain to the frequency domain to obtain a first amplitude, a first phase and a first frequency of the first signal, where the first signal is the original signal sent by the transmitting end and reaches the receiving end through the wireless communication channel; based on a machine learning model, correcting at least one of the first amplitude, the first phase and the first frequency to obtain a corrected second amplitude, a second phase and a second frequency; based on the corrected second amplitude, the second phase and the second frequency, determining the corrected second signal. The present invention improves the accuracy of signal correction through the above method.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a receiving-end signal correction method, device, and medium based on terahertz wireless communication. Background Art

[0002] Wireless communication systems usually consist of a transmitter and a receiver. The transmitter is responsible for converting information into electromagnetic wave signals and sending them out through the antenna; the transmitted signal is transmitted through the channel to the receiver, and the receiver is responsible for receiving the electromagnetic wave signals and restoring them to the original information.

[0003] Due to channel complexity (such as multipath effects, interference, and noise), the signal received by the receiver may differ from the signal sent by the transmitter. For example, terahertz waves are susceptible to absorption and scattering when propagating through air, resulting in a shorter transmission distance and significant signal attenuation. The terahertz wave signal received by the receiver may differ significantly from the signal sent by the transmitter.

[0004] Therefore, after receiving a signal, the receiver needs to perform signal correction to ensure its accuracy and reliability. However, existing signal correction methods for wireless communication receivers ignore the presence of noise and interference, resulting in a large error between the corrected signal and the transmitted signal, and the accuracy of signal correction needs to be improved. Summary of the Invention

[0005] The present invention provides a receiving-end signal correction method, device, and medium based on terahertz wireless communication, which are used to solve the technical problems that the existing receiving-end signal correction methods for wireless communication ignore the existence of noise and interference, the error between the corrected signal and the transmitted signal is large, and the accuracy of signal correction needs to be improved.

[0006] In a first aspect, the present invention provides a receiving-end signal correction method based on terahertz wireless communication, comprising:

[0007] Acquire a first signal received by a receiving end, and convert the first signal from a time domain to a frequency domain to obtain a first amplitude, a first phase, and a first frequency of the first signal, where the first signal is a signal of an original signal sent by a transmitting end that reaches the receiving end via a wireless communication channel;

[0008] Based on a machine learning model, correct at least one of the first amplitude, the first phase, and the first frequency to obtain a corrected second amplitude, second phase, and second frequency;

[0009] A corrected second signal is determined based on the corrected second amplitude, second phase, and second frequency.

[0010] In a feasible implementation manner, after converting the first signal from the time domain to the frequency domain, the method further includes:

[0011] The attenuated frequency band in the first signal is enhanced by a filter to obtain a frequency-domain enhanced first signal.

[0012] In a feasible implementation manner, performing enhancement processing on the attenuated frequency band in the first signal by using a filter includes:

[0013] Acquiring an original signal sent by the transmitting end;

[0014] comparing the first signal with the original signal corresponding to the first signal to determine an attenuation frequency band;

[0015] Based on the attenuation frequency band, adjusting the filter weight corresponding to the attenuation frequency band in the learnable filter group;

[0016] The attenuated frequency band in the first signal is enhanced by the learnable filter group.

[0017] In a feasible implementation, based on a machine learning model, at least one of the first amplitude, the first phase, and the first frequency is corrected to obtain a corrected second amplitude, second phase, and second frequency, including:

[0018] Based on a generative adversarial network, the first amplitude is corrected to obtain a corrected first amplitude, the generator in the generative adversarial network is used to generate a corrected second amplitude, and the discriminator in the generative adversarial network is used to evaluate whether the second amplitude is consistent with the amplitude of the original signal sent by the transmitter.

[0019] In a feasible implementation, based on a machine learning model, at least one of the first amplitude, the first phase, and the first frequency is corrected to obtain a corrected second amplitude, second phase, and second frequency, including:

[0020] Based on a recurrent neural network and / or a long short-term memory network, the first phase is corrected to obtain a corrected second phase.

[0021] In a feasible implementation, correcting the first phase based on a recurrent neural network and / or a long short-term memory network to obtain a corrected second phase includes:

[0022] Inputting the first phase into a recurrent neural network to obtain an intermediate phase corrected by the recurrent neural network;

[0023] The intermediate phase is input into a long short-term memory network to obtain a corrected second phase.

[0024] In a feasible implementation, based on a machine learning model, at least one of the first amplitude, the first phase, and the first frequency is corrected to obtain a corrected second amplitude, second phase, and second frequency, including:

[0025] Performing dimensionality reduction processing on the first frequency by an encoder in the autoencoder to extract features of the frequency shift;

[0026] The frequency offset feature is input into a decoder in the autoencoder to obtain a corrected second frequency.

[0027] In a feasible implementation manner, after determining the corrected second signal, the method further includes:

[0028] determining a quality parameter of the second signal, the quality parameter comprising at least one of a signal-to-noise ratio, a bit error rate, a phase offset, a frequency error, and a frequency response, wherein the phase offset is an offset between the second phase and the phase of the original signal, the frequency error is an error between a center frequency in the second frequency and the frequency of the original signal, and the frequency response is used to characterize a relationship between the second signal and the original signal;

[0029] Based on the quality parameter, parameters of the machine learning model are adjusted.

[0030] In a second aspect, the present invention provides a receiving-end signal correction device based on terahertz wireless communication, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the receiving-end signal correction method based on terahertz wireless communication described in any of the above embodiments.

[0031] In a third aspect, the present invention provides a non-volatile computer storage medium, which is a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores at least one program, each of which includes instructions. When the instructions are executed by a terminal, the terminal executes the receiving end signal correction method based on terahertz wireless communication described in any of the above embodiments.

[0032] The receiving-end signal correction method, device, and medium based on terahertz wireless communication provided by the present invention have the following beneficial technical effects compared with the prior art:

[0033] (1) After obtaining the first signal received by the receiving end, the machine learning model of the present invention can correct the amplitude, phase and frequency of the first signal respectively, so as to more comprehensively capture the detailed information contained in the first signal, so that the corrected second amplitude, second phase and second frequency are the same as or similar to the amplitude, phase and frequency of the original signal sent by the transmitting end, thereby reducing the deviation of the amplitude, phase and frequency of the first signal caused by noise and interference in the wireless communication system; the corrected second signal obtained based on the corrected second amplitude, second phase and second frequency reduces the error between the corrected second signal and the original signal sent by the transmitting end, thereby improving the accuracy of signal correction at the receiving end of wireless communication.

[0034] (2) Based on the advantages of different types of machine learning models, the present invention can adopt a suitable machine learning model for the first amplitude, first phase, and first frequency in the first signal, and perform multi-branch network error correction on the first signal to restore the original signal to the greatest extent and reduce signal errors.

[0035] (3) The present invention can also identify and enhance the attenuation band in the first signal through a learnable filter group, thereby reducing the interference of the attenuation in the wireless communication system on the first signal, so that the first signal after frequency domain enhancement can more completely reflect the characteristics of the original signal sent by the transmitter.

[0036] (4) The present invention can monitor the signal quality of the corrected second signal through quality parameters such as the signal-to-noise ratio, bit error rate, phase offset and frequency response of the second signal to ensure the accuracy and robustness of signal recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0038] Figure 1 A flowchart of a receiving-end signal correction method based on terahertz wireless communication provided by the present invention;

[0039] Figure 2 This is a schematic structural diagram of the receiving-end signal correction device based on terahertz wireless communication provided by the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] A signal correction method in the related art obtains the initial level of the initial output signal at the output end of the signal source and the actual level of the actual output signal at the output end of the processing circuit; if they are the same, the actual output signal is output to the signal receiving end; if they are not the same, the actual output signal is corrected and the corrected actual output signal is output to the signal receiving end. However, this method relies too much on the accuracy of the initial level. If the obtained initial level itself has errors or drifts, the corrected signal may also be inaccurate. In addition, this method mainly focuses on level matching and ignores the existence of noise and interference. In actual applications, if the signal is affected by noise or interference, this correction method may output an erroneous signal as a correct signal.

[0042] Another signal correction method in the related art includes: receiving a test signal; coupling the test signal to obtain a coupled signal; performing low-noise amplification on the coupled signal to obtain a low-noise amplified signal; transmitting the signal to obtain a test signal; determining an amplitude difference and a phase difference between the current amplified test signal and the last amplified test signal; and correcting a pre-received echo signal based on the amplitude difference and the phase difference to obtain a correct echo signal. While low-noise amplification of the coupled signal can improve signal strength, it may also amplify some noise, thereby affecting signal quality. Residual noise may still remain in the corrected signal, affecting the accuracy of the system's echo signal.

[0043] Both of the aforementioned signal correction methods ignore the presence of noise and interference, resulting in significant errors between the corrected signal and the transmitted signal, and the accuracy of signal correction needs to be improved. The present invention addresses these deficiencies in the prior art by providing a receiving-end signal correction method, device, and medium based on terahertz wireless communication to address these deficiencies and achieve more efficient wireless communication.

[0044] The technical solution proposed by the present invention is described in detail below with reference to the accompanying drawings.

[0045] Figure 1 The flowchart of the receiving end signal correction method based on terahertz wireless communication provided by the present invention. Figure 1 As shown, the method includes the following execution steps:

[0046] S101: Acquire a first signal received by a receiving end, and convert the first signal from a time domain to a frequency domain to obtain a first amplitude, a first phase, and a first frequency of the first signal.

[0047] The first signal received by the receiving end in the present invention is a transmission signal sent by the transmitting end in the wireless communication system, and is a signal that reaches the receiving end after being transmitted through the wireless communication channel.

[0048] To facilitate analysis and processing, the first signal can be converted from the time domain to the frequency domain. Key features of a frequency domain signal include amplitude, phase, and frequency. After performing the time-frequency conversion, the amplitude, phase, and frequency of the first signal can be obtained, namely, a first amplitude, a first phase, and a first frequency.

[0049] In some embodiments, the first signal may be converted from the time domain to the frequency domain by Fourier transform.

[0050] When the original signal sent by the transmitter is transmitted to the receiver through the wireless communication channel, some frequency bands may be distorted or attenuated due to absorption and scattering. As a possible implementation method, the first signal after time-frequency conversion can be enhanced in the frequency domain, focusing on restoring the frequency band information that is severely attenuated. After converting the first signal from the time domain to the frequency domain, the method also includes: using a filter to enhance the attenuated frequency band in the first signal to obtain the first signal after frequency domain enhancement.

[0051] As a possible implementation method, the attenuation frequency band in the first signal is enhanced through a filter, including: obtaining the original signal sent by the transmitter; comparing the first signal with the original signal corresponding to the first signal to determine the attenuation frequency band; based on the attenuation frequency band, adjusting the filtering weight corresponding to the attenuation frequency band in the learnable filter group; and enhancing the attenuation frequency band in the first signal through the learnable filter group.

[0052] S102. Based on the machine learning model, correct at least one of the first amplitude, the first phase, and the first frequency to obtain a corrected second amplitude, second phase, and second frequency.

[0053] Due to the influence of noise, attenuation and other transmission errors, the first signal is usually a time-domain signal with noise, so the first signal needs to be corrected.

[0054] The present invention processes the first amplitude, first phase, and first frequency of the first signal respectively: only one of the first amplitude, first phase, and first frequency can be corrected through a machine learning model, or two of the first amplitude, first phase, and first frequency can be corrected through a machine learning model, or all three of the first amplitude, first phase, and first frequency can be corrected through a machine learning model respectively.

[0055] The machine learning model used to correct the first amplitude, the machine learning model used to correct the first phase, and the machine learning model used to correct the first frequency can be the same.

[0056] Different types of machine learning models have different advantages. In order to improve the accuracy of correction, different machine learning models can be used to correct the first amplitude, first phase and first frequency respectively according to their characteristics.

[0057] Amplitude represents signal strength and is often affected by factors such as noise, attenuation, and multipath. In the frequency domain, amplitude information primarily reflects the energy distribution of the signal. The goal of the machine learning model used to correct the first amplitude is to restore or enhance the correct signal strength.

[0058] As a possible implementation, the present invention can use a Generative Adversarial Network (GAN) to correct the first amplitude of the first signal to obtain a corrected second amplitude. This is because GANs have excellent applications in amplitude recovery, especially in signal enhancement and compensation, and can reduce amplitude distortion caused by attenuation or noise. The generator of the GAN is responsible for generating the corrected second amplitude, while the discriminator evaluates whether the generated second amplitude is consistent with the original signal. Through adversarial training, the accuracy of amplitude recovery is continuously improved.

[0059] By introducing a GAN-based noise reduction mechanism and generating simulated noise scenarios for adversarial network training, the network can be more generalized, more efficiently identify and suppress complex noise, and improve bit error rate and transmission stability.

[0060] Phase reflects the signal's position on the time axis and is often affected by delay, phase offset, or multipath propagation. Optimizing the phase is crucial because, even with the correct amplitude, an incorrect phase can still cause signal distortion or interference. As one possible implementation, the first phase is corrected based on a recurrent neural network (RNN) and / or a long short-term memory (LSTM) network to produce a corrected second phase.

[0061] In phase correction, RNNs or LSTMs can learn the temporal dependencies of signals, especially in the case of time delay and phase drift. They can capture long-term dependencies in signals and effectively optimize the phase.

[0062] In some embodiments, one of RNN and LSTM can be used to correct the first phase of the first signal to obtain a corrected second signal; or both RNN and LSTM machine learning models can be used simultaneously to combine the advantages of both for phase correction.

[0063] As a possible implementation method, the first phase is corrected based on a recurrent neural network and / or a long short-term memory network to obtain a corrected second phase, including: inputting the first phase into the recurrent neural network to obtain an intermediate phase corrected by the recurrent neural network; inputting the intermediate phase into the long short-term memory network to obtain the corrected second phase.

[0064] The RNN and LSTM networks used in phase correction optimize signal delay and phase drift by combining time series modeling and memory capabilities. Specifically, the RNN and LSTM models accept the raw phase information of the input signal (such as time series data or frequency domain features) as input and capture the signal's temporal correlation and long-term dependencies through their recursive structure. The model output is the corrected phase value.

[0065] In this process, RNNs are used to quickly capture short-term temporal dependencies, while LSTMs, through their memory cells and gating mechanisms, address the problem of traditional RNNs being prone to forgetting over long sequences. The combination of these two ensures that the network can both accurately model short-term characteristics and effectively address long-term phase drift.

[0066] The optimization effect is reflected in the generated corrected phase, which effectively reduces errors caused by time delay and mitigates the impact of phase mismatch on signal transmission quality, thereby improving the overall fidelity and stability of the signal. By introducing this time series modeling technique, the original phase information of the signal can be restored more accurately than correction methods based solely on amplitude or static analysis.

[0067] Frequency offset is typically caused by factors such as multipath, frequency drift, and carrier frequency deviation. Frequency correction focuses on restoring the correct frequency position of the signal to ensure proper demodulation and synchronization. As a possible implementation, an autoencoder can be used to correct the first frequency to generate a corrected second frequency.

[0068] The autoencoder used in frequency correction learns and corrects the frequency offset in the signal through its compression and reconstruction mechanism. Specifically, the autoencoder takes the first frequency of the first signal as input. The encoder performs dimensionality reduction on this first frequency to extract the characteristics of the frequency offset. The decoder then reconstructs the signal based on the extracted characteristics and outputs the corrected second frequency or the frequency offset correction value.

[0069] The encoder is responsible for capturing the patterns and regularities of frequency offsets, helping the network identify complex frequency offset characteristics. The decoder, on the other hand, learns how to restore the signal and correct for issues such as frequency drift and carrier frequency offset. The entire autoencoder network is trained end-to-end to ensure that the frequency position of the reconstructed signal is as close as possible to the correct frequency of the original signal.

[0070] The optimization effect is reflected in the autoencoder's ability to accurately correct frequency errors, restore the signal's correct frequency position, and ensure accurate demodulation and synchronization. Compared to traditional frequency correction methods (such as phase-locked loops or frequency filtering), the autoencoder can more flexibly adapt to nonlinear frequency drift in complex environments, providing greater robustness and accuracy for frequency recovery of the first signal.

[0071] S103 : Determine a corrected second signal based on the corrected second amplitude, second phase, and second frequency.

[0072] The present invention can fuse the corrected second amplitude, second phase and second frequency through the output of the machine learning model to obtain a corrected frequency domain signal, and then convert the corrected frequency domain signal back to the time domain through an inverse Fourier transform to obtain a corrected second signal.

[0073] By respectively correcting the first amplitude, the first phase, and the first frequency in the first signal, it is possible to capture signal details more comprehensively and improve the accuracy of signal reconstruction.

[0074] As a possible implementation, the present invention can measure the accuracy of signal recovery and the performance of a machine learning model used for signal correction using quality parameters of the second signal. After determining the corrected second signal, the method further includes determining quality parameters of the second signal, where the quality parameters include at least one of signal-to-noise ratio, bit error rate, phase offset, frequency error, and frequency response. Based on the quality parameters, the parameters of the machine learning model are adjusted.

[0075] The signal-to-noise ratio (SNR) is a measure of signal quality. The higher the SNR, the better the signal quality. The SNR of the second signal can be calculated by comparing the signal power with the noise power: Among them, P signal is the signal power in the second signal, P noise is the noise power in the second signal. A higher SNR indicates better signal quality of the second signal.

[0076] The Bit Error Rate (BER) measures the probability of bit errors during transmission and is an important indicator of communication system performance. BER is calculated by comparing the difference between the received bit stream and the originally transmitted bit stream to determine the number of errors.

[0077] The present invention can determine the number of bit errors in the second signal by the difference between the bit stream corresponding to the second signal and the bit stream corresponding to the original signal, and determine the bit error rate of the second signal by the proportion of the number of bit errors in the total number of codes.

[0078] The phase offset is the offset between the second phase and the phase of the original signal sent by the transmitter.

[0079] The frequency error is an error between a center frequency in the second frequency and a frequency of an original signal sent by the transmitting end.

[0080] Frequency response describes a system's response to input signals of varying frequencies. It's typically expressed as the amplitude ratio and phase difference between the system's output and input as a function of frequency. It reflects a system's signal processing capabilities in the frequency domain and can be used to analyze distortion, gain, and attenuation as a signal passes through the system.

[0081] The amplitude response is the modulus of the frequency response, indicating the gain or attenuation of the system for different frequency components. If the amplitude response of a frequency component is less than 1 (or negative gain, usually expressed in decibels (dB) on a logarithmic scale), it means that the frequency signal component is attenuated after propagation or passing through the system.

[0082] Phase response is the angular portion of the frequency response. It describes the time delay or phase shift of the different frequency components of a signal after it passes through the system. This delay can cause signal distortion, especially in multi-band signals.

[0083] The frequency response of the second signal is used to characterize the relationship between the second signal and the original signal sent by the transmitter, that is, the frequency response of the wireless communication system composed of the transmitter, the receiver and the wireless communication channel.

[0084] Terahertz waves (0.1 to 10 THz) have abundant spectrum resources and the potential for large bandwidth and extremely high data transmission rates. They are considered to be a key technology for future 6G and even 7G communications. However, terahertz waves are susceptible to absorption and scattering when propagating in the air, resulting in a short transmission distance, large signal attenuation, and a large error between the signal received by the receiving end and the signal sent by the transmitting end. Therefore, how to improve the transmission efficiency of terahertz wave communication and overcome the challenges in its propagation has become an urgent problem to be solved. Taking terahertz wave communication as an example, the receiving end signal correction method based on terahertz wireless communication provided by the present invention is described below.

[0085] The core function of this method is to ensure that the data can be restored to the original signal as accurately as possible at the receiving end, reducing interference from factors such as noise, attenuation, and scattering.

[0086] In this method, the received terahertz wave signal undergoes a multi-step processing process, including signal feature extraction, frequency domain enhancement, joint time-frequency analysis, and multi-branch network error correction, to maximize the restoration of the original signal sent by the transmitter and minimize signal errors. The specific workflow is as follows.

[0087] First, a terahertz wave signal, i.e., a first signal, is received from a terahertz wave transmission link. This signal is usually affected by noise, attenuation, and other transmission errors. The first signal is usually a noisy time-domain signal, expressed as y(t), representing the received time-domain signal.

[0088] The main characteristics of a terahertz signal include amplitude, phase, and frequency. Amplitude represents the strength or power of a terahertz signal and is typically affected by factors such as attenuation, noise, and multipath. Phase represents the phase information of a terahertz signal in the frequency domain and may be offset by multipath propagation. Frequency is affected by the frequency characteristics of the channel, with varying degrees of attenuation in different frequency bands.

[0089] After receiving the terahertz signal, Fourier transform is first used to perform time-frequency domain conversion to convert the time domain signal into a frequency domain signal for subsequent processing.

[0090] During the transmission of terahertz wave signals, some frequency bands may be distorted or attenuated due to absorption and scattering. Therefore, the present invention can convert the first signal from the time domain to the frequency domain, and then perform frequency domain enhancement on the first signal, focusing on restoring the frequency band information that is severely attenuated.

[0091] Frequency domain enhancement can use a learnable filter group to dynamically adjust the frequency band filtering weights, so that the model retains more information for frequency bands with severe attenuation, thereby improving the final signal restoration effect.

[0092] The method for identifying attenuated frequency bands includes: converting the time domain signal y(t) of the first signal into the frequency domain signal Y(f) through Fourier transform, and then comparing the signal strength and noise level of each frequency band of the received first signal with the original signal sent by the transmitter to identify those attenuated frequency bands caused by factors such as the propagation medium (such as the atmosphere).

[0093] By using a learnable filter bank to enhance the received terahertz wave signal in the frequency domain, the signal strength in the attenuated frequency band is restored, allowing the terahertz signal to more fully reflect the characteristics of the original signal. Compared with conventional noise reduction schemes, this method can better adapt to the frequency band characteristics of the terahertz wave signal and improve the quality of the reconstructed signal.

[0094] In frequency-domain signals, amplitude, phase, and frequency are three critical parameters that directly impact signal quality and accuracy. After frequency-domain enhancement, different network types offer different advantages in optimizing these three parameters. The following analysis specifically analyzes amplitude, phase, and frequency to help determine the most suitable network architecture.

[0095] Amplitude represents the strength of the signal and is often affected by factors such as noise, attenuation, and multipath. In the frequency domain, amplitude information primarily reflects the energy distribution of the signal. The goal of optimizing the amplitude is to restore or enhance the correct strength of the signal. This branch uses a generative adversarial network (GAN) to correct the signal amplitude because GANs have excellent applications in amplitude recovery, especially in signal enhancement and compensation, and can reduce amplitude distortion caused by attenuation or noise. The generator of the generative adversarial network is responsible for generating the corrected amplitude, while the discriminator evaluates whether the generated amplitude matches the true signal, continuously improving the accuracy of amplitude recovery through adversarial training.

[0096] By introducing a noise reduction mechanism based on a generative adversarial network (GAN), a simulated noise scenario is generated for adversarial network training, making it more generalizable, able to more efficiently identify and suppress complex noise, and improve bit error rate and transmission stability.

[0097] Phase reflects the signal's position on the time axis and is often affected by delay, phase offset, or multipath propagation. Optimizing phase is crucial because, even with the correct amplitude, incorrect phase can still cause signal distortion or interference. Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks can be used for phase correction. RNNs or LSTMs can learn the temporal dependencies of signals, particularly regarding delay and phase drift. They can capture long-term dependencies in signals, effectively optimizing phase.

[0098] Frequency offset is typically caused by factors such as multipath, frequency drift, and carrier frequency deviation. Frequency correction focuses on restoring the correct frequency position of the signal to ensure proper demodulation and synchronization. This branch can utilize an autoencoder, which learns the pattern of frequency offset by compressing and reconstructing the input signal. This reconstruction process corrects the frequency offset and restores the correct signal frequency.

[0099] Through the output of the machine learning model, the amplitude, phase, and frequency information are corrected, and then the optimized values are fused to obtain a corrected frequency domain signal Then, the corrected frequency domain signal is converted back to the time domain through inverse Fourier transform to obtain the final corrected second signal

[0100]

[0101] Compared with the general scheme, which only extracts a single feature for noise reduction, this scheme can capture the details of the signal more comprehensively and improve the accuracy of signal reconstruction.

[0102] During the signal correction process, we continuously extract and monitor various signal parameters to ensure the accuracy of signal recovery. These parameters include signal-to-noise ratio, bit error rate, phase offset and frequency response.

[0103] This solution utilizes frequency domain enhancement and multi-branch optimization to more comprehensively capture signal details, improve the accuracy of signal reconstruction, and the transmission efficiency and stability of terahertz wave communication.

[0104] The signal parameters throughout the entire process include signal-to-noise ratio, bit error rate, signal amplitude, phase offset, frequency response, etc. These parameters can be used to monitor the optimized signal quality in real time to ensure the accuracy and robustness of signal recovery.

[0105] Corresponding to the above embodiments, the present invention also provides a receiving-end signal correction device based on terahertz wireless communication. Figure 2 A structural schematic diagram of a receiving-end signal correction device based on terahertz wireless communication provided in an embodiment of the present invention, wherein the device may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the receiving-end signal correction method based on terahertz wireless communication as described in the above embodiment.

[0106] In one possible implementation of the present invention, the at least one processor is capable of executing the following steps: obtaining a first signal received by a receiving end, converting the first signal from the time domain to the frequency domain, and obtaining a first amplitude, a first phase, and a first frequency of the first signal, where the first signal is the original signal sent by the transmitting end and reaches the receiving end through a wireless communication channel; based on a machine learning model, correcting at least one of the first amplitude, the first phase, and the first frequency, respectively, to obtain a corrected second amplitude, a second phase, and a second frequency; and determining a corrected second signal based on the corrected second amplitude, the second phase, and the second frequency.

[0107] In a specific implementation, the present invention can also be provided as a non-volatile computer storage medium, wherein the storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each program including instructions, which, when executed by a terminal, enable the terminal to execute the receiving end signal correction method based on terahertz wireless communication as described in the above embodiment.

[0108] In a possible implementation of the present invention, the aforementioned terminal executes, obtains a first signal received by a receiving end, and converts the first signal from the time domain to the frequency domain to obtain a first amplitude, a first phase and a first frequency of the first signal, where the first signal is the original signal sent by the transmitting end and reaches the receiving end through a wireless communication channel; based on a machine learning model, at least one of the first amplitude, the first phase and the first frequency is corrected respectively to obtain a corrected second amplitude, second phase and second frequency; based on the corrected second amplitude, second phase and second frequency, a corrected second signal is determined.

[0109] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0110] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0111] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A receiving-end signal correction method based on terahertz wireless communication, characterized in that: The method comprises: Acquire a first signal received by a receiving end, and convert the first signal from a time domain to a frequency domain to obtain a first amplitude, a first phase, and a first frequency of the first signal, where the first signal is a signal of an original signal sent by a transmitting end that reaches the receiving end via a wireless communication channel; Based on the machine learning model, the first amplitude, the first phase, and the first frequency are corrected respectively to obtain a corrected second amplitude, second phase, and second frequency, including: Based on a generative adversarial network, the first amplitude is corrected to obtain a corrected first amplitude, a generator in the generative adversarial network is used to generate a corrected second amplitude, and a discriminator in the generative adversarial network is used to evaluate whether the second amplitude is consistent with the amplitude of the original signal sent by the transmitting end; Correcting the first phase based on a recurrent neural network and / or a long short-term memory network to obtain a corrected second phase; Performing dimensionality reduction processing on the first frequency by an encoder in the autoencoder to extract features of the frequency offset; inputting the features of the frequency offset into a decoder in the autoencoder to obtain a corrected second frequency; A corrected second signal is determined based on the corrected second amplitude, second phase, and second frequency.

2. The receiving end signal correction method based on terahertz wireless communication according to claim 1, characterized in that: After converting the first signal from the time domain to the frequency domain, the method further includes: The attenuated frequency band in the first signal is enhanced by a filter to obtain a frequency-domain enhanced first signal.

3. The receiving end signal correction method based on terahertz wireless communication according to claim 2, characterized in that: Performing enhancement processing on the attenuated frequency band in the first signal through a filter, including: Acquiring an original signal sent by the transmitting end; comparing the first signal with the original signal corresponding to the first signal to determine an attenuation frequency band; Based on the attenuation frequency band, adjusting the filter weight corresponding to the attenuation frequency band in the learnable filter group; The attenuated frequency band in the first signal is enhanced by the learnable filter group.

4. The receiving end signal correction method based on terahertz wireless communication according to claim 1, characterized in that: Correcting the first phase based on a recurrent neural network and / or a long short-term memory network to obtain a corrected second phase includes: Inputting the first phase into a recurrent neural network to obtain an intermediate phase corrected by the recurrent neural network; The intermediate phase is input into a long short-term memory network to obtain a corrected second phase.

5. The receiving end signal correction method based on terahertz wireless communication according to claim 1, characterized in that: After determining the corrected second signal, the method further includes: determining a quality parameter of the second signal, the quality parameter comprising at least one of a signal-to-noise ratio, a bit error rate, a phase offset, a frequency error, and a frequency response, wherein the phase offset is an offset between the second phase and the phase of the original signal, the frequency error is an error between a center frequency in the second frequency and the frequency of the original signal, and the frequency response is used to characterize a relationship between the second signal and the original signal; Based on the quality parameter, parameters of the machine learning model are adjusted.

6. A receiving-end signal correction device based on terahertz wireless communication, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the receiving end signal correction method based on terahertz wireless communication according to any one of claims 1 to 5.

7. A non-volatile computer storage medium, characterized in that The storage medium is a non-volatile computer-readable storage medium, which stores at least one program. Each of the programs includes instructions. When the instructions are executed by the terminal, the terminal executes the receiving end signal correction method based on terahertz wireless communication according to any one of claims 1 to 5.

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