Iot signal processing method based on dvb system under extremely low snr

Abstract

The application relates to a DvB system-based Internet of Things signal processing method under an extremely low signal-to-noise ratio, which comprises the following steps: S1, receiving a signal, and cutting the signal from a preset position according to a preset length; S2, adopting a rotation factor dimension reduction and a nonlinear frequency offset compensation algorithm to perform signal dimension reduction, signal feature identification and square loop nonlinear frequency offset compensation on the cut signal, so as to obtain a correct despreading position and a frequency offset estimation result; S3, according to the frequency offset estimation result, selecting a sampling point with a maximum FFT operation result of the frequency offset estimation of the cut signal as an optimal sampling point; and S4, according to the frequency offset estimation result of the optimal sampling point and a starting position of a spread spectrum code, performing starting position capture of the received signal by using a correlation operation peak value method. The application can realize feature identification of a preset position of a received signal, reduces the calculation complexity of the validity determination of the received signal, realizes signal dimension reduction of the received signal by introducing a rotation factor, and effectively reduces the complexity of subsequent signal processing algorithms.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to an Internet of Things (IoT) signal processing method based on the DvB system under extremely low signal-to-noise ratio conditions. Background Technology

[0002] With the expansion of the Internet of Things (IoT) and the continuous development of satellite communications, high-throughput satellite-terrestrial IoT applications have become an important trend. Ground-based end-users in IoT applications typically have small size and limited power consumption, requiring operation under low power consumption and small antenna size conditions. Traditional high-throughput satellites use DVB (Digital Video Broadcasting) waveforms, which, when directly applied to IoT scenarios, suffer from insufficient link margin. Spread spectrum technology is needed to improve gain and achieve reliable information transmission under extremely low signal-to-noise ratio conditions.

[0003] The existing DvB-RCS2 standard protocol uses retransmission as the spreading scheme, but the actual gain differs significantly from the theoretical value, which cannot meet the requirements of IoT applications. Therefore, direct spreading is adopted as the spreading scheme. However, under extremely low signal-to-noise ratio conditions, frequency offset estimation of the received signal in direct spreading is difficult, and the complexity of existing algorithms is too high.

[0004] Existing frequency offset estimation algorithms are divided into two types: those without data assistance and those with data assistance. Existing algorithms based on... The data-unassisted frequency offset estimation algorithm for BPSK modulated signals is a fourth-order method. Under extremely low signal-to-noise ratio (SNR) conditions, the estimation performance of the fourth-order method drops sharply, failing to accurately estimate the frequency offset. Data-assisted frequency offset estimation algorithms, such as Chinese invention patents CN101051852A (Two-dimensional fast acquisition device and method for spread spectrum signals), CN117650808A (A multi-dimensional demodulation device for large dynamic range spread spectrum signals), and CN118746847A (Satellite signal acquisition method, device, and electronic equipment), require alignment of the received signal with the reference signal. While employing a position-frequency two-dimensional scanning method effectively reduces the number of position dimension scans, under extremely low SNR conditions, it still requires increasing scanning accuracy and the number of scans to ensure significant computational results, leading to excessive computational resource consumption and preventing signal processing from being completed within the specified time slot. The PMF-FFT algorithm can meet the requirements of extremely low SNR applications and uses parallel processing to shorten processing time; however, due to the long frame length after DVB-RCS2 spread spectrum, the processing time still does not meet the timeliness requirements.

[0005] Therefore, how to reduce the signal-to-noise ratio under extremely low conditions? The computational complexity of the BPSK signal frequency offset estimation algorithm has become a problem that urgently needs to be solved. Summary of the Invention

[0006] To address the technical problems existing in the prior art, the present invention aims to provide an IoT signal processing method based on the DvB system under extremely low signal-to-noise ratio conditions, thereby reducing... The computational complexity of the BPSK signal frequency offset estimation algorithm.

[0007] To achieve the above-mentioned objectives, this invention provides an IoT signal processing method based on DvB architecture under extremely low signal-to-noise ratio conditions, comprising the following steps:

[0008] Step S1: Extract the received signal from a preset position according to a preset length;

[0009] Step S2: Using rotation factor dimensionality reduction and nonlinear frequency offset compensation algorithm, the intercepted signal is subjected to signal dimensionality reduction, signal feature recognition and square loop nonlinear frequency offset compensation to obtain the correct despreading position and frequency offset estimation results.

[0010] Step S3: Based on the frequency offset estimation result, select the sampling point with the maximum value of the frequency offset estimation FFT operation result in the signal as the optimal sampling point;

[0011] Step S4: Based on the frequency offset estimation results of the spreading code start position and the optimal sampling point, the start position of the received signal is captured using the correlation calculation peak method.

[0012] According to one technical solution of the present invention, step S2 specifically includes:

[0013] Step S21: Introduce a rotation factor to reduce the dimensionality of the intercepted signal;

[0014] Step S22: Despread the signal after introducing the rotation factor. The rotation factor and despreading work together to achieve feature recognition and obtain the correct despreading position.

[0015] Step S23: Based on the correct despreading position, frequency offset estimation is performed on the feature recognition result through square ring nonlinear processing.

[0016] According to one technical solution of the present invention, in step S21, the modulated signal is shifted from the four quadrants to the first and third quadrants by introducing a rotation factor, as shown below:

[0017]

[0018] in, For the signal after introducing the rotation factor, = , , , , respectively, represent extracting one bit every four bits of the signal, starting from the 1st, 2nd, 3rd, and 4th bits, until the last bit of the signal. This indicates that the real part of the signal within the parentheses is selected. This indicates that the imaginary part of the signal within the parentheses is selected.

[0019] According to one technical solution of the present invention, step S22 specifically includes:

[0020] Step S221: Despread the signal after introducing the rotation factor, as follows:

[0021]

[0022] in, SF is the length of the signal before spread spectrum, and SF is the spreading factor. This is the despread signal;

[0023] Step S222: Based on the different positions of the spreading codes in the despread signal and the direct spreading sequence, several despread signal sequences are obtained, represented as follows:

[0024]

[0025] in This is the starting position of the spreading code for the direct spreading sequence. The starting position of the spreading code for the direct spreading sequence The corresponding despread signal, where SF represents the spreading factor;

[0026] Step S223: Determine the amplitude of the multiple despread signal sequences. The starting position of the spreading code corresponding to the maximum amplitude is the correct despreading position, represented as:

[0027] ;

[0028] Step S224: Perform signal feature identification on the despread signal, determine the peak-to-average ratio (PAR) of the signal features, and discard invalid data if the PAR exceeds the PAR threshold.

[0029] According to one technical solution of the present invention, step S23 specifically includes:

[0030] Step S231: Based on the correct despreading position, perform a square operation on the feature recognition result, expressed as:

[0031]

[0032] in, The phase of the signal after squaring is a phase that can be linearly modeled.

[0033] Step S232: Perform FFT operation on the squared signal, and extract the frequency offset based on the FFT operation result to obtain the frequency offset estimation result. The FFT operation is expressed as follows:

[0034] .

[0035] According to one technical solution of the present invention, step S4 specifically includes:

[0036] Step S41: Determine the location capture range based on the received signal;

[0037] Step S42: Design the relevant calculation thresholds and position steps;

[0038] Step S43: Perform truncation processing on the received signal based on the frequency offset estimation results of the correct despreading position and the optimal sampling point;

[0039] Step S44: Despread the intercepted received signal;

[0040] Step S45: Traverse the intercepted received signals according to the position step and obtain the peak-to-average power ratio (PAPR) of the intercepted received signals and the frame header preamble.

[0041] Step S46: Determine whether the peak value exceeds the relevant calculation threshold. If yes, capture is complete; otherwise, return to step S43.

[0042] According to a technical solution of the present invention, in both step S1 and step S42, the interception processing of the received signal includes:

[0043] Set the truncated signal length according to the signal-to-noise ratio requirements and the capabilities of subsequent despreading and frequency offset estimation algorithms;

[0044] Based on the set signal length, the signal is captured from a preset position;

[0045] In step S1, the preset position is the data frame start position, and in step S42, the preset position is the correct despreading position.

[0046] According to one technical solution of the present invention, step S44 specifically includes:

[0047] The length of the correlation operation signal is selected based on the spreading ratio and the DSP correlation operation length requirements. The spread preamble reference signal and the truncated received signal are processed according to the correlation operation signal length. Finally, the spread preamble reference signal and the truncated received signal are used for... BPSK despreading.

[0048] According to one aspect of the present invention, an electronic device includes: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the above-described IoT signal processing method based on the DvB system under extremely low signal-to-noise ratio.

[0049] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the above-described IoT signal processing method based on the DvB system under extremely low signal-to-noise ratio.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] This invention provides an IoT signal processing method based on a DvB (Distributed Vulnerability) architecture for extremely low signal-to-noise ratio (SNR). By truncating the received signal and performing dimensionality reduction, feature recognition, and square-loop nonlinear frequency offset compensation on the truncated signal, the correct despreading position and frequency offset estimation results are obtained. The starting position of the received signal is captured based on the frequency offset estimation results of the spreading code start position and the optimal sampling point. This reduces the computational complexity of determining the validity of the received signal by performing feature recognition at a preset position. Compared to traditional acquisition methods that involve a sliding search from the starting position of the received data, where signal reception is considered failed if no relevant peak is found beyond a preset position, this invention uses feature recognition at a preset position of the received signal, transforming the sliding search into a single feature recognition operation, thus reducing the computational complexity of determining the validity of the received signal.

[0052] This invention employs a rotation factor dimensionality reduction and a nonlinear frequency offset compensation algorithm. By introducing a rotation factor, the received signal is reduced in dimensionality, effectively reducing the complexity of subsequent signal processing algorithms. Frequency offset compensation under extremely low signal-to-noise ratio (SNR) conditions is achieved through despreading and square-loop nonlinear processing. Nonlinear frequency offset compensation algorithms without data assistance have high SNR thresholds, failing to meet the requirements of extremely low SNR scenarios. Data-assisted frequency offset compensation algorithms require increased scanning accuracy and number of scans under extremely low SNR conditions, resulting in excessive computational complexity and long processing times. This invention utilizes a rotation factor dimensionality reduction and a nonlinear frequency offset compensation algorithm. The algorithm introduces a rotation factor to reduce the dimensionality of the received signal, effectively reducing the complexity of subsequent signal processing algorithms. The BPSK signal is dimensionally reduced and then despread and subjected to square-loop nonlinear processing to achieve frequency offset compensation under extremely low signal-to-noise ratio conditions. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0054] Figure 1 A flowchart illustrating an IoT signal processing method based on DvB architecture with extremely low signal-to-noise ratio according to an embodiment of the present invention is shown.

[0055] Figure 2 This schematic diagram illustrates a flowchart of a rotation factor dimensionality reduction and nonlinear frequency offset compensation algorithm according to one embodiment of the present invention.

[0056] Figure 3 This schematic diagram illustrates a location acquisition flowchart for IoT signal processing based on the DvB architecture under extremely low signal-to-noise ratio according to one embodiment of the present invention. Detailed Implementation

[0057] The description of the embodiments in this specification should be taken in conjunction with the accompanying drawings, which should form part of the complete specification. In the drawings, the shape or thickness of the embodiments may be exaggerated and may be indicated in a simplified or convenient manner. Furthermore, parts of the various structures in the drawings will be described separately; it is worth noting that elements not shown in the figures or not described in words are in a form known to those skilled in the art.

[0058] The descriptions of the embodiments herein, including any references to directions and orientations, are for ease of description only and should not be construed as limiting the scope of the invention. The following description of preferred embodiments involves combinations of features, which may exist independently or in combination; the invention is not particularly limited to the preferred embodiments. The scope of the invention is defined by the claims.

[0059] like Figure 1 As shown, the present invention provides an IoT signal processing method based on DvB architecture under extremely low signal-to-noise ratio conditions, comprising:

[0060] Step S1: Extract the received signal from a preset position according to a preset length;

[0061] In this embodiment, the preset length for signal interception in step S1 is set to half the length of the signal data frame, and the preset position is the starting position of the signal data frame.

[0062] The DvB-RCS2 received signal contains an irrelevant signal of a preset length. The signal is truncated from a preset position for subsequent signal feature identification and frequency offset compensation operations. The preset length can be set according to the signal-to-noise ratio requirements and the capabilities of subsequent despreading and frequency offset estimation algorithms.

[0063] Step S2: Using rotation factor dimensionality reduction and nonlinear frequency offset compensation algorithm, the intercepted signal is subjected to signal dimensionality reduction, signal feature recognition and square loop nonlinear frequency offset compensation to obtain the correct despreading position and frequency offset estimation results.

[0064] Step S2 specifically includes:

[0065] Step S21: Introduce a rotation factor to reduce the dimensionality of the intercepted signal;

[0066] Step S21 specifically includes:

[0067] By introducing a rotation factor, the modulated signal is shifted from the four quadrants to the first and third quadrants;

[0068] In this embodiment, the received signal is a DvB-RCS2 direct sequence spread spectrum signal, which is first directly spread spectrumd and then modulated at the transmitting end. The signal employs... BPSK modulation method The BPSK modulation function is expressed as:

[0069]

[0070] in, The signal before modulation. The signal is modulated, where i represents the bit sequence number, containing each bit from the beginning to the end. The imaginary unit;

[0071] according to BPSK modulation, by introducing a rotation factor, shifts the modulated signal from the four quadrants to the first and third quadrants, as shown below:

[0072]

[0073] in, For the signal after introducing the rotation factor, = , , , , respectively, represent extracting one bit every four bits of the signal, starting from the 1st, 2nd, 3rd, and 4th bits, until the last bit of the signal. This indicates that the real part of the signal within the parentheses is selected. This indicates that the imaginary part of the signal within the parentheses is selected.

[0074] Step S22: Despread the signal after introducing the rotation factor. The rotation factor and despreading work together to achieve feature recognition and obtain the correct despreading position.

[0075] In this embodiment of the invention, after introducing a rotation factor to shift the signal from the four quadrants to the first and third quadrants, due to the rotation factor and The characteristics of BPSK modulation, and the relationship between the signal after introducing the rotation factor and the signal before modulation, are expressed as follows:

[0076] .

[0077] Step S22 specifically includes:

[0078] Step S221: Despread the signal after introducing the rotation factor, as follows:

[0079]

[0080] in, SF is the length of the signal before spread spectrum, and SF is the spreading factor. This is the despread signal;

[0081] Step S222: Based on the different positions of the spreading codes in the despread signal and the direct spreading sequence, several despread signal sequences are obtained, represented as follows:

[0082]

[0083] in This is the starting position of the spreading code for the direct spreading sequence. The starting position of the spreading code for the direct spreading sequence The corresponding despread signal, where SF represents the spreading factor;

[0084] Step S223: Determine the amplitude of the multiple despread signal sequences. The starting position of the spreading code corresponding to the maximum amplitude is the correct despreading position, represented as:

[0085] ;

[0086] Step S224: Perform signal feature identification on the despread signal, determine the peak-to-average ratio (PAR) of the signal features, and discard invalid data if the PAR exceeds the PAR threshold.

[0087] Step S23: Based on the correct despreading position, frequency offset estimation is performed on the feature recognition result through square ring nonlinear processing.

[0088] Step S23 specifically includes:

[0089] Step S231: Based on the correct despreading position, perform a square operation on the feature recognition result, expressed as:

[0090]

[0091] in, The phase of the signal after squaring is a phase that can be linearly modeled.

[0092] In this embodiment, a linear phase is obtained by performing nonlinear processing on the despread signal, which is beneficial for subsequent frequency offset extraction through FFT operation.

[0093] Step S232: Perform FFT operation on the squared signal, and extract the frequency offset based on the FFT operation result to obtain the frequency offset estimation result. The FFT operation is expressed as follows:

[0094]

[0095] The frequency offset estimation results obtained from the above FFT operation can be used for frequency offset compensation of the received signal.

[0096] Step S3: Based on the frequency offset estimation result, select the sampling point with the maximum value of the frequency offset estimation FFT operation result in the signal as the optimal sampling point;

[0097] In this embodiment, the received signal is oversampled four times at the receiving end. Despreading and frequency offset estimation are performed on signals at different sampling points, and the sampling point corresponding to the largest frequency offset estimation FFT operation result is selected as the optimal sampling point. In this embodiment, selecting the optimal sampling point for subsequent position estimation and acquisition can effectively reduce the resource consumption ratio of the system during signal processing compared to the traditional full-bandwidth search mode, and effectively improve signal processing efficiency and speed.

[0098] Step S4: Based on the frequency offset estimation results of the spreading code start position and the optimal sampling point, the start position of the received signal is obtained using the correlation calculation peak method to complete the acquisition.

[0099] like Figure 3 As shown, step S4 specifically includes:

[0100] Step S41: Determine the location capture range based on the received signal;

[0101] Step S42: Design the relevant calculation thresholds and position steps;

[0102] Step S43: Perform truncation processing on the received signal based on the frequency offset estimation results of the correct despreading position and the optimal sampling point;

[0103] Step S44: Despread the intercepted received signal;

[0104] Step S45: Traverse the intercepted received signals according to the position step and obtain the peak-to-average power ratio (PAPR) of the intercepted received signals and the frame header preamble.

[0105] Step S46: Determine whether the peak value exceeds the relevant calculation threshold. If yes, capture is complete; otherwise, return to step S43.

[0106] In this embodiment, the DSP correlation operation requires a length that is an integer power of 2. During position acquisition, the received signal is truncated based on the starting position of the spreading code and the frequency offset. The correlation operation signal length is selected according to the spreading ratio and the DSP correlation operation length requirement. The spread preamble reference signal and the truncated received signal are processed according to the correlation operation signal length, and then... BPSK despreading is performed. Then, the signal position dimension is scanned by traversing the position parameters of the despread signal, and the starting position is obtained based on the relevant computational peak values ​​to complete the capture.

[0107] Through the above methods, the IoT signal processing method based on the DvB system under extremely low signal-to-noise ratio conditions provided by this invention employs rotation factor dimensionality reduction and nonlinear frequency offset compensation algorithms to reduce signal-to-noise ratio under extremely low conditions. The complexity of the received signal processing algorithm for BPSK direct spread spectrum signals. The algorithm first introduces a rotation factor to... BPSK signal dimensionality reduction effectively lowers the complexity of subsequent signal processing algorithms. Despreading the signal then enables signal feature recognition, reducing the computational complexity of determining the validity of the received signal. Finally, a square-loop nonlinear processing is applied to the despread signal to obtain a frequency offset estimate and complete frequency offset compensation. Under extremely low signal-to-noise ratio (SNR) conditions, this invention lowers the demodulation threshold by 6 dB compared to the fourth-order method. Under a -20 dB SNR condition, this invention performs 2.72 million additions and 3.62 million multiplications, while the time-frequency two-dimensional search and acquisition algorithm performs 28.04 million additions and 34.37 million multiplications, and the PMF-FFT algorithm performs 6.41 million additions and 3.36 million multiplications. Therefore, this invention reduces processing time by 90% compared to the time-frequency two-dimensional search and acquisition algorithm and by 20% compared to the PMF-FFT algorithm.

[0108] According to one aspect of the present invention, an electronic device is provided, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform an Internet of Things signal processing method based on DvB system under extremely low signal-to-noise ratio as described in any of the above technical solutions.

[0109] The processor can be a central processing unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0110] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement an Internet of Things (IoT) signal processing method based on DvB architecture under extremely low signal-to-noise ratio as described in any of the above technical solutions.

[0111] Computer-readable storage media can include any medium capable of storing or transmitting information. Examples of computer-readable storage media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and so on. Code segments can be downloaded via computer networks such as the Internet and intranets.

[0112] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0113] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0116] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for IoT signal processing based on DvB architecture under extremely low signal-to-noise ratio, characterized in that, Includes the following steps: Step S1: Extract the received signal from a preset position according to a preset length; Step S2: Using rotation factor dimensionality reduction and nonlinear frequency offset compensation algorithm, the intercepted signal is subjected to signal dimensionality reduction, signal feature recognition and square loop nonlinear frequency offset compensation to obtain the correct despreading position and frequency offset estimation results. Step S3: Based on the frequency offset estimation result, select the sampling point with the maximum value of the frequency offset estimation FFT operation result in the signal as the optimal sampling point; Step S4: Based on the frequency offset estimation results of the spreading code start position and the optimal sampling point, the start position of the received signal is captured using the correlation calculation peak method; Step S2 specifically includes: Step S21: Introduce a rotation factor to reduce the dimensionality of the intercepted signal; Step S22: Despread the signal after introducing the rotation factor. The rotation factor and despreading work together to achieve feature recognition and obtain the correct despreading position. Step S23: Based on the correct despreading position, frequency offset estimation is performed on the feature recognition result through square ring nonlinear processing; In step S21, the modulated signal is shifted from the four quadrants to the first and third quadrants by introducing a rotation factor, as shown below: in, For the signal after introducing the rotation factor, = , , , , respectively, represent extracting one bit every four bits of the signal, starting from the 1st, 2nd, 3rd, and 4th bits, until the last bit of the signal. This indicates that the real part of the signal within the parentheses is selected. This indicates that the imaginary part of the signal within the parentheses is selected; Step S22 specifically includes: Step S221: Despread the signal after introducing the rotation factor, as follows: in, SF is the length of the signal before spread spectrum, and SF is the spreading factor. This is the despread signal; Step S222: Based on the different positions of the spreading codes in the despread signal and the direct spreading sequence, several despread signal sequences are obtained, represented as follows: in This is the starting position of the spreading code for the direct spreading sequence. The starting position of the spreading code for the direct spreading sequence The corresponding despread signal, where SF represents the spreading factor; Step S223: Determine the amplitude of the multiple despread signal sequences. The starting position of the spreading code corresponding to the maximum amplitude is the correct despreading position, represented as: ； Step S224: Perform signal feature identification on the despread signal, determine the peak-to-average ratio (PAR) of the signal features, and discard invalid data if the PAR exceeds the PAR threshold.

2. The IoT signal processing method based on DvB architecture under extremely low signal-to-noise ratio according to claim 1, characterized in that, Step S23 specifically includes: Step S231: Based on the correct despreading position, perform a square operation on the feature recognition result, expressed as: in, The phase of the signal after squaring is a phase that can be linearly modeled. Step S232: Perform FFT operation on the squared signal, and extract the frequency offset based on the FFT operation result to obtain the frequency offset estimation result. The FFT operation is expressed as follows: 。 3. The IoT signal processing method based on DvB architecture under extremely low signal-to-noise ratio according to claim 2, characterized in that, Step S4 specifically includes: Step S41: Determine the location capture range based on the received signal; Step S42: Design the relevant calculation thresholds and position steps; Step S43: Perform truncation processing on the received signal based on the frequency offset estimation results of the correct despreading position and the optimal sampling point; Step S44: Despread the intercepted received signal; Step S45: Traverse the intercepted received signals according to the position step and obtain the peak-to-average power ratio (PAPR) of the intercepted received signals and the frame header preamble. Step S46: Determine whether the peak value exceeds the relevant calculation threshold. If yes, capture is complete; otherwise, return to step S43.

4. The IoT signal processing method based on DvB architecture under extremely low signal-to-noise ratio according to claim 3, characterized in that, In both step S1 and step S42, the interception processing of the received signal includes: Set the truncated signal length according to the signal-to-noise ratio requirements and the capabilities of subsequent despreading and frequency offset estimation algorithms; Based on the set signal length, the signal is captured from a preset position; In step S1, the preset position is the data frame start position, and in step S42, the preset position is the correct despreading position.

5. The IoT signal processing method based on DvB architecture under extremely low signal-to-noise ratio according to claim 4, characterized in that, Step S44 specifically includes: The length of the correlation operation signal is selected based on the spreading ratio and the DSP correlation operation length requirements. The spread preamble reference signal and the truncated received signal are processed according to the correlation operation signal length. Finally, the spread preamble reference signal and the truncated received signal are used for... BPSK despreading.

6. An electronic device, characterized in that, include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the IoT signal processing method based on the DvB system under extremely low signal-to-noise ratio as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, implement the IoT signal processing method based on the DvB system under extremely low signal-to-noise ratio as described in any one of claims 1 to 5.

Images