Signal transmission method, system and storage medium of transmitter

Abstract

The application relates to the field of radio transmission technology, in particular to a signal transmission method and system of a transmitter and a storage medium. The method comprises the following steps: obtaining an original transmission signal to be transmitted and performing filtering processing on the original transmission signal to obtain a transmission signal to be input into a power amplifier; and performing block processing on the transmission signal according to a preset signal length. The application can locate signal peak points in the time domain, identify peak value regions to be compressed, and perform nonlinear amplitude-phase joint mapping compression on the peak value regions by using a polarization constraint matrix. The nonlinear amplitude-phase joint correction path enables the signal energy to be redistributed without damaging the sideband spectrum characteristics, thereby avoiding the problem of the dynamic range expansion of the input signal of the power amplifier caused by excessive compression. The application enables the power amplifier in the transmitter to work in a lower backoff zone, and improves the conversion efficiency of the power amplifier.

Description

Technical Field

[0001] This invention relates to the field of transmitter technology, specifically to a signal transmission method, system, and storage medium for a transmitter. Background Technology

[0002] The transmitter is the core transmitting device in a wireless communication system, and the power amplifier is the most critical power-consuming component within it. Its efficiency directly impacts the transmitter's overall energy consumption, heat dissipation design, and system reliability. In a transmission system, the signals processed by the transmitter exhibit peak-to-average power ratio (PAR) characteristics, necessitating that the power amplifier operate within a large back-off region to avoid nonlinear distortion, thus significantly reducing its conversion efficiency. A key technical challenge in transmitter design is how to reduce the PAR of the power amplifier's input signal while maintaining the linearity of the transmitted signal, enabling it to operate in a higher-efficiency region.

[0003] Existing transmitter peak-to-average power ratio (PAPR) reduction schemes mainly include hard clipping, probabilistic techniques, and coding techniques. However, while hard clipping is simple to implement, it introduces severe out-of-band spectral leakage, compromising the spectral purity of the transmitted signal and requiring additional filtering, which often results in peak regeneration. Probabilistic techniques require the transmitter to transmit additional sideband information to the receiver, consuming valuable spectrum resources and making reliable transmission of sideband information difficult to guarantee in fast time-varying channels. Coding techniques, limited by the number of codewords and coding flexibility, struggle to adapt to different modulation schemes and signal bandwidth requirements. None of these schemes address the issue of transmitter power amplifier efficiency optimization by providing a transmitter signal processing method that effectively reduces PAPR without compromising signal spectral characteristics.

[0004] Therefore, it is necessary to provide a new transmitter signal transmission method that can reduce the peak-to-average power ratio of the power amplifier input signal from the transmitter source, reduce the power amplifier back-off, improve the power amplifier conversion efficiency, and at the same time maintain the spectral purity of the transmitted signal, so as to meet the requirements of modern wireless communication systems for high efficiency, low power consumption, and high linearity of transmitters.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This disclosure presents a signal transmission method, system, and storage medium for a transmitter, with the aim of overcoming at least one of the defects existing in the prior art.

[0007] To achieve the above objectives, the technical solution disclosed in this invention is as follows: According to a first aspect of the present disclosure, a signal transmission method of a transmitter is provided, the method comprising: The original transmission signal to be transmitted is acquired and filtered to obtain the transmission signal to be input into the power amplifier. The transmitted signal is divided into blocks according to a preset signal length to obtain multiple signal sub-blocks; Oversampling rate analysis is performed on each signal sub-block according to a preset power threshold to locate signal peaks in the signal sub-block that exceed the preset power threshold range, so as to identify the peak region to be compressed. A polarization constraint matrix adapted to the peak region is constructed, and nonlinear amplitude-phase joint mapping compression is performed on the peak region using the polarization constraint matrix to obtain preprocessed signal sub-blocks; The preprocessed signal sub-blocks are subjected to time-domain interleaving and signal amplitude quantization to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. The transmit baseband signal is then converted into an analog baseband signal, amplified by the power amplifier, and fed to the transmit antenna for transmission.

[0008] In an exemplary embodiment of this disclosure, the step of constructing a polarization constraint matrix adapted to the peak region, and using the polarization constraint matrix to perform nonlinear amplitude-phase joint mapping compression on the peak region to obtain a preprocessed signal sub-block, includes the following steps: Extract the amplitude envelope and phase information of the peak region, calculate the offset of the instantaneous power of the signal relative to the average power, and use the offset as a distortion metric. Based on the distortion metric, a polarized coordinate system is constructed in the complex plane, and a polarization constraint matrix is ​​generated to perform rotation and shrinkage operations on high-power points. The signal points within the peak region are mapped to the polarization constraint matrix for amplitude and phase joint correction until the maximum signal amplitude in the peak region converges to the preset power threshold range, thus obtaining the preprocessed signal sub-block.

[0009] In an exemplary embodiment of this disclosure, the step of constructing a polarized coordinate system in the complex plane based on the distortion metric includes the following steps: The minimum Euclidean distance of the original signal space map is set according to the symbol set cardinality of the transmitted signal, and the minimum Euclidean distance is used as the constraint boundary. The rotation angle and contraction ratio of the polarization coordinate system are adjusted according to the instantaneous energy density of the signal point on the complex plane so that the polarization coordinate system parameters are adapted to the power distribution of the current peak region. Calculate the distance deviation between the signal points after polarization transformation and the original signal mapping points, and use the distance deviation to correct the sparsity of the polarization constraint matrix to be generated.

[0010] In one exemplary embodiment of this disclosure, after identifying the peak region to be compressed, the method further includes the following steps: The identified peak regions are windowed and truncated, and the ratio of the main lobe energy to the side lobe energy of the truncated signal is extracted. The ratio of the main lobe energy to the side lobe energy is compared with a set energy threshold. When the ratio of the main lobe energy to the side lobe energy is lower than the energy threshold, the parameter reset process of the polarization constraint matrix is ​​triggered, and a reset polarization constraint matrix adapted to the energy distribution characteristics of the current peak region is regenerated. The peak region is repeatedly subjected to nonlinear amplitude-phase joint mapping compression using the reset polarization constraint matrix, while preserving the original signal characteristics of the non-peak region.

[0011] In an exemplary embodiment of this disclosure, the step of performing time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-block to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter, converting the transmit baseband signal into an analog baseband signal, amplifying it through the power amplifier, and then feeding it to the transmit antenna for transmission includes the following steps: In the time domain, the sample points in the preprocessed signal sub-block are repositioned according to the pseudo-random sequence pre-shared by both the sender and receiver, resulting in interleaved signal samples that break up the original continuous distribution. Based on the preset quantization bit width, the interleaved signal sample is subjected to preliminary amplitude compression mapping to constrain the continuous signal amplitude to the dynamic range corresponding to the preset quantization bit width, thereby obtaining the constrained signal. The amplitude of the constrained signal is adjusted using a preset nonlinear compression function, including: amplitude enhancement of small-amplitude signal components and amplitude attenuation of large-amplitude signal components that exceed the dynamic range, so as to obtain an amplitude-adapted quantized signal. The signal to be quantized is subjected to discrete quantization processing to obtain quantized symbol data; The quantized symbol data is organized into a fixed-length data frame, and a pilot sequence for receiving end synchronization recovery is inserted at the frame header of the data frame to obtain the transmitted baseband signal.

[0012] In an exemplary embodiment of this disclosure, the step of dividing the transmitted signal into blocks according to a preset signal length to obtain multiple signal sub-blocks includes the following steps: Detect the frame header identifier of the transmitted signal to determine the starting position of the transmitted signal; The preset signal length is determined based on the preset number of sub-signals, and the frame length of the entire transmitted signal is divided into an integer number of length-aligned signal sub-blocks to complete the block processing.

[0013] In one exemplary embodiment of this disclosure, the method further includes the following steps: Real-time monitoring of instantaneous peak-to-average ratio (PAR) during the PAR suppression process, and statistical analysis of the cumulative PAR distribution function within a preset time window; The preset power threshold is adjusted according to the cumulative peak-to-average power ratio distribution function. When the instantaneous peak-to-average power ratio (PAPR) exceeds the preset PAPR warning threshold range, the operating point of the output power amplifier is adjusted.

[0014] In an exemplary embodiment of this disclosure, the extraction of the amplitude envelope and phase information of the peak region includes the following steps: The target peak region signal is extracted and subjected to Hilbert transform to obtain the corresponding analytical signal; The instantaneous amplitude spectrum is calculated based on the analytical signal, and the amplitude envelope of the peak region is extracted. The initial instantaneous phase spectrum is calculated based on the analytical signal, and the initial instantaneous phase spectrum is unwound to obtain the phase information after the peak region correction. The instantaneous frequency deviation is calculated using the corrected phase information, and the peak spatiotemporal feature vector is constructed by combining it with the amplitude envelope for subsequent nonlinear compression processing.

[0015] According to a second aspect of the present disclosure, a signal transmission system for a transmitter is provided for implementing the signal transmission method of the transmitter as described in any of the preceding claims, the system comprising: The acquisition and filtering module is used to acquire the original transmission signal to be transmitted and to filter the original transmission signal to obtain the transmission signal to be input into the power amplifier; The signal block acquisition module is used to divide the transmitted signal into blocks according to a preset signal length to obtain multiple signal sub-blocks; The peak region identification module is used to perform oversampling rate analysis on each of the signal sub-blocks according to a preset power threshold, locate the signal peak points in the signal sub-blocks that exceed the preset power threshold range, so as to identify the peak region to be compressed. The polarization constraint mapping module is used to construct a polarization constraint matrix adapted to the peak region, and to perform nonlinear amplitude-phase joint mapping compression on the peak region using the polarization constraint matrix to obtain preprocessed signal sub-blocks. The time-domain interleaving and quantization module is used to perform time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-block to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. After converting the transmit baseband signal into an analog baseband signal, it is amplified by the power amplifier and then fed to the transmit antenna for transmission.

[0016] According to a third aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the signal transmission method of the transmitter as described in any of the preceding claims.

[0017] The beneficial effects of this invention are as follows: This invention provides a signal transmission method for a transmitter. By segmenting the transmitted signal into blocks to accurately locate the peak region, and utilizing a polarization constraint matrix adapted to the power distortion characteristics of the peak region, nonlinear amplitude-phase joint mapping compression is performed only on the peak region. This converges the peak amplitude to within a preset power threshold without altering the original signal characteristics of the non-peak regions, effectively reducing the peak-to-average power ratio (PAPR) of the transmitted signal. This allows the power amplifier to operate without operating in a large backoff region, significantly improving the power amplifier's conversion efficiency. After peak compression, time-domain interleaving is used to break down the continuous distribution of residual peak values, further stabilizing the PAPR. This enables the power amplifier in the transmitter to achieve the same linearity output at a lower backoff point, or at the same linearity. By outputting higher transmit power, the overall power consumption of the transmit link is directly reduced. Simultaneously, this invention avoids out-of-band spectrum leakage caused by traditional hard clipping, ensuring the spectral purity of the transmitted signal and meeting the stringent requirements of communication standards for adjacent channel leakage ratio and spectrum transmission template, thus reducing interference to adjacent channels. This invention can be widely applied to various transmitting devices such as fourth-generation / fifth-generation mobile communication base stations and terminals, wireless LAN access devices, and digital video broadcast transmitters. It solves the problem of peak-to-average power ratio (PAPR) limiting the efficiency of power amplifiers from the transmitter source, significantly improving the conversion efficiency of power amplifiers while ensuring the quality of the transmitted signal, reducing transmit link power consumption, and providing a reliable technical solution for the efficient and green operation of wireless communication transmitting equipment.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0019] Figure 1 This is a flowchart of the signal transmission method of the transmitter of the present invention; Figure 2 This is a schematic diagram of the amplitude probability density distribution of the transmitted signal of the present invention; Figure 3 This is a schematic diagram of the sparse energy distribution of the polarization constraint matrix of the present invention; Figure 4 This is a schematic diagram of the spatial mapping of the 64QAM signal before polarization compression transformation according to the present invention; Figure 5This is a schematic diagram of the spatial mapping of the 64QAM signal after polarization compression transformation according to the present invention; Figure 6 This is a CCDF curve illustrating the peak-to-average power ratio (PAPR) suppression effect of the present invention. Figure 7 This is a schematic diagram of the time-frequency energy distribution after windowing and truncation of the peak region in this invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0021] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0022] To address the issue of reduced power amplifier efficiency caused by excessively large signal amplitude in existing transmission systems, such as... Figure 1 As shown, this embodiment discloses a signal transmission method for a transmitter, the transmitter being equipped with a transmitting antenna and a power amplifier, the method including steps S100 to S500.

[0023] S100: Acquire the original transmission signal to be transmitted and filter the original transmission signal to obtain the transmission signal to be input into the power amplifier.

[0024] S200: The transmitted signal is divided into blocks according to the preset signal length to obtain multiple signal sub-blocks.

[0025] S300: Perform oversampling rate analysis on each signal sub-block according to the preset power threshold, locate the signal peak points in the signal sub-block that exceed the preset power threshold range, and identify the peak area to be compressed.

[0026] S400: Construct a polarization constraint matrix adapted to the peak region, and use the polarization constraint matrix to perform nonlinear amplitude-phase joint mapping compression on the peak region to obtain preprocessed signal sub-blocks.

[0027] S500: Performs time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-blocks to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. After converting the transmit baseband signal into an analog baseband signal, it is amplified by a power amplifier and then fed to the transmit antenna for transmission.

[0028] Specifically, the original transmission signal to be transmitted is first acquired and then filtered to obtain the transmission signal. This process removes noise, clutter, and harmonic components from the transmission signal, making it cleaner and preventing interference with subsequent amplification and transmission. Then, the transmission signal is divided into blocks according to a preset signal length. This divides the transmission signal into multiple signal sub-blocks with local sparsity. Block processing transforms the global amplitude distribution problem into a local peak processing problem, reducing the computational complexity of a single processing step and adapting to the requirements of subsequent polarization constraint processing for local signal sparsity.

[0029] Furthermore, oversampling rate analysis is performed on each signal sub-block according to a preset power threshold to locate signal spikes exceeding the preset power threshold range within the signal sub-block, thereby identifying the peak region to be compressed. Here, the preset power threshold range is the normal fluctuation range of signal power allowed by the transmission system, and the preset power threshold is the upper limit of the preset power threshold range. Instantaneous power exceeding the preset power threshold is considered to be outside the preset power threshold range and requires compression processing. The process for identifying the peak region to be compressed is as follows: The original time-domain samples of each signal sub-block are oversampled by 2x linear interpolation to obtain a continuous time-domain amplitude sequence with doubled resolution; the instantaneous power of each signal point in the oversampled sequence is calculated point by point, and then the calculated instantaneous power is compared with the preset power threshold point by point. The time-domain position and power value corresponding to all signal points with instantaneous power greater than the preset power threshold are recorded, thus completing the location of all signal spikes; finally, multiple signal spikes with consecutive time-domain positions are merged into a connected region, which is the peak region to be compressed.

[0030] The preset power threshold ranges from 1.2 to 2.5 times the average power of the corresponding signal sub-block, and in this embodiment, it is preferably set to 1.8 times the average power of the signal sub-block. It can be understood that the transmitted signal is generated by the superposition of multiple independent sub-signals, and the signal amplitude follows a certain statistical distribution, such as... Figure 2 As shown, the probability density distribution of the transmitted signal amplitude exhibits a high peak tail characteristic. Only a small portion of the transmitted signal peaks exceed the dynamic range of the power amplifier. Oversampling rate analysis can perform high-resolution sampling fitting of the signal amplitude and, combined with a preset power threshold, accurately locate the peak region, avoiding additional operations on signals in non-peak regions.

[0031] Furthermore, a polarization constraint matrix adapted to the peak region is constructed, and nonlinear amplitude-phase joint mapping compression is performed on the peak region using the polarization constraint matrix, so that the maximum signal amplitude in the peak region converges to the preset power threshold range. This reduces the amplitude fluctuation of the entire signal sub-block without damaging the signal sideband spectral characteristics, resulting in a preprocessed signal sub-block with a peak-to-average power ratio that meets the transmission requirements.

[0032] It is important to understand that the polarization constraint matrix only applies amplitude constraints to the peak region obtained from the localization, and has no effect on the signal in the non-peak region, such as... Figure 3 As shown, in the sparse energy distribution of the polarization constraint matrix, constraint energy exists only in the region corresponding to the peak position, while no additional constraints are applied to the remaining regions. The compressed signal mapping is as follows: Figure 4 and Figure 5 As shown, the transformed signal mapping only adjusts the amplitude of the high-power point and does not shift the signal mapping position of the low-power point, thus ensuring the correct demodulation of the transmitted signal obtained after processing.

[0033] Furthermore, the preprocessed signal sub-blocks undergo time-domain interleaving and signal amplitude quantization to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. This transmit baseband signal is then converted to an analog baseband signal, amplified by a power amplifier, and fed to the transmit antenna for transmission. Signal randomization eliminates residual peak power to reduce the transmission error rate. Time-domain interleaving disperses residual amplitude fluctuations after local compression into different transmission time slots, preventing fluctuations from accumulating and forming new peak values. Signal amplitude quantization, according to the transmission protocol, performs bit mapping on the preprocessed signal sub-blocks after peak compression and time-domain interleaving to generate the transmit baseband signal. This transmit baseband signal is then converted to an analog baseband signal, amplified by a power amplifier, and fed to the transmit antenna for transmission. The final signal obtained after step S500 of this method, i.e., the amplified baseband signal, has a peak-to-average power ratio (PAPR) characteristic that can be characterized by a complementary cumulative distribution curve, such as... Figure 6 As shown, the probability of the peak-to-average power ratio (PAPR) of the final signal after compression exceeding a specific PAPR threshold is significantly reduced. The specific PAPR threshold ranges from 6dB to 12dB, and in this embodiment, it is preferably set to 8dB. The time-frequency energy distribution of the signal after peak region processing is shown below. Figure 7 As shown, the main lobe energy of the peak region signal after windowing is concentrated, and the side lobe energy is effectively controlled, which will not have a significant impact on the overall spectral characteristics of the final output transmitted signal.

[0034] The advantage of this embodiment lies in that, through block processing combined with peak localization, polarization constraint compression is applied only to signal peaks within signal sub-blocks exceeding the preset power threshold range of the power amplifier. This reduces the peak-to-average power ratio while ensuring the spectral characteristics of the final output transmitted signal and the demodulation correctness of the signal to be demodulated at the receiving end. It is compatible with the processing framework of existing transmission systems, requires no large-scale modifications to the transmission protocol, and can be directly embedded into existing signal processing workflows. The entire processing flow is designed based on the statistical distribution characteristics of the signal itself, and the processing logic is adapted to the operational architecture of existing digital signal processing hardware, enabling real-time encoding processing and meeting the processing latency requirements of high-speed transmission systems.

[0035] In one embodiment, to address the issue that adjusting only the amplitude in existing nonlinear compression processes leads to phase distortion, thus affecting demodulation performance, this embodiment further optimizes the process by constructing a polarization constraint matrix adapted to the peak region. The polarization constraint matrix is ​​then used to perform nonlinear amplitude-phase joint mapping compression on the peak region to obtain the preprocessed signal sub-blocks. The specific steps are as follows: Extract the amplitude envelope and phase information of the peak region, calculate the offset of the instantaneous power of the signal relative to the average power, and use the offset as a distortion metric. A polarized coordinate system is constructed in the complex plane based on the distortion metric, and a polarization constraint matrix is ​​generated to perform rotation and shrinkage operations on high-power points. The signal points in the peak region are mapped to the polarization constraint matrix for amplitude and phase joint correction until the maximum signal amplitude in the peak region converges to the preset power threshold range, thus obtaining the preprocessed signal sub-block.

[0036] In one embodiment, the amplitude envelope and phase information of the peak region are extracted, the offset of the instantaneous power of the signal relative to the average power is calculated, and the offset is used as a distortion metric. The specific calculation process is as follows: the analytic signal of the peak region obtained by Hilbert transform can be expressed as... ,in, Indicates an analytical signal. For the amplitude envelope in the first... The value of each signal point For the corrected phase information in the first... The value of each signal point Represents an imaginary number; calculates the first imaginary number. Instantaneous power of each signal point , Indicates the first The instantaneous power of each signal point is calculated in advance, and then the average power of the signal sub-block to which the peak region belongs is calculated in advance. Finally, the number was obtained. The offset of the instantaneous power of each signal point relative to the average power , Indicates the first The deviation of the instantaneous power of each signal point from the average power, As a measure of the distortion of that signal point. Figure 2 As shown, the amplitude probability density distribution of the transmitted signal exhibits a high peak tail characteristic, with only a small portion of the signal having a large power offset. Therefore, the distortion metric only needs to be calculated for the peak region after positioning, without traversing the entire signal sub-block, thus reducing the overall computational load.

[0037] In one embodiment, a polarimetric coordinate system is constructed in the complex plane based on a distortion metric, and a polarimetric constraint matrix is ​​generated to perform rotation and shrinkage operations on high-power points. The specific implementation process is as follows: 1. Constructing a polarimetric coordinate system: First, based on the symbol set cardinality of the current transmitted signal, determine the minimum Euclidean distance of the original signal spatial mapping map. Set half of the minimum Euclidean distance as the constraint boundary of the polarimetric coordinate system to prevent the corrected signal mapping points from crossing the boundary and intruding into the decision region of adjacent signal mappings. Then, for each signal point in the peak region, adjust the polarimetric coordinate system parameters according to its distortion metric.

[0038] 2. Generate polarization constraint matrix: Initialize an empty matrix with the same dimension as the complex signal of the entire signal sub-block, and set all elements to zero initially; only for each signal point with positive power offset in the peak region, fill the corresponding rotation transformation matrix into the corresponding coordinate position of the polarization constraint matrix, and keep the position of the non-peak region at the initial zero value.

[0039] The rotation transformation matrix is ​​expressed as: in, Represents the rotation transformation matrix. The shrinkage ratio is represented by the distance deviation between the transformed signal point and the original signal mapping point. After normalizing the distance deviation to the [0,1] interval, the weights of the corresponding position rotation transformation matrix are corrected to obtain the final sparseness-fitting polarization constraint matrix.

[0040] It needs to be explained that high-power points are time-domain signal points in the transmitted signal where the instantaneous power is significantly higher than the average power, exceeding the transmitter's preset power threshold. The transmitted signal is composed of multiple independent orthogonal sub-signals superimposed. When these sub-signals are in phase, power superposition occurs, generating these high-power points with instantaneous power far exceeding the average power. This is the core reason for increasing the overall signal peak-to-average power ratio (PAPR). In this invention, high-power points constitute only a very small portion of all signal points; only compression correction is needed for these points, without affecting the overall signal quality. Figure 3 As shown, in the sparse energy distribution of the polarization constraint matrix, constraint energy exists only in the region corresponding to the peak position, while the remaining regions retain their initial zero values ​​and do not affect the signal in non-peak regions. The polarization constraint matrix generates constraint energy only at the coordinate positions corresponding to the detected peaks, and the sparse energy distribution characteristics conform to the local distribution characteristics of signal peaks. It should be understood that conventional compression methods only hard-truncate the amplitude, which changes the phase information of the signal and introduces additional in-band interference. The polarization constraint matrix in this embodiment acts on both amplitude and phase, adapting the original amplitude-phase relationship of the signal points through rotation and shrinkage operations, thus avoiding distortion of phase information.

[0041] Furthermore, signal points within the peak region are mapped to the polarization constraint matrix for amplitude and phase joint correction until the maximum signal amplitude in the peak region converges to a preset power threshold. After correction, all signal points are written back to the signal sub-block in their original positions, resulting in a preprocessed signal sub-block with peak compression completed. Energy equalization is achieved through nonlinear transformation. Figure 4 and Figure 5 As shown, in the comparison of 64 Quadrature Amplitude Modulation (64QAM) signal mapping before and after polarization compression transformation, only the high-power signal points experience amplitude contraction, while the relative positional relationship of the original signal points remains stable, thus avoiding additional interference to the signal mapping point decision during demodulation. The entire mapping compression process adjusts the constraint strength of the corresponding positions of the polarization constraint matrix based on the local signal characteristics of the peak region. It does not require applying a uniform transformation to the entire signal sub-block, and the computational load is concentrated in the local region. This adapts to the parallel computing architecture of digital signal processing hardware, enabling real-time processing.

[0042] The advantage of this embodiment is that by jointly correcting the amplitude and phase in the peak region, the relative relationship of the original phase information of the transmitted signal is preserved while the peak-to-average power ratio is compressed. This avoids the additional demodulation error introduced by single-dimensional amplitude adjustment. The energy achieves an overall balanced distribution through nonlinear transformation, without changing the original characteristics of the signal in the non-peak region, thus adapting to the transmitter's signal processing requirements.

[0043] In one embodiment, to address the issues of decision boundary ambiguity caused by signal mapping point offset during polarization transformation and high constraint matrix complexity, this embodiment further optimizes the construction process of building a polarization coordinate system in the complex plane based on distortion metrics, specifically including the following: The minimum Euclidean distance of the original signal space map is set according to the symbol set cardinality of the transmitted signal, and the minimum Euclidean distance is used as the constraint boundary. The rotation angle and contraction ratio of the polarization coordinate system are adjusted according to the instantaneous energy density of the signal point on the complex plane so that the parameters of the polarization coordinate system are adapted to the power distribution of the current peak region. Calculate the distance deviation between the polarization transformed signal points and the original signal mapping points, and use the distance deviation to correct the sparsity of the polarization constraint matrix to be generated.

[0044] In one embodiment, the minimum Euclidean distance of the original signal space map is set as a constraint boundary based on the symbol set cardinality of the transmitted signal to prevent amplitude correction from causing ambiguity in the decision boundary of the signal mapping points. This solution is adaptable to various orthogonal amplitude symbol set cardinities commonly used in transmission systems, specifically including 4, 16, 64, 256, and 1024. It is understood that signal space maps with different symbol set cardinities have different point spacings. The higher the symbol set cardinality, the smaller the minimum Euclidean distance between signal mapping points in the 64QAM signal space map, and the easier it is for amplitude adjustment to cause overlap of adjacent signal mapping points. Therefore, this embodiment uses the minimum Euclidean distance as a constraint boundary, limiting the corrected distance between any two signal points to no less than this value, ensuring the correctness of the signal mapping decision.

[0045] Furthermore, the rotation angle and contraction ratio of the polarization coordinate system are adjusted based on the instantaneous energy density of the signal point in the complex plane to maintain the original signal mapping structure unchanged in the low-power region. The specific adjustment steps are as follows: 1. Calculate the peak value within the peak region. Instantaneous energy density of each signal point ,in, Indicates the first Instantaneous energy density at each signal point For the first The instantaneous power of each signal point For the first The average power of each signal point; 2. Calculate the shrinkage ratio: If ,in To preset the power threshold, The value range is the preset power threshold range, indicating the first... If the instantaneous power of a signal point exceeds a preset power threshold, it needs to be reduced. The formula for calculating the reduction ratio is: in, For the first The distance from each corrected signal point to its nearest neighbor in the original signal mapping. The minimum Euclidean distance of the original signal space map under the current symbol set cardinality; if calculated... If <0.5, then the value is 0.5 to prevent excessive contraction from causing signal mapping offset; if ,but =1, keep the original amplitude unchanged; 3. Calculate the rotation angle: Preserve the sign of the original phase of the signal point. The formula for calculating the rotation angle is: in, For the corrected phase information in the first... The value of each signal point For the first The original phase of each signal point is set, with π set to 3.1415, enabling adaptive adjustment of the phase correction amplitude based on energy density. For example... Figure 4 and Figure 5 As shown, the positions of low-power signal points in the compressed signal space mapping are completely consistent with the original signal mapping. Only the high-power points with high energy density are shrunk. This adjustment method ensures that most low-power signals will not introduce additional correction errors, reducing the impact of the compression process on the overall signal-to-noise ratio.

[0046] Furthermore, the distance deviation between the polarization-transformed signal points and the original signal mapping points is calculated. This distance deviation is used to correct the sparsity of the polarization constraint matrix to be generated, balancing computational complexity and suppression performance. The specific correction process is as follows: First, the distance deviation from each transformed signal point within the peak region to its corresponding original signal mapping point is calculated. Then for all The global average distance deviation is obtained by averaging. In this embodiment, the sparsity of the polarization constraint matrix is ​​defined as the proportion of zero elements in the matrix to the total number of elements. Higher sparsity indicates fewer non-zero constraint elements, resulting in lower computational and storage overhead. The distance deviation threshold is set... Set as the minimum Euclidean distance of the original signal space map under the current symbol set cardinality. 1 / 4, that is .

[0047] when When the overall transformation deviation is small, only a few constraint points are needed to meet the compression requirements. The correction rule then becomes: adjust the distance deviation of all individual signal points within the peak region. The corresponding constraint matrix elements are set to zero, and redundant constraints on deviation target points are removed. The sparsity of the corrected polarization constraint matrix is ​​increased by 10%-15%, reducing unnecessary computational overhead. when If the polarization constraint matrix is ​​insufficient to constrain the high-power points in the peak region, the correction rule is to retain the non-zero constraint elements corresponding to all signal points with power offset greater than 0 in the peak region, and at the same time supplement the non-zero constraints corresponding to the signal points whose power at the edge of the peak region is close to the preset power threshold range. The sparsity of the corrected polarization constraint matrix is ​​reduced by 5%-10%, and the compression correction effect is improved by increasing the effective constraint points.

[0048] It's important to understand that the corrected polarization constraint matrix retains only the non-zero energy at the peak position, while keeping the rest at zero, exhibiting a sparse distribution. This sparse matrix operation reduces storage and computational overhead, adapting to the resource constraints of embedded processing platforms. For applications involving peak-to-average power ratio (PAPR) compression of transmitted signals, in addition to high-capacity processing at the base station, real-time processing requirements for embedded devices such as IoT terminals and mobile terminals must be met. These platforms generally have limited computational and storage resources. The optimized polarization constraint matrix in this solution perfectly suits these resource constraints, expanding the applicability of this solution. The entire construction process adjusts four core parameters—polarization coordinate system constraint boundaries, rotation angle of individual signal points, shrinkage ratio, and sparsity of the polarization constraint matrix—based on the characteristics and local energy distribution of the peak region signal points within each signal sub-block after the transmitted signal is divided. This eliminates the need for pre-setting a fixed transformation matrix, adapting to input signals with different PAPR distributions.

[0049] It should be explained that the polarization transformation in this embodiment is a nonlinear amplitude and phase adjustment operation designed for high-power points in the peak region. Specifically, based on the original signal points in the original complex plane signal mapping coordinate system, a nonlinear mapping of rotation and contraction is performed on the signal points using a polarization constraint matrix. This contracts the amplitude of high-power points that originally exceeded a preset power threshold towards the signal mapping origin, and slightly adjusts the phase if necessary. The signal point at the new coordinate position obtained after this nonlinear transformation is the polarized signal point. It can be understood that only high-power points in the peak region undergo polarization transformation; low-power non-peak signal points remain in their original positions. Therefore, the polarized signal reduces the peak-to-average power ratio to the required range while preserving the characteristics of the original signal to the greatest extent possible, without introducing excessive additional distortion.

[0050] The advantage of this embodiment is that the separability of the signal mapping points after amplitude correction is guaranteed by the minimum Euclidean distance constraint. Here, separability refers to the characteristic that different signal mapping points can be correctly distinguished during demodulation at the receiving end: after the minimum Euclidean distance constraint, the Euclidean distance between any two adjacent signal mapping points after amplitude correction is still greater than the minimum constraint boundary, and signal mapping points will not overlap or alias. This ensures that the signal mapping decision module at the receiving end can correctly classify each signal point into the corresponding signal mapping category, avoiding the increase in bit error rate caused by the overlap of adjacent signal mapping points. By adjusting the sparsity of the polarization constraint matrix through distance deviation, the computational complexity is controlled while ensuring compression performance. It can be adapted to transmission systems with different transmission rates, does not require modification of the original framework of baseband signal processing, and can be directly embedded into existing processes.

[0051] In one embodiment, to address the problem of misidentifying non-dominant peaks during peak region identification, leading to excessive compression and in-band signal-to-noise ratio loss, this embodiment further optimizes the preprocessing workflow after identifying the peak regions to be compressed, specifically including the following: The identified peak regions are windowed and truncated, and the ratio of the main lobe energy to the side lobe energy of the truncated signal is extracted. The ratio of main lobe energy to side lobe energy is compared with a set energy threshold. When the ratio of the main lobe energy to the side lobe energy is lower than the energy threshold, the parameter reset process of the polarization constraint matrix is ​​triggered, and a reset polarization constraint matrix adapted to the energy distribution characteristics of the current peak region is regenerated. By repeatedly performing nonlinear amplitude-phase joint mapping compression on the peak region using the reset polarization constraint matrix, the original signal characteristics of the non-peak region are preserved.

[0052] In one embodiment, the identified peak region is windowed and truncated to extract the ratio of the main lobe energy to the side lobe energy of the truncated signal, which is used to evaluate the temporal concentration of the peak signal. The specific technical process is as follows: First, taking the point with the maximum detected peak amplitude as the center, a fixed number of signal points are extended to both sides of the signal to select the processing area that completely covers the candidate peak. Then, a Hanning window is applied to this area, and the signal outside the window is set to zero to obtain the truncated peak window signal. Subsequently, a Fourier transform is performed on the truncated peak window signal to obtain the frequency domain energy distribution. The continuous 3dB bandwidth range with the highest energy amplitude is defined as the main lobe region, and the entire frequency band range outside the main lobe region is defined as the side lobe region. The total energy of all points in the main lobe region and the total energy of all points in the side lobe region are accumulated and counted respectively to calculate the ratio of the main lobe energy to the side lobe energy, thus completing the energy distribution feature extraction. It is understandable that the signal peaks that actually need compression belong to energy concentrated in the time domain. After windowing and truncation, the ratio of main lobe energy to side lobe energy is high. Conversely, non-dominant amplitude fluctuations caused by noise or superposition of multiple signals have dispersed energy distribution, resulting in a low ratio of main lobe energy to side lobe energy. This ratio can effectively distinguish between the dominant peaks and non-dominant amplitude fluctuations that actually need compression. Figure 7 As shown, in the time-frequency energy distribution after windowing and truncation in the peak region, the energy is concentrated in the main lobe region and the energy proportion in the side lobe region is low, which is consistent with the energy distribution characteristics of the true dominant peak.

[0053] Furthermore, the calculated ratio of main lobe energy to side lobe energy is compared with a set energy threshold; When the ratio of the main lobe energy to the side lobe energy is lower than a set energy threshold, it indicates that the current candidate peak is a non-dominant peak, triggering a parameter reset process at the corresponding position of the polarization constraint matrix. The parameters to be reset include two core constraint parameters in the polarization constraint matrix: the shrinkage ratio parameter and the rotation angle parameter at the coordinate position corresponding to the peak region. The specific parameter reset process is as follows: all non-zero constraint parameters corresponding to the coordinates of the peak region are cleared to zero, restoring the corresponding position of the polarization constraint matrix to its initial zero value, and no further compression constraints are applied to the non-dominant peak region. This process effectively avoids excessive compression of non-dominant peaks, which could lead to in-band signal-to-noise ratio loss. It's important to understand that non-dominant peaks have low energy contribution, and even without compression, they do not significantly affect the overall peak-to-average power ratio. Compression of non-dominant peaks can introduce additional signal distortion and reduce the overall signal-to-noise ratio. Therefore, constraint parameters at the corresponding position are only retained when the peak is determined to be the true dominant peak.

[0054] Furthermore, nonlinear amplitude-phase joint mapping compression is repeatedly performed in the peak region using the reset polarization constraint matrix to preserve the original signal characteristics in the non-peak region. The entire preprocessing process performs a secondary screening of the initially identified peaks before the compression operation, eliminating non-dominant peaks that do not need compression, reducing unnecessary transformation operations, lowering computational load, and avoiding additional signal distortion. The peak-to-average power ratio (PAPR) of the processed signal can be characterized by complementary cumulative distribution curves, such as... Figure 6 As shown, the probability of the compressed signal exceeding the preset peak-to-average ratio threshold is significantly reduced. At the same time, since only the true dominant peak value is compressed, the in-band signal-to-noise ratio loss is controlled within a reasonable range.

[0055] The advantage of this embodiment is that by evaluating the ratio of the main lobe energy to the side lobe energy obtained after windowing and truncation, a secondary screening of peak values ​​is completed, distinguishing between the dominant peaks that need to be compressed and the non-dominant peaks that do not need to be processed. This avoids the signal-to-noise ratio loss introduced by excessive compression, while reducing unnecessary constraint transformation operations and lowering the overall computational load. While ensuring the peak-to-average power ratio compression effect, it maintains the overall transmission quality of the transmitted signal and adapts to the amplitude distribution characteristics of the transmitted signal in actual transmission scenarios.

[0056] In one embodiment, to address the issues of continuous distribution of residual high-power signal samples after peak compression of the signal sub-blocks, and insufficient signal-to-noise ratio and difficulty in receiving synchronization of small-amplitude signals during quantization, this embodiment further optimizes the steps of performing time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-blocks to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter, converting the transmit baseband signal into an analog baseband signal, amplifying it through a power amplifier, and then feeding it to the transmit antenna for transmission. Specifically, this includes the following steps: In the time domain, the sample points in the preprocessed signal sub-block are repositioned according to the pseudo-random sequence pre-shared by both the sender and receiver, resulting in interleaved signal samples that break up the original continuous distribution. Based on the preset quantization bit width, the interleaved signal sample is initially compressed and mapped to constrain the amplitude of the continuous signal to the dynamic range corresponding to the preset quantization bit width, thus obtaining the constrained signal. The amplitude of the constrained signal is adjusted using a preset nonlinear compression function, including: amplitude enhancement of small-amplitude signal components and amplitude attenuation of large-amplitude signal components that exceed the dynamic range, so as to obtain an amplitude-adapted quantized signal. Perform discrete quantization on the signal to be quantized to obtain quantized symbolic data; The quantized symbol data is organized into fixed-length data frames, and a pilot sequence for receiving synchronization recovery is inserted at the frame header to obtain the transmitted baseband signal.

[0057] Specifically, in the time domain, the sample points within the preprocessed signal sub-block are repositioned according to a pre-agreed pseudo-random sequence, thus dispersing the distribution of residual high-power continuous signal samples. Even after peak compression, the preprocessed signal sub-block may still contain localized clusters of continuous high-power signal samples. Such clustering can lead to energy distribution imbalance during subsequent quantization. Pseudo-random permutation disperses these continuously distributed high-power signal samples to different time-domain locations, preventing unreasonable occupation of the quantization interval by continuous high-amplitude signal samples and preserving the overall amplitude statistics of the compressed preprocessed signal sub-block.

[0058] Furthermore, the preset quantization bit width used in this quantization process is first clarified: the preset quantization bit width refers to the number of binary bits used to represent the amplitude value of a single signal sample in the quantization process. The preset quantization bit width determines the total number of discrete quantization intervals that can be divided in the quantization process. Then, based on the currently determined preset quantization bit width, amplitude compression mapping is performed on all signal samples after time-domain interleaving. The specific implementation method is as follows: first, the maximum amplitude value of all signal samples after interleaving is counted, and based on this, a normalized reference interval corresponding to the total range of the current preset quantization bit width is defined. Then, the amplitude is mapped one by one for each signal sample through a preset nonlinear compression function. The specific adjustment rule is as follows: an amplitude boundary threshold is set according to the preset quantization bit width. For small amplitude signal samples with amplitudes less than the amplitude boundary threshold, the amplitude value is directly amplified by a fixed ratio to complete the enhancement, ensuring that small amplitude signal samples occupy more discrete intervals in the quantization interval. For large amplitude signal samples with amplitudes greater than or equal to the amplitude boundary threshold, the larger the amplitude, the greater the amplitude attenuation. Finally, the amplitudes of all signal samples are normalized to the predefined normalized reference interval, completing the entire amplitude compression mapping, improving the quantization signal-to-noise ratio and reducing particle noise.

[0059] It is understandable that the distribution of quantization noise in the quantization process is directly related to the distribution of signal sample amplitudes, and the probability density of the original signal amplitude exhibits obvious tailing characteristics, such as... Figure 2 As shown, most low-amplitude signal samples are concentrated in the low-amplitude range, while a small number of high-amplitude signal samples occupy a large portion of the quantization range, resulting in a large quantization interval for low-amplitude signal samples and introducing more granular noise. By mapping with a preset nonlinear compression function, the amplitude distribution can be adjusted, stretching the proportion of the quantization range for low-amplitude signal samples and compressing the proportion of the quantization range for high-amplitude signal samples, thereby optimizing the quantization signal-to-noise ratio performance under a fixed preset quantization bit width.

[0060] Furthermore, the quantized symbol data is organized into fixed-length data frames, and a pilot sequence for synchronization recovery at the receiver is inserted at the frame header. The symbol data is multi-signal symbol data, and the data frame is a multi-signal data frame. The pilot sequence is a fixed-structure pseudo-random sequence pre-agreed upon by both the transmitter and receiver (i.e., the transmitter and receiver). The core purpose of inserting the pilot sequence is to enable the receiver to quickly establish time-domain and frequency-domain synchronization, reducing the difficulty of synchronization processing.

[0061] It's important to understand that when the receiver completes decoding and recovery, it first needs to synchronize the time and frequency domains to correctly demodulate the transmitted signal. After inserting a pilot sequence with a fixed structure, the receiver can quickly capture the frame start position of the current multi-signal data frame through sliding correlation, thereby completing frequency offset estimation and correction. This eliminates the need to traverse the entire data stream to search for synchronization points, reducing the receiver's synchronization processing latency. After synchronization, the receiver generates an inverse permutation matrix using the same pseudo-random sequence as the transmitter. The inverse permutation matrix is ​​a position mapping matrix that is completely reversed from the permutation rule used during time-domain interleaving at the transmitter. Its function is to restore the scrambled signal samples at the transmitter to their original time-domain arrangement. After generating the inverse permutation matrix, the original time-domain positions of the signal samples can be restored. The entire inverse process does not introduce additional amplitude distortion, maintaining the original peak-to-average power ratio compression effect.

[0062] The advantage of this embodiment is that by disrupting the continuous distribution characteristics of residual high-power signal samples through time-domain interleaving, optimizing the noise distribution of the quantization process by combining nonlinear amplitude mapping, and adding pilot-assisted frame structure design, it can improve the transmission reliability of the quantized signal, reduce the synchronization processing complexity at the receiver, and adapt to the transmission link requirements of transmitters with fixed bit widths.

[0063] In one embodiment, to address the issue of misaligned input signal block boundaries causing bit error data to affect the effectiveness of subsequent peak-to-average power ratio (PAPR) compression processing, this embodiment further refines the steps of dividing the transmitted signal into blocks according to a preset signal length to obtain multiple signal sub-blocks, specifically including the following: Detect the frame header identifier of the transmitted signal to determine the starting position of the transmitted signal; The preset signal length is determined based on the preset number of sub-signals. The frame length of the entire transmitted signal is divided into an integer number of length-aligned signal sub-blocks to complete the block processing.

[0064] Specifically, the frame header identifier of the transmitted signal is first detected to determine the signal's starting position. This frame header identifier is a fixed-structure pseudo-random sequence pre-agreed upon by the transmitter and receiver and placed at the very beginning of each transmitted signal frame, specifically used to mark the signal's starting boundary. The specific implementation for determining the starting position is as follows: when transmitting the signal, the transmitter places the fixed frame header identifier at the very beginning of the entire signal. The receiver performs a sliding correlation operation between the locally stored identical frame header identifier and the received data stream. When the correlation operation result shows a correlation peak significantly higher than the noise level, the position corresponding to this correlation peak is the starting position of the input signal. Subsequently, based on a preset number of sub-signals, starting from the determined starting position, the frame length is divided into signal sub-blocks that are integer multiples of the signal length, ensuring that each signal sub-block contains a complete orthogonal sub-signal period.

[0065] Understandably, the orthogonality of the transmitted signal is based on complete sub-signal periods. If the block boundary crosses a sub-signal period, the orthogonality of the sub-signals will be destroyed. Subsequent peak region identification will then be unable to accurately calculate the signal energy distribution within the sub-block, thus affecting the compression effect. By dividing the signal into integer multiples of a preset number of sub-signals, it can be ensured that each signal sub-block contains an integer number of complete orthogonal sub-signal periods, maintaining the orthogonality of the sub-signals within the sub-block. The entire block division process starts directly from a predetermined starting position and divides according to a preset signal length, eliminating the need for additional boundary searches and reducing the computational complexity of the block division process.

[0066] Furthermore, a cyclic redundancy check (CRC) is performed on each signal sub-block to detect transmission errors. The specific process is as follows: When generating each signal sub-block, the transmitting end calculates the CRC code corresponding to the current signal sub-block according to a pre-agreed generator polynomial, appends the check code to the end of the corresponding signal sub-block, and sends it along with the signal. After completing the block division, the receiving end recalculates the CRC code for each signal sub-block using the same generator polynomial, and then compares the recalculated check code with the check code generated by the transmitting end carried at the end of the signal sub-block bit by bit. If all bits match perfectly, the current signal sub-block is determined to have passed the check and there are no transmission errors. Only the signal sub-blocks that have passed the check are subjected to subsequent peak identification and compression operations. If any bit does not match, the current signal sub-block is determined to have failed the check and there are transmission errors. Subsequent compression operations are not performed directly. This rule prevents erroneous data from affecting statistical characteristic analysis. It's important to understand that bit errors introduced during transmission alter the amplitude distribution of signal sub-blocks, causing the statistically obtained peak positions and amplitudes to deviate from their actual values. Incorrect compression operations can introduce additional signal distortion, negatively impacting signal transmission quality. Performing subsequent operations only on verified signal sub-blocks filters out signal data contaminated by bit errors, preventing the impact of erroneous processing on the overall signal frame quality. The subsequent polarization constraint matrix construction process relies on accurate signal statistics; only correct and reliable input data can ensure that the sparse energy distribution of the polarization constraint matrix conforms to the actual signal characteristics, such as... Figure 3 As shown, the regular energy distribution is based on reliable input data.

[0067] Furthermore, for signal sub-blocks that fail verification, forward error correction coding or a request-retransmission mechanism is selected based on the application scenario. Forward error correction coding is an error handling method where the sender adds redundant error correction bits in advance, and the receiver can correct the errors itself. When the sender transmits a signal sub-block, it adds redundant error correction bits in addition to the information bits. After the receiver detects a sub-block error, it can directly use the redundant bits to locate and correct the error. After correction, the sub-block is sent to the subsequent processing flow without needing to send a request back to the sender. The request-retransmission mechanism is an error handling method that obtains correct data through feedback. After the receiver detects that a sub-block verification fails, it sends a retransmission request to the sender through the feedback channel, requesting the sender to retransmit the erroneous signal sub-block. The receiver discards the original erroneous sub-block and uses the re-received correct sub-block to send to the subsequent processing, ensuring the reliability of the input data stream from the source. Among them, forward error correction coding does not require a return transmission wait, making it suitable for real-time voice and video transmission systems with high real-time requirements; the request retransmission mechanism can obtain completely correct data from the source, making it suitable for IoT sensing data transmission, control signaling transmission, and other systems with high reliability requirements. Both processing methods can adapt to the system design requirements of the corresponding scenarios.

[0068] This embodiment optimizes the block processing process, ensuring data reliability in the subsequent peak-to-average power ratio (PAPR) compression process from the input level. It avoids additional signal distortion introduced by block boundary misalignment and bit error data, maintains the inherent orthogonality of the signal, and ensures the stable operation of the entire PAPR suppression process.

[0069] In one embodiment, to address the problem that a fixed peak-to-average power ratio (PAPR) compression threshold cannot adapt to changes in signal PAPR under different channel environments, and that the operating point of the back-end power amplifier cannot match the actual signal dynamic range, this embodiment further refines the adaptive adjustment steps in the PAPR suppression process, specifically including the following: Real-time monitoring of instantaneous peak-to-average ratio (PAR) during the PAR suppression process, and statistical analysis of the cumulative PAR distribution function within a preset time window; Adjust the preset power threshold based on the cumulative distribution function of peak-to-average power ratio; When the instantaneous peak-to-average power ratio (PAPR) exceeds the preset PAPR warning threshold, the operating point of the output power amplifier is adjusted.

[0070] Specifically, the instantaneous peak-to-average ratio (PAR) of the transmitted signal is monitored in real time during PAR suppression, and the cumulative PAR distribution function within a preset time window is statistically analyzed to assess the dynamic range of the transmitted signal under the current channel environment. It is understandable that the statistical characteristics of the PAR of the transmitted signal differ under different channel environments, and a fixed PAR compression threshold cannot adapt to all scenarios. By sliding the statistical PAR distribution function within a preset time window, the statistical characteristics of the current transmitted signal's PAR can be obtained in real time: a higher probability of high-amplitude peaks indicates a larger actual dynamic range of the current transmitted signal; a lower probability of high-amplitude peaks indicates a smaller actual dynamic range. This statistical result accurately reflects the actual dynamic range of the current signal, providing a statistical basis for subsequent threshold adjustments. The length of the preset sliding time window can be pre-set according to the frame structure of the transmitted signal, covering multiple complete signal frames. This ensures that the statistically obtained cumulative PAR distribution function reflects the long-term statistical characteristics of the current channel, avoiding frequent threshold adjustments due to instantaneous fluctuations and maintaining the stability of the compression process.

[0071] Furthermore, the preset peak-to-average ratio (PAR) compression threshold is adjusted based on the statistically obtained PAR cumulative distribution function to adaptively match the transmitter's signal characteristics. The specific technical process is as follows: First, a PAR baseline value covering the vast majority of transmitted signal samples is extracted from the PAR cumulative distribution function. This baseline value is then compared with the currently used PAR compression threshold. If the baseline value is higher than the original PAR compression threshold, it indicates that the overall PAR level of the transmitted signal is higher, corresponding to a larger actual dynamic range. The PAR compression threshold is then appropriately raised to avoid excessive compression and unnecessary signal distortion. If the baseline value is lower than the original PAR compression threshold, it indicates that the overall PAR level of the transmitted signal is lower, corresponding to a smaller actual dynamic range. The PAR compression threshold is then appropriately lowered to ensure that the required PAR suppression effect is achieved. It is understandable that the PAR cumulative distribution function, which reflects the statistical distribution of the probability of a signal exceeding a corresponding PAR value, can clearly reflect the signal probability distribution under different PARs. Figure 6 As shown, different peak-to-average ratio (PAR) compression thresholds correspond to different PAR cumulative distribution function (CCDF) curve trends. By observing the changes in the CCDF, the current overall PAR level can be automatically determined. The entire adjustment process does not require manual intervention and can automatically adapt to changes in signal statistical characteristics.

[0072] Furthermore, when the peak-to-average ratio (PAR) exceeds the preset PAR warning threshold, the operating point of the output power amplifier is adjusted to protect the downstream RF devices from large signal impacts and maintain linearity. In this scheme, the input signal refers to the transmitted signal fed into the PAR suppression process. Large peak signals refer to peak samples of the transmitted signal whose amplitude is much higher than the average amplitude of the transmitted signal and whose amplitude exceeds the linear operating range of the downstream power amplifier. It is important to understand that the linear operating range of the power amplifier is limited. When the PAR of the transmitted signal exceeds the design range, large peak signals will push the power amplifier into the nonlinear region, which will not only generate out-of-band radiation but also cause signal demodulation errors and even damage the devices. By monitoring the PAR in real time, when it exceeds the preset PAR warning threshold, the operating point of the output power amplifier is adjusted in a timely manner to reduce the gain, keeping the signal peak within the linear range of the power amplifier, avoiding large signal impacts on the devices, and maintaining the linear operating characteristics of the power amplifier. When the PAR exceeds the preset PAR warning threshold, while adjusting the operating point of the output power amplifier, the signal energy distribution can also be further optimized in conjunction with peak region processing, such as... Figure 7 As shown, the time-frequency energy distribution in the peak region is more uniform after processing, reducing the overall peak amplitude. Combined with operating point adjustment, a stable output signal effect can be obtained for the entire transmission system.

[0073] The advantage of this embodiment is that by monitoring the peak-to-average power ratio (PAPR) statistical characteristics in real time, the compression threshold can be dynamically adjusted. At the same time, in conjunction with the feedback adjustment of the operating point of the back-end power amplifier, it can adapt to the signal characteristics under different transmission scenarios. While ensuring the PAPR suppression effect, it protects the back-end RF devices and maintains the linear characteristics of the entire transmission system.

[0074] It should be noted that the peak-to-average ratio (PAR) is an evaluation metric used to measure whether the PAR level of the transmitted signal meets the transmission requirements. When the actual PAR value of the transmitted signal is lower than the maximum allowable PAR threshold required by the transmission system, the PAR requirement is met.

[0075] In one embodiment, to address the issues of inaccurate peak feature extraction and insufficient peak suppression targeting in frequency-selective fading channels, this embodiment further refines the steps for extracting the amplitude envelope and phase information of the peak region, specifically including the following: The target peak region signal is extracted and subjected to Hilbert transform to obtain the corresponding analytical signal; The instantaneous amplitude spectrum is calculated based on the analytic signal, and the amplitude envelope of the peak region is extracted. The initial instantaneous phase spectrum is calculated based on the analytical signal. The initial instantaneous phase spectrum is then unwound to obtain the phase information after peak region correction. The instantaneous frequency deviation is calculated using the corrected phase information, and the peak spatiotemporal feature vector is constructed by combining it with the amplitude envelope for subsequent nonlinear compression processing.

[0076] Specifically, a Hilbert transform is performed on the extracted target peak region signal to obtain the corresponding analytic signal. Then, based on the analytic signal, the instantaneous amplitude spectrum and the initial instantaneous phase spectrum are calculated, thus accurately extracting the signal's transient features. It's important to understand that the analytic signal constructed by the Hilbert transform removes negative frequency components, preserving the original signal's amplitude and phase information while directly separating instantaneous feature parameters. This avoids the computational errors introduced by traditional direct extraction methods. In this embodiment, the Hilbert transform calculation is performed in the time domain, requiring no additional frequency domain transformation operations. This allows for direct adaptation to existing transmitted signal processing workflows without requiring modifications to the overall processing architecture.

[0077] Furthermore, the calculated initial instantaneous phase spectrum is unwrapped to eliminate phase jumps that occur during phase calculation. The instantaneous frequency deviation is then calculated using the unwrapped instantaneous phase spectrum (i.e., the corrected phase information) to help identify peak position drift caused by Doppler frequency shift in the channel. The unwrapping process employs a gradient search-based phase continuity method, which can eliminate phase jumps that are multiples of 2π while ensuring computational efficiency, obtaining continuously changing phase data and providing an accurate basis for calculating the instantaneous frequency deviation. It is understandable that in mobile transmission scenarios, Doppler frequency shift changes the frequency distribution of the transmitted signal, causing signal energy that was originally below the preset peak-to-average power ratio (PAPR) compression threshold to shift to the peak region, or causing the original peak position to shift. If this shift is not identified in advance, it will lead to inaccurate range of subsequent polarization constraint processing, introducing unnecessary signal distortion. The calculation of the instantaneous frequency deviation can directly reflect the degree of frequency domain shift, providing a basis for peak region correction.

[0078] It should be noted that the definitions, functions, and application scenarios of the preset peak-to-average ratio (PAR) compression judgment threshold, PAR compression threshold, and preset PAR warning threshold range are significantly different. The specific distinctions are as follows: Preset peak-to-average power ratio (PAPR) compression threshold: This is a threshold for judging a single signal point in the peak compression processing stage. Its function is to determine whether a single signal point belongs to the peak region that needs to be compressed. It is a criterion for judging the power / amplitude of a single signal sample and solves the problem of which signal points need to be compressed.

[0079] Peak-to-average ratio (PAR) compression threshold: In this embodiment, the adaptive adjustment object is the overall PAR level of the entire frame / the entire signal sub-block. Its function is to set the maximum PAR requirement that the entire signal needs to meet after compression processing, so as to adapt to the overall PAR statistical characteristics of the signal under different channel environments and solve the problem of what level the overall signal should be compressed to.

[0080] Preset peak-to-average power ratio (PAPR) warning threshold range: This is a safety monitoring threshold for the back-end power amplifier, not a judgment threshold for the compression processing stage. Its function is to monitor whether the overall PAPR exceeds the linear operating safety range of the power amplifier and trigger the power amplifier operating point adjustment to protect the back-end RF devices and maintain the linearity of the power amplifier.

[0081] Furthermore, the obtained instantaneous amplitude spectrum and the calculated instantaneous frequency deviation are combined to construct the corresponding peak spatiotemporal feature vector. This peak spatiotemporal feature vector is used to guide the polarization constraint matrix to achieve targeted suppression of peak signals in frequency-selective fading channels. In the peak spatiotemporal feature vector, the instantaneous amplitude spectrum corresponds to the amplitude distribution characteristics of the time-domain peak, and the instantaneous frequency deviation corresponds to the frequency-domain offset characteristics. The combination of the two covers the peak correlation characteristics in both time and frequency dimensions, providing a complete basis for adjusting the parameters of the polarization constraint matrix. Figure 2 As shown, the transmitted signal itself exhibits a high-peak tail characteristic, and the amplitude probability density distribution still has a non-negligible distribution in the high-amplitude region. By combining the peak spatiotemporal feature vector, it is possible to distinguish which peaks are caused by channel fading tails and which are characteristics of the transmitted signal itself, thereby adjusting the sparse energy distribution of the polarization constraint matrix. Figure 3 As shown, the sparse energy distribution of the self-polarization constraint matrix can adjust the peak constraint strength at different time-frequency positions based on the peak spatiotemporal eigenvector, applying constraints only to the peak regions that need to be suppressed, thus avoiding the influence on signals in non-peak regions. Figure 4 and Figure 5 As shown, the targeted peak-to-average power ratio (PAPR) compression processing maintains the overall distribution pattern of the signal mapping points, compressing only the amplitude of high-amplitude points without changing the relative positions of normal signal mapping points, thus ensuring the accuracy of signal demodulation. Figure 6 As shown, the peak-to-average power ratio (PAPR) distribution of the signal after targeted suppression meets the expected suppression requirements without introducing additional performance loss. Figure 7 As shown, the adjusted peak region processing can retain a reasonable time-frequency energy distribution, without excessively changing the ratio of main lobe energy to side lobe energy, thus maintaining the original transmission characteristics of the signal.

[0082] This embodiment addresses the peak extraction error caused by frequency-selective fading and Doppler shift by accurately extracting and classifying peak features, enabling polarization constraint processing to adapt to different channel transmission conditions, maintaining peak-to-average power ratio suppression while avoiding additional signal distortion.

[0083] Furthermore, to address the issues of fragmented functions and poor workflow integration in existing peak-to-average power ratio (PAPR) suppression processing, which fails to balance PAPR performance with signal transmission quality, this embodiment provides a signal transmission system for a transmitter, used to implement the signal transmission method of any of the above embodiments. This system includes an acquisition and filtering module, a signal block acquisition module, a peak region identification module, a polarization constraint mapping module, and a time-domain interleaving quantization module. The system includes the following modules: a filtering module for acquiring the original transmit signal to be transmitted and filtering it to obtain the transmit signal to be input to the power amplifier; a signal block acquisition module for dividing the transmit signal into blocks according to a preset signal length to obtain multiple signal sub-blocks; a peak region identification module for performing oversampling rate analysis on each signal sub-block according to a preset power threshold to locate signal spikes exceeding the preset power threshold range, thereby identifying the peak region to be compressed; a polarization constraint mapping module for constructing a polarization constraint matrix adapted to the peak region and using the polarization constraint matrix to perform nonlinear amplitude-phase joint mapping compression on the peak region to obtain preprocessed signal sub-blocks; and a time-domain interleaving and quantization module for performing time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-blocks to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. The transmit baseband signal is then converted into an analog baseband signal, amplified by the power amplifier, and fed to the transmit antenna for transmission.

[0084] The signal transmission system of the transmitter provided in this embodiment can divide the complete transmitted signal into multiple signal sub-blocks with local sparsity through a signal block acquisition module. Decomposing the complete transmitted signal into independent processing units facilitates separate processing of the peak characteristics of different signal sub-blocks, reducing the overall computational complexity and adapting to the operational requirements of parallel processing architectures. Oversampling rate analysis through the peak region identification module improves the accuracy of peak position location, avoids processing errors caused by peak position offset under Nyquist sampling, and ensures that subsequent compression processing only applies to the true peak region, reducing unnecessary signal distortion. The polarization constraint mapping module can perform nonlinear amplitude-phase joint mapping compression on the peak region using a polarization constraint matrix, causing the maximum signal amplitude of the peak region to converge to a preset power threshold range. This reduces the amplitude fluctuation of the entire signal sub-block without compromising the signal sideband spectral characteristics, resulting in a pre-processed signal sub-block with a peak-to-average power ratio that meets transmission requirements. The preprocessed signal sub-blocks are time-domain interleaved and amplitude-quantized using a time-domain interleaving quantization module to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. This transmit baseband signal is then converted to an analog baseband signal, amplified by a power amplifier, and fed to the transmit antenna for transmission. The randomization process through time-domain interleaving eliminates the continuous effects of residual high peak power, reducing the bit error rate during transmission and improving signal transmission reliability.

[0085] Regarding the signal transmission system of the transmitter in the above embodiments, the specific methods by which each module performs its operation have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0086] To address the limitations of existing transmitter signal transmission methods in terms of ease of portability and reusability, and their difficulty in adapting to different hardware processing platforms, this embodiment also provides a computer-readable storage medium. This medium stores a computer program, which, when executed by a processor, implements all the processing steps of the transmitter signal transmission method described in any of the above embodiments, completing the entire processing flow from transmitting the signal to outputting amplified baseband signal that meets the requirements. This ensures consistency in the processing flow across different platforms. It is important to understand that the computer-readable storage medium can take different types of storage media forms to adapt to different types of processing terminals. It can be applied to various scenarios such as general-purpose computer platforms, software-defined radio platforms, and storage units of dedicated signal processing chips. There is no need to redevelop the entire processing flow for different platforms; only the computer program in the storage medium needs to be ported to different platforms to run, reducing the cost of promoting and applying the method.

[0087] Furthermore, the computer program in the computer-readable storage medium is written according to a modular architecture, with each processing step corresponding to an independent functional module. When it is necessary to adjust some processing parameters or processes for different application scenarios, only the content of the corresponding functional module needs to be modified, without changing the overall program architecture, which facilitates subsequent function upgrades and parameter adjustments. Understandably, modular program design also facilitates individual debugging and performance testing of each processing step, enabling quick location of problems in the processing flow, improving development and debugging efficiency, and shortening the development cycle for adapting to new scenarios.

[0088] Furthermore, all parameters in the computer program are set as configurable parameters, which can be adjusted according to the needs of actual application scenarios, including peak recognition threshold and sparsity of polarization constraint matrix, to adapt to the transmission signal processing needs of different standards and bandwidths. Parameter adjustment can be completed without recompiling the program, thus improving the adaptability of the method.

[0089] Furthermore, all computational processes in the computer program are implemented using vectorized operations, which can adapt to the parallel computing architecture of modern processors, fully utilize the processor's computing performance, improve the overall processing speed, meet the requirements of real-time processing of high-speed signals, and adapt to the processing needs of high-speed communication scenarios.

[0090] Furthermore, intermediate processing data during computer program execution is stored in a standardized format, facilitating subsequent backtracking and analysis of the processing process and helping researchers optimize processing parameters and improve processing workflows.

[0091] This embodiment solidifies the signal transmission method of the transmitter into a storable and portable computer program, which facilitates the deployment and application of the method on different types of signal processing equipment, lowers the application threshold of the method, and ensures the consistency and repeatability of the method execution process, making it convenient for application and promotion in different scenarios.

[0092] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A signal transmission method for a transmitter, characterized in that, The transmitter is equipped with a transmitting antenna and a power amplifier. The method includes: The original transmission signal to be transmitted is acquired and filtered to obtain the transmission signal to be input into the power amplifier. The transmitted signal is divided into blocks according to a preset signal length to obtain multiple signal sub-blocks; Oversampling rate analysis is performed on each signal sub-block according to a preset power threshold to locate signal peaks in the signal sub-block that exceed the preset power threshold range, so as to identify the peak region to be compressed. A polarization constraint matrix adapted to the peak region is constructed, and nonlinear amplitude-phase joint mapping compression is performed on the peak region using the polarization constraint matrix to obtain preprocessed signal sub-blocks; The preprocessed signal sub-blocks are subjected to time-domain interleaving and signal amplitude quantization to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. The transmit baseband signal is then converted into an analog baseband signal, amplified by the power amplifier, and fed to the transmit antenna for transmission. The process of constructing a polarization constraint matrix adapted to the peak region, and then using the polarization constraint matrix to perform nonlinear amplitude-phase joint mapping compression on the peak region to obtain preprocessed signal sub-blocks, includes the following steps: Extract the amplitude envelope and phase information of the peak region, calculate the offset of the instantaneous power of the signal relative to the average power, and use the offset as a distortion metric. Based on the distortion metric, a polarized coordinate system is constructed in the complex plane, and a polarization constraint matrix is ​​generated to perform rotation and shrinkage operations on high-power points. The corresponding rotation transformation matrix is ​​filled into the corresponding coordinate position of the polarization constraint matrix. The rotation transformation matrix is ​​expressed as follows: ,in, Represents the rotation transformation matrix. Indicates the shrinkage ratio. For the corrected phase information in the first... The values ​​of each signal point are taken; the distance deviation between the transformed signal point and the original signal mapping point is calculated; the distance deviation is normalized to the [0,1] interval and the weight of the corresponding position rotation transformation matrix is ​​corrected to obtain the final sparsity-fitting polarization constraint matrix. The signal points in the peak region are mapped to the polarization constraint matrix for amplitude and phase joint correction until the maximum signal amplitude in the peak region converges to the preset power threshold range, thus obtaining the preprocessed signal sub-block. The construction of a polarized coordinate system in the complex plane based on the distortion metric includes the following steps: The minimum Euclidean distance of the original signal space map is set according to the symbol set cardinality of the transmitted signal, and the minimum Euclidean distance is used as the constraint boundary. The rotation angle and contraction ratio of the polarization coordinate system are adjusted according to the instantaneous energy density of the signal point on the complex plane so that the polarization coordinate system parameters are adapted to the power distribution of the current peak region. The formula for calculating the shrinkage ratio is: , ,in, Indicates the first Instantaneous energy density at each signal point For the first The instantaneous power of each signal point For the first The average power of each signal point To preset the power threshold, For the first The distance from each corrected signal point to its nearest neighbor in the original signal mapping. The minimum Euclidean distance of the original signal space map under the current symbol set cardinality; The formula for calculating the rotation angle is: ,in, For the corrected phase information in the first... The value of each signal point For the first Original phase of each signal point; Calculate the distance deviation between the signal points after polarization transformation and the original signal mapping points, and use the distance deviation to correct the sparsity of the polarization constraint matrix to be generated.

2. The signal transmission method of the transmitter as described in claim 1, characterized in that, After identifying the peak region to be compressed, the process further includes the following steps: The identified peak regions are windowed and truncated, and the ratio of the main lobe energy to the side lobe energy of the truncated signal is extracted. The ratio of the main lobe energy to the side lobe energy is compared with a set energy threshold. When the ratio of the main lobe energy to the side lobe energy is lower than the energy threshold, the parameter reset process of the polarization constraint matrix is ​​triggered, and a reset polarization constraint matrix adapted to the energy distribution characteristics of the current peak region is regenerated. The peak region is repeatedly subjected to nonlinear amplitude-phase joint mapping compression using the reset polarization constraint matrix, while preserving the original signal characteristics of the non-peak region.

3. The signal transmission method of the transmitter as described in claim 1, characterized in that, The process of performing time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-blocks to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter, converting the transmit baseband signal into an analog baseband signal, amplifying it through the power amplifier, and then feeding it to the transmit antenna for transmission includes the following steps: In the time domain, the sample points in the preprocessed signal sub-block are repositioned according to the pseudo-random sequence pre-shared by both the sender and receiver, resulting in interleaved signal samples that break up the original continuous distribution. Based on the preset quantization bit width, the interleaved signal sample is subjected to preliminary amplitude compression mapping to constrain the continuous signal amplitude to the dynamic range corresponding to the preset quantization bit width, thereby obtaining the constrained signal. The amplitude of the constrained signal is adjusted using a preset nonlinear compression function, including: amplitude enhancement of small-amplitude signal components within the dynamic range and amplitude attenuation of large-amplitude signal components outside the dynamic range, so as to obtain an amplitude-adapted quantized signal. The signal to be quantized is subjected to discrete quantization processing to obtain quantized symbol data; The quantized symbol data is organized into a fixed-length data frame, and a pilot sequence for receiving end synchronization recovery is inserted at the frame header of the data frame to obtain the transmitted baseband signal.

4. The signal transmission method of the transmitter as described in claim 1, characterized in that, The step of dividing the transmitted signal into blocks according to a preset signal length to obtain multiple signal sub-blocks includes the following steps: Detect the frame header identifier of the transmitted signal to determine the starting position of the transmitted signal; The preset signal length is determined based on the preset number of sub-signals, and the frame length of the entire transmitted signal is divided into an integer number of length-aligned signal sub-blocks to complete the block processing.

5. The signal transmission method of the transmitter as described in claim 1, characterized in that, The method further includes the following steps: Real-time monitoring of instantaneous peak-to-average ratio (PAR) during the PAR suppression process, and statistical analysis of the cumulative PAR distribution function within a preset time window; The preset power threshold is adjusted according to the cumulative peak-to-average power ratio distribution function. When the instantaneous peak-to-average power ratio (PAPR) exceeds the preset PAPR warning threshold range, the operating point of the output power amplifier is adjusted.

6. The signal transmission method of the transmitter as described in claim 1, characterized in that, The extraction of the amplitude envelope and phase information of the peak region includes the following steps: The target peak region signal is extracted and subjected to Hilbert transform to obtain the corresponding analytical signal; The instantaneous amplitude spectrum is calculated based on the analytical signal, and the amplitude envelope of the peak region is extracted. The initial instantaneous phase spectrum is calculated based on the analytical signal, and the initial instantaneous phase spectrum is unwound to obtain the phase information after the peak region correction. The instantaneous frequency deviation is calculated using the corrected phase information, and the peak spatiotemporal feature vector is constructed by combining it with the amplitude envelope for subsequent nonlinear compression processing.

7. A signal transmission system for a transmitter, characterized in that, For implementing the signal transmission method of the transmitter as described in any one of claims 1-6, the system comprises: The acquisition and filtering module is used to acquire the original transmission signal to be transmitted and to filter the original transmission signal to obtain the transmission signal to be input into the power amplifier; The signal block acquisition module is used to divide the transmitted signal into blocks according to a preset signal length to obtain multiple signal sub-blocks; The peak region identification module is used to perform oversampling rate analysis on each of the signal sub-blocks according to a preset power threshold, locate the signal peak points in the signal sub-blocks that exceed the preset power threshold range, so as to identify the peak region to be compressed. The polarization constraint mapping module is used to construct a polarization constraint matrix adapted to the peak region, and to perform nonlinear amplitude-phase joint mapping compression on the peak region using the polarization constraint matrix to obtain preprocessed signal sub-blocks. The time-domain interleaving and quantization module is used to perform time-domain interleaving and signal amplitude quantization on the preprocessed signal sub-block to obtain a transmit baseband signal adapted to the input format of the digital-to-analog converter. After converting the transmit baseband signal into an analog baseband signal, it is amplified by the power amplifier and then fed to the transmit antenna for transmission.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the signal transmission method of the transmitter as described in any one of claims 1-6.

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