Peak-to-average power ratio suppression method and system based on adaptive iterative proportional compression soft peak clipping filtering

Abstract

The invention discloses a peak-to-average power ratio suppression method and system based on adaptive iterative proportional compression soft peak clipping filtering, and the method comprises the steps: carrying out the serial-to-parallel conversion of a to-be-transmitted data stream, modulating the to-be-transmitted data stream to form a frequency domain signal, and converting the frequency domain signal into a time domain signal through IFFT (Inverse Fast Fourier Transform); performing logarithmic proportion compression soft peak clipping processing on the time domain signal, and filtering the signal after peak clipping processing to obtain a signal with a regenerated peak value; if the peak-to-average ratio of the signal is greater than the target peak-to-average ratio, returning to carry out logarithmic proportion compression soft peak clipping filtering processing; if the number of iterations in this round is higher than the optimal number of iterations, the threshold peak-to-average power ratio is increased, and if the number of iterations does not reach the optimal number of iterations, the threshold peak-to-average power ratio is reduced, and the next frame of signal is processed. The system comprises a signal conversion module, a logarithmic proportion compression soft peak clipping processing module, a filtering module, a discrimination module and a threshold self-adaption module. The method is easy to operate, small in signal damage and small in calculation amount, and meets the requirement for strong real-time processing of a communication system.

Description

Technical Field

[0001] This invention relates to the field of OFDM peak-to-average power ratio (PAPR) suppression technology, and in particular to a PAPR suppression method and system based on adaptive iterative proportional compression soft clipping filtering. Background Technology

[0002] Orthogonal Frequency Division Multiplexing (OFDM) is widely used in modern wireless communication systems due to its high spectral efficiency and excellent resistance to multipath interference. However, one of the main drawbacks of this system is its excessively high peak-to-average power ratio (PAPR) of the transmitted signal. This is because if multiple carriers accumulate with the same initial phase at certain times, large peak values ​​can be randomly generated. Therefore, certain techniques must be used to reduce the PAPR of OFDM signals. In recent years, researchers have conducted extensive research on how to reduce the PAPR of OFDM signals, which can be broadly categorized into three types: coding techniques, clipping techniques, and probabilistic techniques.

[0003] The core of coding techniques is to add redundant coding and then select the smaller signal for transmission after calculation, such as Gray codes, block codes, and M-sequences. The core of probabilistic techniques is to weight the input signal with different phase rotation factors, and after calculation, find a set of phase factors with the smallest PAPR value. This set of phase factors is called the optimal or suboptimal phase factors and is then transmitted. Common methods include Selective Mapping (SLM) and Partial Transmitted Sequence (PTS) algorithms. The PTS method has very high computational complexity. While the suboptimal hierarchical iterative PTS method, an improvement on PTS, reduces computational complexity, it incurs some performance loss.

[0004] The core of amplitude limiting techniques is to directly limit peak values ​​by performing nonlinear processing on the transmitted signal, such as peak windowing, U-transform, and peak clipping. The simplest and most intuitive method is peak clipping, which directly limits the amplitude of the IFFT output sequence; amplitudes exceeding the limit are reduced to the same size as the threshold, while amplitudes below the threshold are allowed to pass. While this can reduce PAPR to some extent, it often leads to out-of-band leakage, potentially causing severe in-band distortion, reduced bit error rate performance, and the introduction of out-of-band noise. This also causes spectral spread and reduces spectral efficiency, interfering with communication equipment in nearby frequency bands and wasting transmission capacity. To reduce spectral spread, filtering can be applied after amplitude limiting. Since filtering causes the signal peak value to increase again, but the peak value is much smaller than before limiting, iterative peak clipping filtering methods have been proposed. These methods involve multiple iterations of peak clipping filtering until the target PAPR threshold is reached, at which point the iteration loop terminates. Balancing the PAPR reduction effect with signal distortion to some extent has become a hot topic in current research and application.

[0005] Existing iterative peak-shaving filtering methods mainly have two problems:

[0006] Firstly, regarding peak clipping mechanisms, traditional hard peak clipping directly truncates peaks exceeding a threshold, which is simple to operate but introduces significant signal distortion; traditional soft peak clipping mostly uses nonlinear compression functions for peak clipping, which faces the complexity of parameter selection and optimization, as well as the increased computational load brought about by nonlinear operations.

[0007] Secondly, regarding iterative strategies, most existing iterative peak-to-average power ratio (PAPR) filtering methods target a fixed threshold and iterate repeatedly until the target threshold is reached, at which point the loop terminates. Common threshold determination methods are primarily based on the statistical characteristics of the signal, such as setting the threshold according to the signal amplitude probability density function (PDF) or complementary cumulative distribution function (CCDF). For example, based on the CCDF curve, a threshold is selected that meets certain probability requirements (e.g., a CCDF value of 10). -3 The peak-to-average power ratio (PAPR) is used as the target threshold. This preset fixed target threshold is not only difficult to adapt to the variable characteristics of OFDM signals in complex time-varying environments, but also the number of iterations is uncontrollable, making it difficult to meet the requirements of strong real-time processing in communication systems.

[0008] The two issues mentioned above limit the further application and promotion of the iterative peak-to-average ratio suppression method for peak clipping filtering, and there is an urgent need for innovative improvement strategies to overcome these challenges. Summary of the Invention

[0009] The purpose of this invention is to provide a peak-to-average power ratio (PAPR) suppression method and system based on adaptive iterative proportional compression soft clipping filtering, which is simple to operate, has minimal signal impairment, and requires minimal computation. This method suppresses the PAPR of signals transmitted by orthogonal frequency division multiplexing (OFDM) systems under complex time-varying environments, thus meeting the requirements of strong real-time processing in communication systems.

[0010] The technical solution to achieve the objective of this invention is: a peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering, comprising the following steps:

[0011] Step 1: Convert the serial data to parallel data by performing serial-to-parallel conversion, and modulate it to form a frequency domain signal. Then, use the inverse fast Fourier transform (IFFT) to convert the frequency domain signal into a time domain signal.

[0012] Step 2: Perform logarithmic scaling soft peak clipping on the time-domain signal: Set the target peak-to-average power ratio (TG-PAPR) as the suppression threshold, and simultaneously configure the threshold peak-to-average power ratio (TH-PAPR). Calculate the amplitude of each sampling point, and for peak components exceeding the threshold peak-to-average power ratio (TH-PAPR), scale down the peaks proportionally to the ratio of the signal's threshold power to its actual power.

[0013] Step 3: Filter the peak-clipping signal to eliminate out-of-band noise and spectral diffusion introduced during the peak-clipping process, and obtain the peak-regenerated signal.

[0014] Step 4: Determine the peak-to-average power ratio (PAPR) of the signal: If the PAPR of the signal is greater than the target PAPR value TG-PAPR, return to step 2; if the PAPR of the signal is less than or equal to the target PAPR value TG-PAPR, proceed to step 5.

[0015] Step 5: Perform threshold adaptive operation to determine the number of iterations in this round:

[0016] If the number of iterations equals the optimal number of iterations, then keep the threshold peak-to-average power ratio (TH-PAPR) unchanged;

[0017] If the number of iterations is higher than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is increased by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

[0018] If the number of iterations is less than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is reduced by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

[0019] A peak-to-average power ratio (PAPR) suppression system based on adaptive iterative proportional compression soft clipping filtering is disclosed. This system implements the PAPR suppression method based on adaptive iterative proportional compression soft clipping filtering. Specifically, it includes a signal transformation module, a logarithmic proportional compression soft clipping processing module, a filtering module, a discrimination module, and a threshold adaptive module, wherein:

[0020] The signal conversion module performs serial-to-parallel conversion on the data stream to be transmitted, transforming serial data into parallel data, and modulates it to form a frequency domain signal. The inverse fast Fourier transform (IFFT) is then used to convert the frequency domain signal into a time domain signal.

[0021] The logarithmic scaling soft peak clipping module performs logarithmic scaling soft peak clipping on the time-domain signal: it sets a target peak-to-average power ratio (TG-PAPR) as a suppression threshold and simultaneously configures a threshold peak-to-average power ratio (TH-PAPR). It calculates the amplitude of each sampling point and scales down the peak components that exceed the threshold peak-to-average power ratio (TH-PAPR) proportionally to the ratio of the signal's threshold power to its actual power.

[0022] The filtering module filters the signal after peak clipping to eliminate out-of-band noise and spectral diffusion introduced during the peak clipping process, and obtains the signal with peak regeneration.

[0023] The discrimination module discriminates the peak regeneration signal: if the peak-to-average ratio (PAR) of the signal is greater than the target PAR value TG-PAPR, it returns to the logarithmic scaling soft clipping processing module; if the PAR of the signal is less than or equal to the target PAR value TG-PAPR, it enters the threshold adaptive module.

[0024] The threshold adaptive module performs threshold adaptive operation to determine the number of iterations in the current round:

[0025] If the number of iterations equals the optimal number of iterations, then keep the threshold peak-to-average power ratio (TH-PAPR) unchanged;

[0026] If the number of iterations is higher than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is increased by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

[0027] If the number of iterations is less than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is reduced by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

[0028] A mobile terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the peak-to-average power ratio suppression method based on adaptive iterative proportional compression soft clipping filtering.

[0029] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the peak-to-average ratio suppression method based on adaptive iterative proportional compression soft clipping filtering.

[0030] Compared with the prior art, the significant advantages of this invention are:

[0031] (1) The traditional peak clipping is optimized into proportional compression soft peak clipping, which minimizes signal damage while ensuring the peak-to-average power ratio suppression effect;

[0032] (2) The fixed peak-to-average ratio target threshold is improved to a floating peak-to-average ratio target threshold, so that the number of iterations and the output peak-to-average ratio can be automatically adjusted according to the signal characteristics, and the optimal balance between computational load and performance can be achieved adaptively. Attached Figure Description

[0033] Figure 1 This is a flowchart of the peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to the present invention.

[0034] Figure 2 This is the first peak clipping of the twentieth group of signals and the power spectrum after peak clipping and filtering.

[0035] Figure 3This is the power spectrum of the twentieth group of signals after the second peak clipping and filtering.

[0036] Figure 4 This is the third peak clipping of the twentieth group of signals, and the power spectrum after peak clipping and filtering.

[0037] Figure 5 This is a comparison of the time-domain waveforms before and after the iterative ratio adjustment soft clipping filter.

[0038] Figure 6 This is the parameter transformation diagram of the threshold adaptive method.

[0039] Figure 7 It is a CCDF curve graph.

[0040] Figure 8 This is a comparison chart of the bit error rate performance curves of the original signal, traditional hard clipping filtering, and the method of this invention. Detailed Implementation

[0041] It is readily understood that, based on the technical solution of this invention, those skilled in the art can conceive of various embodiments of this invention without altering its essential spirit. Therefore, the following specific embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of this invention or as limitations or restrictions on its technical solution.

[0042] Combination Figure 1 The present invention discloses a peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering, comprising the following steps:

[0043] Step 1: Convert the serial data to parallel data by performing serial-to-parallel conversion, and modulate it to form a frequency domain signal. Then, use the inverse fast Fourier transform (IFFT) to convert the frequency domain signal into a time domain signal.

[0044] Step 2: Perform logarithmic scaling soft peak clipping on the time-domain signal: Set the target peak-to-average power ratio (TG-PAPR) as the suppression threshold, and simultaneously configure the threshold peak-to-average power ratio (TH-PAPR). Calculate the amplitude of each sampling point, and for peak components exceeding the threshold peak-to-average power ratio (TH-PAPR), scale down the peaks proportionally to the ratio of the signal's threshold power to its actual power.

[0045] Step 3: Filter the peak-clipping signal to eliminate out-of-band noise and spectral diffusion introduced during the peak-clipping process, and obtain the peak-regenerated signal.

[0046] Step 4: Determine the peak-to-average power ratio (PAPR) of the signal: If the PAPR of the signal is greater than the target PAPR value TG-PAPR, return to step 2; if the PAPR of the signal is less than or equal to the target PAPR value TG-PAPR, proceed to step 5.

[0047] Step 5: Perform threshold adaptive operation to determine the number of iterations in this round:

[0048] If the number of iterations equals the optimal number of iterations, then keep the threshold peak-to-average power ratio (TH-PAPR) unchanged;

[0049] If the number of iterations is higher than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is increased by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

[0050] If the number of iterations is less than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is reduced by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

[0051] As a specific example, step 1 is as follows:

[0052] Step 1.1: After the signal is input, the data stream to be transmitted is converted from serial to parallel, turning the serial data into N parallel data streams;

[0053] Step 1.2: Perform 64QAM modulation on the N data streams to form N frequency domain signals. 64QAM modulation represents 64-ary quadrature amplitude modulation.

[0054] Step 1.3: Convert the N frequency domain signals into time domain signals using Inverse Fast Fourier Transform (IFFT). This time domain signal is generated by superimposing multiple orthogonal subcarrier signals, as shown in the following formula:

[0055] (1)

[0056] in, Indicates the number of subcarriers. Indicates subcarrier index, This represents the frequency domain signal of the OFDM system. Indicates the first The subcarrier at the ... Phase of each sampling point Represents the imaginary unit; To obtain the time-domain signal after IFFT transformation, Indicates the time-domain signal at the th Complex values ​​of each sampling point; This represents the index of the time-domain sampling point.

[0057] The time-domain OFDM signal generated in step 1 has a peak-to-average power ratio (PAPR) characteristic. High PAPR is the core issue that needs to be addressed in all subsequent processing.

[0058] As a specific example, in step 2, the threshold peak-to-average power ratio (TH-PAPR) is always less than the target peak-to-average power ratio (TG-PAPR) and infinitely approaches the target TG-PAPR. The threshold TH-PAPR is set to avoid the iteration process from falling into an infinite loop. When calculating the instantaneous peak-to-average power ratio of the time-domain signal point by point, peak points higher than the preset threshold need to be continuously detected. If the target TG-PAPR is directly used as the preset threshold, due to the dynamic distribution characteristics of the signal peak points, using the target TG-PAPR as the detection benchmark will result in the inability to completely identify the exceeding points. Therefore, this invention configures the preset threshold as a threshold TH-PAPR that is always less than the target TG-PAPR. This configuration ensures that all peak points higher than the target TG-PAPR can be effectively detected, eliminating the risk of iteration convergence failure from the algorithm principle level.

[0059] In step 2, the peak components exceeding the threshold peak-to-average power ratio (TH-PAPR) are reduced proportionally to the ratio of the signal's threshold power to its actual power, as described by the following formula:

[0060] (2)

[0061] in, For the time-domain signal in the th... Complex values ​​of each sampling point The time-domain signal after peak clipping is at the 1st... Complex values ​​of each sampling point; This represents the actual power value of the signal. This represents the threshold power value of the signal.

[0062] for The corresponding decibel value, for The corresponding decibel value, , The unit is dB, and we get:

[0063] (3)

[0064] The logarithmic form of equation (2) is equation (4):

[0065] (4)

[0066] The natural number form of equation (2) is equation (5):

[0067] (5)

[0068] The logarithmic scaling soft clipping proposed in step 2 compares the PAPR value of the signal sampling point itself with the threshold peak-to-average power ratio TH-PAPR in real time. It only performs logarithmic scaling soft clipping on sampling points that exceed the threshold peak-to-average power ratio TH-PAPR. The peak clipping principle is determined by the ratio of the signal peak clipping threshold power to the actual power. The peak clipping basis is more reasonable, and the peak clipping method is more gentle and slow, effectively avoiding excessive damage to the signal.

[0069] Step 2 effectively reduces the PAPR value of the signal, but peak clipping introduces a certain degree of out-of-band spectral leakage and in-band distortion in the frequency domain, resulting in a decrease in signal quality. Therefore, further filtering is required.

[0070] As a specific example, in step 3, a time-domain windowed filtering method is used to filter the peak-clipping signal: the two ends of the signal's time-domain waveform are set to zero, and only the center effective segment is retained for spectral filtering. This processing effectively suppresses out-of-band spectral leakage after OFDM signal peak clipping, ensuring that the output signal spectrum meets the requirements of the communication system and avoiding interference to adjacent channels.

[0071] As a specific example, in step 5, a threshold adaptive operation is performed: the number of iterations in this round is judged. If it exceeds the set optimal number of iterations (e.g., the optimal number of iterations is 4), the threshold PPR is increased by 1 step unit (the step unit is adjustable, with a typical value of 0.05dB), serving as the convergence condition for the PPR threshold of the next frame signal, thus reducing computational complexity. If the number of iterations does not reach the optimal number of iterations, the threshold PPR is decreased by 1 step unit (the step unit is adjustable, with a typical value of 0.05dB), serving as the convergence condition for the PPR threshold of the next frame signal, thus improving PPR suppression performance. Through multiple threshold adaptive iterations and fine adjustments of soft peak clipping filtering, the final output signal achieves an optimal balance between PAPR suppression, out-of-band radiation, and in-band distortion, outputting a high-quality signal that conforms to communication system standards.

[0072] Step 5 processing ensures that the PAPR of the output signal meets the requirements of the communication system, and makes the number of iterations and the output peak-to-average power ratio autonomously adjustable, adaptively achieving the optimal balance between computational load and performance, while keeping the spectrum and bit error rate performance within the required range.

[0073] Traditional iterations typically use a fixed target PAPR threshold as the convergence condition. The threshold adaptive iterative optimization method proposed in this invention sets an initial threshold PAPR value, ensuring that the PAPR value of the output signal is not higher than the threshold PAPR value. During the iteration process, the PAPR value of the output signal and the number of iterations used are compared. If the PAPR value of the output signal meets the condition but the number of iterations does not meet the condition, the threshold PAPR value is adaptively adjusted to achieve the optimal balance between computation and performance.

[0074] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0075] Example

[0076] 1. Power Spectrum Analysis

[0077] This embodiment verifies the effectiveness of the adaptive iterative proportional compression soft peak clipping filter method in suppressing out-of-band spectral spread through simulation. The simulation conditions are: QAM = 64, FFT points = 128, sampling rate = 1.6G, target peak-to-average power ratio (PAPR) = 5.5dB, and initial threshold PAPR (TH) = 5.4dB. Twenty randomly generated signals were used in this simulation. Figures 2-4 It shows the peak clipping of the twentieth group of signals and the power spectrum after peak clipping and filtering.

[0078] Combination Figure 2 The power spectrum of the OFDM signal after the first logarithmic compression soft clipping is 5.291 dB. It can be seen that the PAPR value is significantly reduced after the first logarithmic compression soft clipping. However, the accompanying problem is that the spectrum spreads after the clipping. Therefore, the signal is further filtered. Filtering can effectively suppress the spectrum spread, but the trade-off is that new peak values ​​are generated, and the PAPR value increases to 6.057 dB.

[0079] Based on this, a second logarithmic scaling soft peak clipping process is performed, such as... Figure 3 This effectively reduced the PAPR value, but also caused spectral spread. After the second filtering, while the spectral spread problem was improved, the PAPR value decreased to 5.701 dB compared to the first logarithmic proportional compression soft clipping filtering method. A third logarithmic proportional compression soft clipping was then performed, as... Figure 4 The PAPR value dropped to 5.484 dB, below the target value of 5.5 dB, completing the iteration.

[0080] Comparison of time-domain waveforms before and after soft peak clipping with iterative scaling adjustment. Figure 5 As shown, this method is effective for higher peak points. The logarithmic scaling soft clipping filter can address both peak-to-average power ratio (PAPR) suppression and spectral spread issues.

[0081] 2. Adaptive Effect Analysis

[0082] The simulation results of the threshold adaptive method are as follows: Figure 6 The simulation conditions consist of twenty randomly generated signals. Figure 6 This includes a threshold adaptive value adjustment transformation graph and an adaptive iteration number transformation graph. The initial threshold peak-to-average power ratio (PAPR) is set to 5.4 dB. Figure 6 As can be seen, with each input frame of signal, the threshold adaptive method is used more frequently. While the threshold value is continuously adjusted, the number of adaptive iterations gradually stabilizes. Figure 6 It can be seen that the adaptive threshold value of the first frame signal remains at 5.4dB, requiring 3 iterations of logarithmic scaling soft clipping filtering; the adaptive threshold value of the eighth frame signal is adjusted to 5.2dB, requiring 6 iterations of logarithmic scaling soft clipping filtering; the adaptive threshold value of the fifteenth frame signal is adjusted to 5.2dB, requiring 3 iterations of logarithmic scaling soft clipping filtering. With each frame of signal input, the adaptive threshold value is continuously adjusted and gradually stabilizes, and the number of iterations also remains relatively stable at around 3. When the last frame signal is input, the threshold value is basically stable at 5.15dB, and the required number of iterations is also basically stable at around 3. Figure 6 As the curve shows, with the increase in the number of input signal frames and the expansion of the corresponding computational scale, the parameter space of this algorithm will gradually converge to the optimal state. When the system processes larger-scale signal frames, the optimized parameters accumulated through historical iterations will significantly improve computational efficiency, resulting in a stable or decreasing trend in overall computational resource consumption. This adaptive optimization mechanism enables the system to maintain the target PAPR suppression performance with low computational complexity during long-term operation. The threshold adaptive method allows for automatic adjustment of the number of iterations and the output peak-to-average power ratio, adaptively achieving the optimal balance between computational load and performance. In scenarios with higher hardware requirements, the computational load can be flexibly controlled, achieving an automatic balance between computational load and performance.

[0083] 3. CCDF Curve Analysis

[0084] Combination Figure 7 The peak-to-average power ratio (PAPR) suppression effect of this invention was simulated and analyzed under the following conditions: 500 samples, FFT size of 128, QAM modulation of level 64, and PAPR target value set at 5.5 dB. This invention only requires setting a target PAPR value; the obtained PAPR value will be within the target value. It can be seen that compared with the original signal, the method proposed in this invention has excellent PAPR suppression effect. By setting a threshold PAPR value for multiple iterations of peak clipping filtering, it is more suitable for engineering applications.

[0085] 4. Bit Error Rate Analysis

[0086] Both iterative amplitude limiting filtering and traditional amplitude limiting filtering essentially reduce the peak-to-average power ratio (PAPR) of OFDM signals at the cost of signal nonlinear distortion. Figure 8 This is a comparison of the bit error rate (BER) performance curves of the original signal, traditional iterative peak clipping filtering, and the adaptive iterative proportional compression soft peak clipping filtering peak-to-average power ratio (PAPR) suppression method proposed in this invention. The horizontal axis (SNR) represents the system signal-to-noise ratio, and the vertical axis (BER) represents the system bit error rate. Figure 8 As can be seen, when the SNR is small, the adaptive iterative proportional compression soft clipping filter PAPR suppression method proposed in this invention almost completely overlaps with the BER of the original signal without any processing. When the SNR>10dB, the bit error rate curve of the method proposed in this invention is close to the curve of the original signal without any operation, and the bit error rate is within an acceptable range. In contrast, the traditional iterative clipping filter method will cause obvious nonlinear distortion, resulting in a higher bit error rate and reduced system transmission performance.

[0087] Based on the above simulation analysis, it can be confirmed that the adaptive iterative proportional compression soft peak clipping filtering PAPR suppression method proposed in this invention effectively suppresses the signal spectrum spread phenomenon while maintaining the controllable bit error rate performance of the system receiver. This method sets a target PAPR value and a threshold PAPR value (the value of which is always less than the target value and can be infinitely approximated), and establishes an adaptive adjustment relationship between the number of iterations and the threshold value based on the actual hardware resource constraints. It can achieve two core advantages, namely: (1) Parameter adaptive control: The number of iterations and the output peak-to-average power ratio form a closed-loop adjustment mechanism to dynamically optimize the balance between computational complexity and system performance; (2) Engineering hardware adaptability enhancement: In scenarios with limited hardware resources, the computational load is precisely controlled by constraining the iteration depth to achieve the best balance between computational resource consumption and PAPR suppression performance. This invention is particularly suitable for the engineering deployment of large-scale OFDM systems and solves the technical bottleneck of traditional methods in high-order modulation systems where computational complexity and performance are difficult to balance.

[0088] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0089] It should be understood that, in order to simplify the present invention and help those skilled in the art understand its various aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes described in a single embodiment or with reference to a single figure. However, the present invention should not be construed as including all features in the exemplary embodiments as essential technical features of the claims of this patent.

Claims

1. A peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering, characterized in that, Includes the following steps: Step 1: Convert the serial data to parallel data by performing serial-to-parallel conversion, and modulate it to form a frequency domain signal. Then, use the inverse fast Fourier transform (IFFT) to convert the frequency domain signal into a time domain signal. Step 2: Perform logarithmic scaling soft peak clipping on the time-domain signal: Set the target peak-to-average power ratio (TG-PAPR) as the suppression threshold, and simultaneously configure the threshold peak-to-average power ratio (TH-PAPR). Calculate the amplitude of each sampling point, and for peak components exceeding the threshold peak-to-average power ratio (TH-PAPR), scale down the peaks proportionally to the ratio of the signal's threshold power to its actual power. Step 3: Filter the peak-clipping signal to eliminate out-of-band noise and spectral diffusion introduced during the peak-clipping process, and obtain the peak-regenerated signal. Step 4: Determine the peak-to-average power ratio (PAPR) of the signal: If the PAPR of the signal is greater than the target PAPR value TG-PAPR, return to step 2; if the PAPR of the signal is less than or equal to the target PAPR value TG-PAPR, proceed to step 5. Step 5: Perform threshold adaptive operation to determine the number of iterations in this round: If the number of iterations equals the optimal number of iterations, then keep the threshold peak-to-average power ratio (TH-PAPR) unchanged; If the number of iterations is higher than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is increased by 1 step unit and used as the threshold TH-PAPR for the next frame signal. If the number of iterations is less than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is reduced by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

2. The peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to claim 1, characterized in that, Step 1 is described in detail as follows: Step 1.1: After the signal is input, the data stream to be transmitted is converted from serial to parallel, turning the serial data into N parallel data streams; Step 1.2: Perform 64QAM modulation on the N data streams to form N frequency domain signals. 64QAM modulation represents 64-ary quadrature amplitude modulation. Step 1.3: Convert the N frequency domain signals into time domain signals using Inverse Fast Fourier Transform (IFFT). This time domain signal is generated by superimposing multiple orthogonal subcarrier signals, as shown in the following formula: （1） in, Indicates the number of subcarriers. Indicates subcarrier index, This represents the frequency domain signal of the OFDM system. Indicates the first The subcarrier at the ... Phase of each sampling point Represents the imaginary unit; To obtain the time-domain signal after IFFT transformation, Indicates the time-domain signal at the th Complex values ​​of each sampling point; This represents the index of the time-domain sampling point.

3. The peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to claim 2, characterized in that, In step 2, the threshold peak-to-average ratio TH-PAPR is always less than the target peak-to-average ratio TG-PAPR and infinitely approaches the target peak-to-average ratio TG-PAPR.

4. The peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to claim 3, characterized in that, In step 2, the peak components exceeding the threshold peak-to-average power ratio (TH-PAPR) are reduced proportionally to the ratio of the signal's threshold power to its actual power, as described by the following formula: （2） in, For the time-domain signal in the th... Complex values ​​of each sampling point The time-domain signal after peak clipping is at the 1st... Complex values ​​of each sampling point; This represents the actual power value of the signal. This represents the threshold power value of the signal. for The corresponding decibel value, for The corresponding decibel value, , The unit is dB, and we get: （3） The logarithmic form of equation (2) is equation (4): （4） The natural number form of equation (2) is equation (5): （5） 5. The peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to claim 1, characterized in that, In step 3, a time-domain windowed filtering method is used to filter the signal after peak clipping: the two ends of the signal time-domain waveform are set to zero, and only the center effective segment is retained for spectral filtering.

6. The peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to claim 1, characterized in that, The optimal number of iterations mentioned in step 5 is 4.

7. The peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering according to claim 1, characterized in that, The step unit mentioned in step 5 is 0.05dB.

8. A peak-to-average power ratio (PAPR) suppression system based on adaptive iterative proportional compression soft clipping filtering, characterized in that, This system is used to implement the peak-to-average power ratio (PAPR) suppression method based on adaptive iterative proportional compression soft clipping filtering as described in any one of claims 1 to 7, specifically including a signal transformation module, a logarithmic proportional compression soft clipping processing module, a filtering module, a discrimination module, and a threshold adaptive module, wherein: The signal conversion module performs serial-to-parallel conversion on the data stream to be transmitted, transforming serial data into parallel data, and modulates it to form a frequency domain signal. The inverse fast Fourier transform (IFFT) is then used to convert the frequency domain signal into a time domain signal. The logarithmic scaling soft peak clipping module performs logarithmic scaling soft peak clipping on the time-domain signal: it sets a target peak-to-average power ratio (TG-PAPR) as a suppression threshold and simultaneously configures a threshold peak-to-average power ratio (TH-PAPR). It calculates the amplitude of each sampling point and scales down the peak components that exceed the threshold peak-to-average power ratio (TH-PAPR) proportionally to the ratio of the signal's threshold power to its actual power. The filtering module filters the signal after peak clipping to eliminate out-of-band noise and spectral diffusion introduced during the peak clipping process, and obtains the signal with peak regeneration. The discrimination module discriminates the peak regeneration signal: if the peak-to-average ratio (PAR) of the signal is greater than the target PAR value TG-PAPR, it returns to the logarithmic scaling soft clipping processing module; if the PAR of the signal is less than or equal to the target PAR value TG-PAPR, it enters the threshold adaptive module. The threshold adaptive module performs threshold adaptive operation to determine the number of iterations in the current round: If the number of iterations equals the optimal number of iterations, then keep the threshold peak-to-average power ratio (TH-PAPR) unchanged; If the number of iterations is higher than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is increased by 1 step unit and used as the threshold TH-PAPR for the next frame signal. If the number of iterations is less than the optimal number of iterations, the threshold peak-to-average power ratio (TH-PAPR) is reduced by 1 step unit and used as the threshold TH-PAPR for the next frame signal.

9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the peak-to-average power ratio suppression method based on adaptive iterative proportional compression soft clipping filtering as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the peak-to-average power ratio suppression method based on adaptive iterative proportional compression soft clipping filtering as described in any one of claims 1 to 7.

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