R-i-pts processing method for suppressing peak-to-average ratio based on fbmc-oqam real data processing

Abstract

Before filtering, this invention performs PTS (Phase-Suppression) based on the real data of the current subcarrier and selects the phase factor corresponding to the minimum PAPR (Phase-Rejection Proportion) value. The updated real data is then filtered and converted into complex data to obtain the transmitted signal. When processing real data blocks, signal cancellation due to superposition is avoided, ensuring signal integrity and optimizing system performance. By using FBMC-OQAM symbols instead of data blocks, this invention increases the number of candidate data sequences, resulting in better PAPR suppression performance. Each FBMC-OQAM symbol selects the optimal phase factor under the current environment. Furthermore, this invention considers signal overlap; when calculating the PAPR value of each sequence, it not only calculates the PAPR value within the current symbol period but also considers the impact of superposition of previous data blocks on the current symbol.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a RI-PTS processing method based on FBMC-OQAM real data processing to suppress peak-to-average power ratio. Background Technology

[0002] Filter bank multicarrier system with offset quadrature amplitude modulation (FBMC-OQAM) is considered a candidate waveform for 5G. FBMC-OQAM has many advantages, such as very high spectral efficiency, suppression of out-of-band leakage, and the ability to utilize fragmented spectrum without requiring synchronization between carriers. One disadvantage of FBMC-OQAM is its high peak-to-average power ratio (PAPR), similar to OFDM. PAPR is an inherent problem in multicarrier systems, which can cause the signal to easily enter the linear range of the power amplifier, leading to signal distortion. Therefore, reducing PAPR is crucial for high-speed transmission systems.

[0003] Common peak-to-average power ratio (PAPR) suppression methods can be broadly classified into two categories: those with signal distortion and those without signal distortion.

[0004] Distortion-related methods: Reducing the peak-to-average power ratio (PAPR) can cause signal distortion, and these can be mainly divided into limiting and companding methods. The basic principle of limiting methods is to restrict the peak envelope of the input signal to a predetermined value. When the signal amplitude is lower than the predetermined value, the signal remains unchanged; when the signal amplitude is higher than the predetermined value, the signal phase remains unchanged, and the signal amplitude is directly reset to the predetermined value. Its disadvantage is that it leads to signal distortion, resulting in in-band distortion, out-of-band radiation, and a decrease in the bit error rate, among other problems. Companding methods mainly include companding and constellation expansion. The basic idea is to compress time-domain signals with larger amplitudes and expand time-domain signals with smaller amplitudes while ensuring that the average power after transformation is equal to the original power. This is relatively easy to implement, but it also suffers from a series of problems caused by signal distortion.

[0005] Distortionless signal transmission methods: These methods do not distort the signal and can be further divided into coding and probabilistic methods. The basic principle of coding methods is to encode the signal using a certain coding scheme, obtaining multiple code groups, and then selecting the code group with the lowest PAPR for transmission. Its disadvantages are increased system complexity and reduced system bandwidth utilization. Probabilistic methods commonly use Selective Mapping (SLM) and Partial Transmission Sequence (PTS). The approach is similar: multiplying the original signal by multiple phase rotation factor vectors to obtain multiple independent signal copies, and then selecting the group with the lowest PAPR for transmission. PAPR reduction is significant, but computational complexity is high. Furthermore, to accurately recover the original data at the receiving end and achieve distortionless transmission, the optimal phase rotation factor vector must be sent as sideband information along with the transmitted signal.

[0006] Because the FBMC system uses a non-rectangular prototype filter function, there is overlap between adjacent symbols. Furthermore, the OQAM modulation technique causes overlap between the real and imaginary parts of the original data. In the FBMC-OQAM system, PAPR is affected not only by its own data block but also by adjacent data blocks. Therefore, the PAPR reduction schemes described above for multi-carrier OFDM cannot be directly applied to the FBMC-OQAM system. To address this issue, existing technologies employ an overlapping SLM (O-SLM) scheme. However, the SLM scheme has high computational complexity. To reduce computational complexity, existing technologies use an iterative partial transmission sequence (PTS) scheme (I-PTS) to search for the optimal weight factor sequence, thus optimizing computational complexity.

[0007] Existing PTS methods process the filtered complex data during phase factor selection, causing the original signal amplitudes to cancel each other out. This process results in the loss of some useful data, degrading the overall signal quality and leading to a suboptimal transmitted signal. Furthermore, the phase factor search conditions in existing PTS methods are concentrated on a single symbol or data block. Adjacent data blocks may overlap, leading to peak regrowth and a significant deterioration in suppression performance. Optimizing one data block can alter the optimization of subsequent blocks, negatively impacting the already optimized blocks. Summary of the Invention

[0008] To address the aforementioned problems in the existing technology, this invention provides a RI-PTS processing method based on FBMC-OQAM real data processing to suppress peak-to-average power ratio (PAPR). The technical problem to be solved by this invention is achieved through the following technical solution:

[0009] This invention provides a RI-PTS processing method based on FBMC-OQAM real data processing to suppress peak-to-average ratio, comprising:

[0010] Step 1: Acquire input data, and perform serial-to-parallel conversion and OQAM modulation on the input data to obtain a symbol sequence composed of FBMC-OQAM symbols;

[0011] Each FBMC-OQAM symbol consists of multiple rows of data;

[0012] Step 2: For any current symbol in the symbol sequence, perform Transmission Sequence (PTS) processing on all rows of data of the current symbol to obtain a set of target symbols, each with a different combination of phase factors;

[0013] Step 3: Feed a set of target symbols into a multiphase network so that the multiphase network can filter the set of target symbols to obtain a filtered set of target symbols.

[0014] Step 4: Determine the window to be processed based on the current symbol's position in the symbol sequence;

[0015] The processing window contains a set of target symbols for the current symbol and the selected optimal target symbol;

[0016] Step 5: Establish an optimization objective function based on the order of the target symbols contained in the window to be processed and the corresponding combination of phase factors;

[0017] Step 6: Solve the objective function to obtain the optimal combination of phase factors, and determine the target symbol corresponding to the optimal combination of phase factors as the optimal target symbol for the current symbol;

[0018] Step 7: Take the next symbol of the current symbol as the current symbol, and repeat the process from Step 2 to Step 6 to obtain the processing result for each FBMC-OQAM symbol.

[0019] Step 8: Add the processing results of each FBMC-OQAM symbol with a staggered sum to obtain the transmitted symbol.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects:

[0021] (1) Considering the overlap problem between signals. In FBMC systems, the traditional PTS algorithm addresses the principle of PAPR generation in multi-carrier systems by reducing the probability of high PAPR. The main idea is to divide each input sequence into blocks, multiply them by different phase factors, and then superimpose them to obtain multiple different input sequences. After modulation, the PAPR value of each sequence is calculated within the current symbol period, and the sequence with the lowest PAPR value is selected as the optimal sequence for transmission. In this invention, considering the overlap problem between signals, when calculating the PAPR value of each sequence, not only is the magnitude of the PAPR value within the current symbol period calculated, but the influence of previous data blocks on the current symbol due to superposition is also considered.

[0022] (2) This invention performs PTS on a symbol-by-symbol basis. The complex input signal of the FBMC-OQAM system used in this invention employs OQAM modulation. The real and imaginary parts of the QAM complex signal are staggered by T / 2 time intervals (T represents one symbol period), and are transmitted as two FBMC symbols. Typically, two adjacent FBMC symbols are treated as a single processing unit, becoming a data block. By using FBMC-OQAM symbols instead of data blocks, the number of candidate data sequences increases, resulting in better PAPR suppression performance. Each FBMC-OQAM symbol makes the optimal choice under the current environment when selecting the optimal phase factor.

[0023] (3) This invention performs PTS in the real data domain. Existing PTS operations are performed in the complex data domain after filtering, which can lead to signal cancellation and distortion, resulting in reduced performance of the filter bank multicarrier system. This invention performs PTS based on the real data of the current subcarrier before filtering, selecting the phase factor corresponding to the minimum PAPR value. The updated real data is then filtered and converted into complex data to obtain the transmitted signal. When processing real data blocks, signal cancellation due to superposition is avoided, ensuring signal integrity and optimizing system performance.

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0025] Figure 1 This is a block diagram of the PTS principle;

[0026] Figure 2 This is a structural diagram of the FBMC-OQAM system based on multiphase networks;

[0027] Figure 3 This is a schematic diagram illustrating the specific implementation steps of FBMC-OQAM based on multiphase networks;

[0028] Figure 4 This is a diagram of the FBMC-OQAM symbol structure;

[0029] Figure 5 This is a flowchart illustrating a RI-PTS processing method for suppressing peak-to-average power ratio based on FBMC-OQAM real data processing provided by the present invention.

[0030] Figure 6 This is a schematic diagram illustrating the specific process of the solution proposed in this invention;

[0031] Figure 7 This is a comparison chart of the peak-to-average power ratio (PAPR) of different PTS algorithms;

[0032] Figure 8This is a graph illustrating the PAPR performance of different block sequence transmission methods used in this invention;

[0033] Figure 9 These are the bit error rate curves for different PTS algorithms. Detailed Implementation

[0034] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0035] Before introducing this invention, a brief overview of the existing knowledge framework and the technical concept of this invention will be provided.

[0036] refer to Figure 1 , Figure 1 This is a block diagram of the PTS principle. Figure 1 As can be seen, the PTS algorithm divides the data into blocks in the frequency domain, with the original data value within each block and the rest padded with zeros. The block-wise data is then subjected to IFFT. The time-domain data is multiplied by different phase factors and then summed to obtain multiple sets of output data. The optimal combination of phase factors and the final output data are determined based on the PAPR of the output values. However, if the real-number data blocks are expanded, all data information can be preserved, the overall power of the transmitting signal is higher, and the receiving end performs accurate demodulation of the transmitted signal. Therefore, this invention proposes to use the Real Number Iteration Partial Transmit Sequence (RI-PTS) algorithm for processing.

[0037] The implementation structure diagram of the FBMC-OQAM system based on multiphase networks is as follows: Figure 2 As shown, the transmitting end consists of two parts: OQAM preprocessing and synthesis filter bank, while the receiving end consists of two parts: analysis filter bank and OQAM postprocessing.

[0038] The specific implementation steps of FBMC-OQAM based on multiphase networks are as follows: Figure 3 As shown. In Figure 3 The input signal is converted from serial to parallel into N sets of parallel data streams based on the number of useful subcarriers N. After OQAM preprocessing, M time-domain symbols are transformed into 2M FBMC-OQAM real symbols. After phase shifting, an N-point IFFT operation is performed, copying the time-domain data by a factor of K and multiplying it by the time-domain coefficients of the prototype filter, thus completing the filtering operation in the time domain. Finally, each FBMC-OQAM symbol is staggered and superimposed.

[0039] refer to Figure 4 , Figure 4This is a diagram of the FBMC-OQAM symbol structure. In the FBMC-OQAM system, the period of a data symbol is no longer T; its symbol length is related to the parameter K selected by the prototype filter. Due to the overlap between symbols, PAPR cannot be defined by the data block length. Therefore, the time-domain transmitted signal is divided into M+K segments with a unit length of T. PAPR is defined as the ratio of the peak power to the average power of each segment, specifically...

[0040]

[0041] In the formula, i = 1, 2, ..., M + K - 1, and E{·} is the mean.

[0042] To verify the effectiveness of methods for reducing PAPR, the complementary cumulative probability distribution function (CCDF) is typically used as a measure of peak-to-average power ratio (PAPR), representing the probability that the power of the time-domain signal in the data exceeds a certain threshold γ. The CCDF can be expressed as...

[0043] CCDF(PAPR0)=P(PAPR0≥γ)

[0044] =1-P(PAPR0<γ)

[0045] =1-(1-e -γ ) N

[0046] The technical solution of this invention is described below, and its performance parameters are compared with those of existing technologies.

[0047] like Figure 5 As shown, this invention provides a RI-PTS processing method based on FBMC-OQAM real data processing to suppress peak-to-average ratio, comprising:

[0048] Step 1: Acquire input data, and perform serial-to-parallel conversion and OQAM modulation on the input data to obtain a symbol sequence composed of FBMC-OQAM symbols;

[0049] Each FBMC-OQAM symbol consists of multiple rows of data;

[0050] Step 2: For any current symbol in the symbol sequence, perform Transmission Sequence (PTS) processing on all rows of data of the current symbol to obtain a set of target symbols, each with a different combination of phase factors;

[0051] In a specific embodiment, step 2 includes:

[0052] Step 21: Divide the row data of each FBMC-OQAM symbol into blocks to obtain sub-blocks;

[0053] Step 22: Perform IFFT transformation on each sub-block to obtain the time-domain data of each block;

[0054] Step 23: Multiply each block of time-domain data by a different phase factor to obtain the multiplication result of the phase factors of each block of time-domain data;

[0055] Step 24: According to the position of the block data in the row data, add the multiplication results to obtain a set of target symbols corresponding to different phase factor combinations;

[0056] Step 25: Determine a set of target symbols as the result of the current symbol after PTS processing.

[0057] Step 3: Feed a set of target symbols into a multiphase network so that the multiphase network can filter the set of target symbols to obtain a filtered set of target symbols.

[0058] In traditional PTS algorithms and some optimization algorithms, the search conditions for phase factors focus only on a single symbol or data block, without considering their overlap characteristics, such as... Figure 4 As shown, because several adjacent data blocks are contained within each other, their superposition leads to peak regeneration and a severe degrade in suppression performance. Optimizing one data block changes the subsequent data blocks, causing the optimization of later data blocks to affect the already optimized ones. To address this problem, this invention performs a Phase-to-Synchronization (PTS) operation based on the real data of the current subcarrier before filtering, selecting the phase factor corresponding to the minimum PAPR value. The updated real data is then filtered and converted into complex data to obtain the transmitted signal. When processing real data blocks, signal cancellation due to superposition is avoided, ensuring signal integrity and optimizing system performance.

[0059] Step 4: Determine the window to be processed based on the current symbol's position in the symbol sequence;

[0060] The processing window contains a set of target symbols for the current symbol and the selected optimal target symbol;

[0061] Step 5: Establish an optimization objective function based on the order of the target symbols contained in the window to be processed and the corresponding combination of phase factors;

[0062] In step 1, there are M constellation mapping symbols, divided into real and virtual symbols, so there are a total of M′ = 2M FBMC-OQAM symbols; each FBMC-OQAM symbol is represented as A. m′ (1≤m′≤2M); In the frequency domain, starting from the first symbol, each symbol... The data is divided into V non-overlapping sub-blocks, with each sub-block having a length of N. Each sub-block is then subjected to an IFFT and multiplied by its corresponding rotation phase factor. The phase factor b is generally defined as follows: v ∈{e j2 π i / W |i=0,1,2,…,W-1}, where W is the number of selectable phases. All possible phase rotation factors are combined into a phase factor candidate set B, and then phase factors are selected from B such that the PAPR of the signal obtained by multiplying and superimposing with the sub-blocks is minimized.

[0063] Therefore, the time-domain signal a after modulating the m′-th data symbol in blocks is... m′ (t), that is, the m′-th target symbol a in a set of target symbols. m′ (t) can be expressed as

[0064]

[0065] In the formula, 1≤m′≤2M, This represents the phase factor that has been selected for the m′-th target symbol and the v-th sub-block. This represents the time-domain signal of the m′-th target symbol and the v-th sub-block after IFFT transformation. This represents the modulation signal of the v-th sub-block of the m′-th target symbol.

[0066] The objective function to be optimized is:

[0067]

[0068] In the formula, This represents the phase factor of the candidate sub-block for the m′-th target symbol. This represents the i-th optimal target symbol that has been selected.

[0069] Step 6: Solve the objective function to obtain the optimal combination of phase factors, and determine the target symbol corresponding to the optimal combination of phase factors as the optimal target symbol for the current symbol;

[0070] Step 7: Take the next symbol of the current symbol as the current symbol, and repeat the process from Step 2 to Step 6 to obtain the processing result for each FBMC-OQAM symbol.

[0071] Step 8: Add the processing results of each FBMC-OQAM symbol with a staggered sum to obtain the transmitted symbol.

[0072] Optimized symbols The output time-domain symbol s is obtained after passing through a polyphase network. m′Finally, by superimposing the 2M optimized symbol data blocks in a staggered manner, the time-domain transmitted signal s(t) can be obtained.

[0073]

[0074] In summary, the phase factor involved in the optimization should be in W of B. V The selection is made from a sequence of phase factors; the larger W and V are, the more candidate phase factors there are, and the better the PAPR suppression performance. However, if an exhaustive selection method is used, the computational complexity increases exponentially. Therefore, W is generally set to 2. m' (t) represents the m′th target symbol after filtering with the optimal phase factor selection.

[0075] See Figure 6 , Figure 6 This is a flowchart illustrating the algorithm proposed in this invention. After the input data undergoes serial-to-parallel conversion and OQAM modulation, it becomes 2M FBMC-OQAM symbols. PTS processing is directly performed on the real data. The data is then divided into blocks and IFFT is performed. Each block of time-domain data is multiplied by a different phase factor and then summed to obtain the PTS-processed data. This data is then fed into a polyphase network structure for further processing. The processed data, combined with the influence of the previous 2K-1 FBMC-OQAM symbols, yields the optimization objective function. The combination of phase factor sequences that minimizes the optimization objective function is solved to obtain the determined PTS-processed symbol data for the current symbol. The PTS operation is then iteratively performed on the next symbol.

[0076] The performance parameters of this invention will be illustrated below through simulation experiments.

[0077] Complexity Analysis: The computational complexity of the proposed algorithm will be calculated and compared with some existing optimized algorithms to verify the feasibility of the proposed algorithm. One calculation of complex multiplication includes four real number multiplications and two real number additions, while one calculation of complex addition includes two real number additions.

[0078] The traditional PTS algorithm for FBMC mainly involves the complexity of KN-point IFFT and phase factor selection. The PTS algorithm for FBMC in this invention mainly involves the complexity of K-point IFFT, PPN, and phase factor selection.

[0079] I-PTS:

[0080] ①The real signal modulated at N points by OQAM is filtered to obtain a complex signal at KN points.

[0081] Real number multiplication: N·KN = KN 2 ;

[0082] ② Perform IFFT on the KN-point complex signal, dividing it into V blocks, which means performing V KN-point IFFTs.

[0083] Complex number multiplication:

[0084] Complex addition:

[0085] ③ Phase factor selection: The data after IFFT is multiplied by the corresponding phase factor and then added together to obtain the data after PTS processing. Since the phase factor is {1, -1}, the multiplication operation is a real number multiplication.

[0086] Real number multiplication: KN·V·W V ;

[0087] Complex addition: KN·(V-1)·W V ;

[0088] ④ Obtain the optimal FBMC symbol using a defined phase factor;

[0089] Real number multiplication: KN·V;

[0090] Complex addition: KN·(V-1);

[0091] There are a total of 2M FBMC-OQAM symbols;

[0092] Real number multiplication:

[0093] Addition of real numbers:

[0094] The computational complexity of the RI-PTS of this invention is: one FBMC symbol;

[0095] ① Perform an N-point IFFT on the real number symbol of FBMC, divide it into V blocks, that is, perform V N-point IFFTs.

[0096] Real number multiplication:

[0097] Real number addition:

[0098] ②The data after IFFT is multiplied by the corresponding phase factor and then added together to obtain the data after PTS processing. Since the phase factor is {1, -1}, the multiplication operation is a real number multiplication.

[0099] Real number multiplication: N·V·W V ;

[0100] Complex addition: N·(V-1)·W V ;

[0101] ③ Pass the time-domain signal through the filter and solve the PTS objective function.

[0102] Complex multiplication: KN·W V ;

[0103] ④ Obtain the optimal FBMC symbol

[0104] Complex multiplication: KN;

[0105] Real number multiplication: N·V;

[0106] Complex addition: N·(V-1);

[0107] There are a total of 2M FBMC-OQAM symbols;

[0108] Real number multiplication:

[0109] Real number addition:

[0110] Table 1. Complexity Analysis: N = 128, K = 4, M = 30, V = 4, W = 2

[0111] Real number multiplication real number addition RI-PTS 43264x60＝2595840 11520x60＝691200 I-PTS 137216x60＝8232960 107520x60＝6451200

[0112] As can be seen from Table 1, the complexity of this invention is significantly reduced in terms of both real number multiplication and real number addition.

[0113] See Figure 7 ,from Figure 7 As can be seen, the traditional PTS algorithm's performance in reducing PAPR in the FBMC system is greatly suppressed due to its overlapping structure. The performance using the PTS algorithm is only around 10... -4 The reduction is only 1.2-1.4 dB. Before filtering, the PTS algorithm (RI-PTS) and the traditional PTS algorithm show little difference in PAPR suppression performance, with the original system showing a reduction of 10 dB. -4 A reduction of 1.6-1.8 dB. Considering the preceding overlap, the DI-PTS algorithm, using data blocks (real and imaginary parts of FBMC symbols) as units, achieves a 10 dB reduction compared to the original system. -4 The PAPR is reduced by approximately 3dB. The I-PTS algorithm, which uses FBMC-OQAM symbols as the unit, and the algorithm using FBMC-OQAM symbols before filtering have roughly the same PAPR reduction performance, reducing PAPR by approximately 3.8-4dB compared to the original system.

[0114] See Figure 8 ,from Figure 8As can be seen, the number of blocks affects the performance of PAPR suppression; more blocks result in better performance, but also higher computational complexity. When the number of blocks is 2, 4, and 8, with 10... -4 Based on the standard, PAPR can effectively reduce peak-to-average power ratio by 2dB, 3.6dB, and 5dB respectively. When the number of blocks is 8, the peak-to-average power ratio can be reduced to below 6dB.

[0115] See Figure 9 ,from Figure 9 As can be seen, performing PTS operation on the real data portion can achieve the same bit error rate as the original system without loss. However, if PTS operation is performed on the complex data domain after filtering, signal cancellation occurs, resulting in signal distortion and degrading the performance of the filter bank multicarrier system.

[0116] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0117] Although this application has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality.

[0118] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A R-I-PTS processing method for suppressing peak-to-average ratio based on FBMC-OQAM real data processing, characterized in that, The method comprises the following steps: Step 1, obtaining input data, and performing serial-parallel conversion and OQAM modulation on the input data to obtain a symbol sequence composed of FBMC-OQAM symbols; Each FBMC-OQAM symbol is composed of a plurality of row data; Step 2, for any current symbol in the symbol sequence, performing PTS processing on all row data of the current symbol to obtain a group of target symbols, each target symbol having a different phase factor combination; Step 3, sending the group of target symbols to a polyphase network to perform filtering processing on the group of target symbols by the polyphase network to obtain a group of filtered target symbols; Step 4, determining a to-be-processed window according to the position of the current symbol in the symbol sequence; The to-be-processed window contains a group of target symbols of the current symbol and an optimal target symbol selected; Step 5, establishing an optimization objective function according to the sequence in which the to-be-processed window contains the target symbols and the corresponding phase factor combination; Step 6, solving the optimization objective function to obtain an optimal phase factor combination, and determining a target symbol corresponding to the optimal phase factor combination as an optimal target symbol of the current symbol; Step 7, taking a next symbol of the current symbol as the current symbol, and repeating the process of steps 2 to 6 to obtain a processing result of each FBMC-OQAM symbol; Step 8, adding the processing results of each FBMC-OQAM symbol to obtain a sending symbol.

2. The R-I-PTS processing method for suppressing PAPR based on FBMC-OQAM real data processing according to claim 1, characterized in that, Step 2 comprises: Step 21, dividing the row data of each FBMC-OQAM symbol into blocks to obtain sub-blocks; Step 22, performing IFFT transformation on each sub-block to obtain time-domain data of each block; Step 23, multiplying each block of time-domain data by a different phase factor to obtain a multiplication result of each block of time-domain data and the phase factor; Step 24, adding the multiplication results according to the positions of the divided data in the row data to obtain a group of target symbols corresponding to different phase factor combinations; Step 25, determining the group of target symbols as a result of PTS processing of the current symbol.

3. The R-I-PTS processing method for suppressing PAPR based on FBMC-OQAM real data processing according to claim 2, characterized in that, The constellation mapping symbol in step 1 has M, which is divided into imaginary and real symbols, and the FBMC-OQAM symbol has a total of ; each FBMC-OQAM symbol is represented as ; Step 2 comprises: Step 21 comprises splitting each symbol into V non-overlapping sub-blocks, and ensuring that the length of each sub-block is N ; Step 23 comprises: multiplying each block of time-domain data by a corresponding phase factor; where the phase factor is W is the number of optional phases, and all possible phase rotation factor combinations are the phase factor candidate set B.

4. The R-I-PTS processing method for suppressing PAPR based on FBMC-OQAM real data processing according to claim 3, characterized in that, The first target symbol in the group of target symbols in step 2 may be represented as may be represented as In the formula, , Indicates the first The phase factor of the vth sub-block of the target symbol has been selected. Indicates the first The time-domain signal of the v-th sub-block of the target symbol after IFFT transformation. Indicates the first The modulation signal of the vth sub-block of the target symbol.

5. The R-I-PTS processing method for suppressing PAPR based on FBMC-OQAM real data processing according to claim 4, characterized in that, The optimization function of step 5 is: wherein denotes the phase factor of the vth subblock alternative of the th target symbol, , denotes the selected i th optimal target symbol, denotes the symbol period, denotes the filter parameter.

6. The R-I-PTS processing method for suppressing PAPR based on FBMC-OQAM real data processing according to claim 5, characterized in that, The sending symbol in step 8 is: ； wherein, represents the filtered 2n-1th target symbol after optimal phase factor selection. represents the filtered 2n-1th target symbol after optimal phase factor selection.

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