FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) adaptive bit cutting method and system

Abstract

The invention relates to the technical field of digital signal processing, and discloses an FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) adaptive bit cutting method and system. The method comprises the following steps: dynamically selecting to intercept a low bit or a high bit by judging a high bit state of data after each level of butterfly operation, avoiding data precision loss and sign bit error caused by fixed bit interception, accumulating and recording bit interception loss of FFT and IFFT operations, and combining an amplitude compensation mechanism to enable the amplitude of an output time domain signal to be consistent with the amplitude of an input signal. According to the invention, the problems of data precision loss and sign bit error caused by a traditional fixed bit cutting mode are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital signal processing, in particular to an FFT and IFFT adaptive clipping method and system. BACKGROUND

[0002] FFT and IFFT are widely used in the field of digital signal processing as fast algorithms of discrete Fourier transform. In hardware implementation, fixed-point operation is usually used to reduce resource consumption and power consumption. When implementing fixed-point FFT, the bit width of input data is assumed to be m, and the data bit width is expanded to m+n after each butterfly operation, where n is determined by the radix of the FFT algorithm. For radix-2 algorithm, n is equal to one, and for radix-4 algorithm, n is equal to two. In order to ensure the cyclic execution of each butterfly operation and resource reuse, clipping processing is required after each butterfly operation to restore the bit width to m, so that the data after clipping can be used as the input of the next butterfly operation. In practical applications, FFT and IFFT are often used together. For example, the Fourier domain narrowband interference suppression algorithm needs to perform FFT transform on the received signal to the frequency domain, detect and delete the interference frequency point, and then perform IFFT transform to restore the time domain signal. This process requires that the signal amplitude input to the FFT and the signal amplitude after the IFFT remain basically the same.

[0003] The traditional clipping method uses a fixed strategy. The most conservative method is to fix the low n bits after each butterfly operation. Although this method is simple, the effective bit width of the data after the butterfly operation is uncertain, and fixed clipping of low bits will cause loss of effective bits, resulting in a decrease in signal-to-noise ratio. Another way is to fix the high bits to retain the high bit effective part of the data, but when the effective bit width of the data does not exceed m bits, clipping high bits will discard the effective data of low bits, also causing precision loss. Moreover, the fixed clipping strategy cannot adjust the clipping method according to the actual state of the data after each operation, making it difficult to minimize data loss while ensuring the correctness of the sign bit. SUMMARY

[0004] The present application provides an FFT and IFFT adaptive clipping method and system to solve the problems of data precision loss and sign bit error caused by traditional fixed clipping methods. By adaptively judging the high bit state of the data after each butterfly operation, the clipping strategy is dynamically selected, and the clipping loss is accumulated and recorded. Combined with the amplitude compensation mechanism, the signal amplitudes before and after FFT and IFFT operation are kept consistent.

[0005] In a first aspect, the present application provides an FFT and IFFT adaptive clipping method, which comprises: Step S1, inputting a zero intermediate frequency time domain signal into an FFT operation unit to perform a butterfly operation, determining a clipping indication by judging a high bit state of frequency point data after each stage of the butterfly operation, performing low bit clipping or high bit clipping on the frequency point data according to the clipping indication and accumulating a clipping bit number, obtaining spectrum data and a first total clipping bit number; Step S2, identifying a narrowband interference position according to an amplitude characteristic of each frequency point in the spectrum data, setting interference frequency points to zero, and obtaining suppressed spectrum data; Step S3, performing conjugate transformation on the suppressed spectrum data, inputting the conjugate data into the FFT operation unit to perform a butterfly operation and perform clipping processing according to the clipping indication, performing conjugate transformation on the operation result again, obtaining time domain recovery data and a second total clipping bit number; Step S4, summing the first total clipping bit number and the second total clipping bit number to obtain a total clipping bit number; Step S5, performing compensation clipping on the time domain recovery data based on a difference between an FFT point number and the total clipping bit number, and obtaining an output time domain signal.

[0006] In a second aspect, the present application provides an FFT and IFFT adaptive clipping system, which comprises: An input module is configured to input a zero intermediate frequency time domain signal into an FFT operation unit to perform a butterfly operation, determine a clipping indication by judging a high bit state of frequency point data after each stage of the butterfly operation, perform low bit clipping or high bit clipping on the frequency point data according to the clipping indication and accumulate a clipping bit number, obtain spectrum data and a first total clipping bit number; An identification module is configured to identify a narrowband interference position according to an amplitude characteristic of each frequency point in the spectrum data, set interference frequency points to zero, and obtain suppressed spectrum data; A clipping module is configured to perform conjugate transformation on the suppressed spectrum data, input the conjugate data into the FFT operation unit to perform a butterfly operation and perform clipping processing according to the clipping indication, perform conjugate transformation on the operation result again, obtain time domain recovery data and a second total clipping bit number; A summation module is configured to sum the first total clipping bit number and the second total clipping bit number to obtain a total clipping bit number; A compensation module is configured to perform compensation clipping on the time domain recovery data based on a difference between an FFT point number and the total clipping bit number, and obtain an output time domain signal.

[0007] In a third aspect, an FFT and IFFT adaptive clipping device is provided, which comprises a memory and at least one processor, the memory stores instructions; the at least one processor invokes the instructions in the memory to enable the FFT and IFFT adaptive clipping device to perform the FFT and IFFT adaptive clipping method described above.

[0008] In a fourth aspect, a computer readable storage medium is provided, in which instructions are stored, when running on a computer, cause the computer to execute the FFT and IFFT adaptive clipping method described above.

[0009] In the technical scheme provided in the present application, the adaptive clipping strategy solves the problems of data precision loss and sign bit error in fixed clipping mode in fixed-point FFT and IFFT operation. In step S1, when the zero intermediate frequency time domain signal is input into the FFT operation unit for butterfly operation, the clipping indication is determined by judging the high bit state of the frequency point data after each level of butterfly operation. The judgment mechanism finds the maximum value of the real part absolute value and the imaginary part absolute value in all frequency point data, checks whether the high n+1 bit of the maximum value is all zero, and if all zero, it means that the effective bit width of the data does not exceed the bit width limit and there is no risk of overflow, so the low bits can be clipped to retain more effective data. If it is not all zero, it means that the effective bit width of the data exceeds the limit and there is a risk of overflow, so the high bits must be clipped to prevent sign bit error. The adaptive judgment dynamically selects the clipping mode according to the actual state of the data after each level of operation, which avoids the sign bit error and numerical overflow caused by fixed clipping of low bits, and avoids the loss of low bit effective data caused by fixed clipping of high bits. On the premise of ensuring the correctness of the sign bit, the data precision loss is minimized. According to the clipping indication, the low bits or high bits of the frequency point data are clipped, and the total number of clipping bits is accumulated to obtain the first total number of clipping bits. The accumulation mechanism accurately records the total number of bits moved to the right due to clipping of high bits in the FFT operation process, providing a quantitative basis for subsequent amplitude compensation. In step S3, when the IFFT operation is performed on the suppressed spectrum data, the IFFT operation is converted into FFT operation through conjugate transformation. In the FFT operation process, the same adaptive clipping strategy as in step S1 is applied. Similarly, the clipping indication is determined according to the high bit state of the intermediate time domain data after each level of butterfly operation, and the total number of clipping bits is accumulated to obtain the second total number of clipping bits. The consistent processing ensures the uniformity of the clipping strategy in FFT and IFFT operation and the integrity of the clipping loss record. In step S4, the first total number of clipping bits and the second total number of clipping bits are summed to obtain the total number of clipping bits, which quantifies the cumulative amplitude attenuation caused by the adaptive clipping strategy in the entire FFT and IFFT operation link.

[0010] The time domain recovery data is compensated by the difference between the FFT point number and the total clip bit number in step S5, the compensation mechanism considers the characteristic that FFT and IFFT operation theoretically expands the data amplitude by the FFT point number, the theoretical amplitude expansion amount is obtained by taking the logarithm of the FFT point number, the compensation clip amount is obtained by subtracting the total clip bit number from the logarithm value, the compensation clip amount reflects the net difference between the amplitude expansion effect and the amplitude reduction effect, the time domain recovery data is right shifted and the low bit width part is clipped according to the compensation clip amount, so that the amplitude of the output time domain signal is basically consistent with the amplitude of the input zero intermediate frequency time domain signal, the amplitude consistency is crucial for Fourier domain narrowband interference suppression application, and the subsequent signal processing error caused by amplitude deviation is avoided, the adaptive clipping strategy of the application has a significant advantage in signal-to-noise ratio performance compared with the traditional fixed clipping mode, the data precision loss of each operation is reduced by dynamically selecting the clipping mode, and the precision advantage is more obvious after the multi-stage butterfly operation is accumulated, the signal amplitude consistency before and after the FFT and IFFT operation is ensured by combining the accurate clip loss record and the amplitude compensation mechanism, the whole scheme algorithm is simple and easy to realize in hardware, does not need complex arithmetic operation and additional storage resource, and only needs high bit state judgment and simple clipping selection to realize the adaptive function, is suitable for deployment on the hardware platform such as FPGA and ASIC, and provides an effective technical scheme for efficient implementation of fixed-point FFT and IFFT. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 An embodiment of the FFT and IFFT adaptive clipping method in the present application is shown in the figure. Figure 2 An embodiment of the FFT and IFFT adaptive clipping system in the present application is shown in the figure. Figure 3 The structure schematic diagram of the FFT and IFFT adaptive clipping device in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide an FFT and IFFT adaptive clipping method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not have to be used to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "comprise" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0014] For the convenience of understanding, the specific flow of the embodiments of the present application is described below, please refer to Figure 1 One embodiment of the FFT and IFFT adaptive clipping method in the embodiments of the present application includes: Step S1, inputting a zero intermediate frequency time domain signal into an FFT operation unit to perform butterfly operation, determining a clipping indication by judging the high bit state of the frequency point data after each stage of butterfly operation, performing low bit clipping or high bit clipping on the frequency point data according to the clipping indication and accumulating the clipping bit number, obtaining spectrum data and a first total clipping bit number; Step S2, identifying the position of narrowband interference according to the amplitude characteristics of each frequency point in the spectrum data, setting the interference frequency point to zero, and obtaining suppressed spectrum data; Step S3, performing conjugate transformation on the suppressed spectrum data, inputting the conjugate data into the FFT operation unit to perform butterfly operation and clipping processing according to the clipping indication, and performing conjugate transformation on the operation result again, obtaining time domain recovery data and a second total clipping bit number; Step S4, summing the first total clipping bit number and the second total clipping bit number to obtain a total clipping bit number; Step S5, compensating the time domain recovery data based on the difference between the FFT point number and the total clipping bit number to obtain an output time domain signal.

[0015] It can be understood that the execution subject of the present application can be an FFT and IFFT adaptive clipping system, and can also be a terminal or a server, which is not limited here. The embodiments of the present application take the server as the execution subject for example.

[0016] Specifically, the problem of data bit width control in the FFT and IFFT operation is solved by an adaptive clipping strategy. When the zero intermediate frequency time domain signal is input into the FFT operation unit, the data bit width is expanded in each butterfly operation. The fixed clipping strategy in the traditional method is easy to cause data loss or sign bit error. The present application adaptively determines the clipping mode by judging the high bit state of the frequency point data after each butterfly operation. Specifically, the maximum value of the real part absolute value and the imaginary part absolute value of all frequency point data is found, and it is checked whether the high bits of the maximum value are all zero. If they are all zero, it means that the effective bit width of the data does not exceed the bit width limit, and the low bits can be clipped to preserve more effective data. If they are not all zero, it means that the effective bit width of the data exceeds the limit and the high bits must be clipped to prevent overflow. At the same time, the total number of bits of the clipped high bits is accumulated to obtain the first total number of bits of clipping. This adaptive judgment mechanism not only avoids sign bit error but also maximally reduces the loss of data precision. The spectrum data is obtained after multiple butterfly operations.

[0017] When identifying narrowband interference according to the spectrum data, the amplitude feature of each frequency point, i.e. the sum of the square of the real part and the square of the imaginary part, is calculated. The amplitude feature reflects the energy intensity of each frequency component. The spectrum energy distribution of the normal signal is relatively uniform, while the narrowband interference forms an abnormal energy peak at a specific frequency point. A reasonable interference detection threshold is set by statistical analysis of the spectrum energy distribution. The amplitude features of each frequency point are compared with the threshold. The frequency points exceeding the threshold are marked as narrowband interference positions. The real part and the imaginary part of these interference frequency points are both set to zero to complete interference suppression, and the suppressed spectrum data is obtained. This data not only removes the interference but also preserves the spectrum features of the useful signal.

[0018] When implementing the IFFT operation, the mathematical relationship between the inverse discrete Fourier transform and the forward transform is used. The opposite of the imaginary part of each frequency point of the suppressed spectrum data is taken to complete the first conjugate transform. The conjugate data is sent into the FFT operation unit to perform the same butterfly operation and adaptive clipping process as in step S1. The high bit state of the intermediate time domain data after each butterfly operation is also judged to determine the clipping indication. According to the clipping indication, the low bits or the high bits are selected and the number of clipping bits is accumulated to obtain the second total number of bits of clipping. After completing all levels of butterfly operation, the opposite of the imaginary part of each data point of the operation result is taken again to complete the second conjugate transform. Through the three-step operation of conjugate-FFT-conjugate, the inverse IFFT transform is equivalent to be realized, and the time domain recovery data and the second total number of bits of clipping are obtained.

[0019] The first total number of bits of clipping and the second total number of bits of clipping are added to obtain the total number of bits of clipping. This total number of bits of clipping reflects the cumulative amplitude attenuation caused by high bit clipping in the entire FFT and IFFT operation process. This value is an important basis for subsequent amplitude compensation.

[0020] When the amplitude compensation is based on the difference between the FFT point number and the total clipping bit number, the theoretical amplitude expansion amount is obtained by taking the logarithm of the FFT point number, the compensation clipping amount is obtained by subtracting the total clipping bit number from the logarithm value, the compensation clipping amount represents the net difference between the amplitude expansion effect caused by the FFT and IFFT operations and the amplitude reduction effect caused by the adaptive clipping, the right shift operation is performed on the time domain recovery data according to the compensation clipping amount, the real part and the imaginary part of each data point are shifted right by the corresponding bit number, the low bit part of the shifted data is intercepted and the high bit is filled with the sign bit, and the output time domain signal is obtained, the amplitude of the output time domain signal is basically consistent with the amplitude of the input zero intermediate frequency time domain signal, and the complete interference suppression processing flow from the time domain to the frequency domain and then to the time domain is completed.

[0021] In a specific embodiment, step S1 comprises: The input data quantization bit width is set as m, the butterfly operation data expansion bit width is set as n, the butterfly operation level number is set as L, and the first total clipping bit number is initialized as zero; The i-th level butterfly operation is performed on the zero intermediate frequency time domain signal to obtain N intermediate frequency point data, where the initial value of i is 1; The maximum value is found in the real part absolute value and the imaginary part absolute value of the N intermediate frequency point data, it is judged whether the high n+1 bit of the maximum value is all 0, if all 0, the clipping indication flag is marked as 0, if not all 0, the clipping indication flag is marked as 1; When the clipping indication is 0, the low m bit of the N intermediate frequency point data is intercepted to obtain the clipped frequency point data, when the clipping indication is 1, the high m bit of the N intermediate frequency point data is intercepted to obtain the clipped frequency point data, the first total clipping bit number is accumulated by n bits, the clipped frequency point data is taken as the input data of the next level butterfly operation, i is set as i+1, the butterfly operation, the judgment and the clipping operation are repeated until i is greater than L, and the spectrum data and the first total clipping bit number are obtained.

[0022] Specifically, three key parameters need to be set before performing the FFT operation, the input data quantization bit width m represents the bit number of each data point of the input time domain signal, the butterfly operation data expansion bit width n represents the increase amount of the data bit width after each level of butterfly operation relative to the input bit width, and the butterfly operation level number L represents the number of butterfly operations required to complete the entire FFT operation. The expansion bit width n is determined by the radix of the FFT algorithm, the radix-2 algorithm involves two-point addition and subtraction operations, resulting in an increase of one bit in the bit width, so n is equal to one, and the radix-4 algorithm involves four-point complex operations, resulting in an increase of two bits in the bit width, so n is equal to two. The butterfly operation level number L is related to the algorithm radix and the FFT point number, when the radix-2 algorithm is used, L is the logarithm of the point number with two as the base, when the radix-4 algorithm is used, L is the logarithm of the point number with four as the base, and the first total clipping bit number is initialized as zero before starting the operation, which is used to record the total bit number lost due to the clipping of the high bit during the FFT operation.

[0023] When performing butterfly operations on a zero-IF time-domain signal, the initial value of the stage counter i is set to one. The i-th stage butterfly operation performs complex multiplication and addition / subtraction operations on the input data according to the rotation factor and data rearrangement rules of the FFT algorithm to obtain N intermediate frequency data points. The bit width of these intermediate frequency data points is m plus n. Since the butterfly operation involves complex number operations and multi-point addition, the bit width will inevitably be expanded. After obtaining N intermediate frequency data points, it is necessary to determine whether there is an overflow risk. Iterate through all the intermediate frequency data points, taking the absolute values ​​of the real and imaginary parts, and find the maximum value among these absolute values. This maximum value represents the peak value of the data amplitude after this level of operation. Determine whether the higher n plus one bit of this maximum value is all zero. The higher n plus one bit includes the extended n bits and the highest bit of the original bit width. If these higher bits are all zero, it means that the effective value part of the data falls completely within the range of the lower m bits, and the higher bits are only the sign bit extension and invalid zero bits. In this case, mark the truncation indicator as zero to indicate that there is no overflow risk. If these higher bits are not all zero, it means that the effective value part of the data exceeds the representation range of the lower m bits. The higher bits contain effective data and must be retained. In this case, mark the truncation indicator as one to indicate that there is an overflow risk.

[0024] Different truncation strategies are applied to N intermediate frequency data points based on the truncation indicator flag value. When the truncation indicator is zero, the lower m bits of these data points are truncated to obtain the truncated frequency data. Since the higher bits are all sign bits, truncating the lower bits will not lose valid data and will preserve data precision. At this time, the total number of bits in the first truncation remains unchanged. When the truncation indicator is one, the higher m bits of these data points are truncated to obtain the truncated frequency data. Since the higher bits contain valid values, the higher bits must be retained to prevent data overflow. At this time, the total number of bits in the first truncation is accumulated by n bits to record the number of bits shifted to the right due to the higher bit truncation. The truncated frequency data is used as input data for the next stage of butterfly operation. The bit width of these data is restored to m bits to meet the requirements of the cyclic operation. The stage counter i is set to i+1 to point to the next stage operation. The butterfly operation is repeated to obtain N new intermediate frequency data. The state of the high-order bit of the maximum value is determined to determine the truncation indicator. Truncation processing is performed according to the truncation indicator and the total number of truncation bits is updated. This cyclic process continues until i is greater than L, indicating that all L stages of butterfly operation have been completed. Finally, N spectrum data and the first total number of truncation bits are obtained. The bit width of the spectrum data is kept at m bits, while the first total number of truncation bits records the accumulated truncation loss in the entire FFT operation. This adaptive truncation strategy dynamically selects the truncation method according to the actual effective bit width of the data after each stage of operation, which ensures the correctness of the sign bit and minimizes the loss of data precision.

[0025] In one specific embodiment, step S2 includes: The amplitude squared value of each frequency point is obtained by squaring the real part and imaginary part of each frequency point and then summing them. calculate a spectrum energy distribution statistical feature based on the amplitude square values, and set an interference detection threshold; compare the amplitude square values with the interference detection threshold, and mark the frequency point position exceeding the interference detection threshold as a narrowband interference position; set the real part and the imaginary part of the frequency point data corresponding to the narrowband interference position to zero, and obtain the suppressed spectrum data.

[0026] Specifically, when calculating the amplitude square value of each frequency point in the spectrum data, the real part and the imaginary part of the frequency point data are extracted respectively, the real part is multiplied by itself to obtain the real part square value, the imaginary part is multiplied by itself to obtain the imaginary part square value, and then the real part square value and the imaginary part square value are added to obtain the amplitude square value of the frequency point. The amplitude square value is mathematically equivalent to the square of the modulus of a complex number, and its physical meaning is the energy intensity of the frequency component corresponding to the frequency point. After performing the operation on all N frequency points of the spectrum data, N amplitude square values are obtained, which constitute the spectrum energy distribution. The spectrum energy distribution of the normal signal is relatively uniform, and the energy difference between each frequency point is small. The narrowband interference is concentrated in a specific frequency point, which will cause the amplitude square value of the frequency point to be significantly higher than that of other frequency points, forming an abnormal energy peak value.

[0027] When calculating the spectrum energy distribution statistical feature based on the amplitude square values of all frequency points, the N amplitude square values are taken as samples for statistical analysis. The average value is calculated to reflect the overall energy level, the variance or standard deviation is calculated to reflect the dispersion degree of the energy distribution, and the median is calculated to reflect the central tendency of the energy distribution. According to these statistical features, the interference detection threshold is set as a threshold for judging whether the energy of the frequency point is abnormal. The setting method of the interference detection threshold includes the fixed multiple method, i.e. multiplying the average value by a preset multiple factor, the standard deviation method, i.e. adding several times of the standard deviation to the average value, and the percentile method, i.e. selecting a certain high percentile value of the energy distribution. The specific value of the threshold needs to be balanced between the interference detection sensitivity and the false alarm rate. If the threshold is too low, the normal signal will be misjudged as interference, and if the threshold is too high, the weak interference signal will be missed. By reasonably setting the threshold, the normal signal frequency point and the interference frequency point can be effectively distinguished.

[0028] When the amplitude square value of each frequency point is compared with the set interference detection threshold, if the amplitude square value of a certain frequency point is greater than the interference detection threshold, it indicates that the energy of the frequency point is abnormally high, and there is narrowband interference, and the index position of the frequency point is marked as the narrowband interference position; if the amplitude square value is less than or equal to the interference detection threshold, it indicates that the energy of the frequency point is in a normal range, and the index position of the frequency point is marked as the normal signal position. After the comparison and marking of all N frequency points are completed, a series of index sets of narrowband interference positions are obtained, and these indexes correspond to the frequency components in the spectrum that are disturbed and polluted, and the marking process is essentially a binary classification based on the energy threshold to divide the frequency points into the interference class and the signal class.

[0029] When the frequency point data marked as the narrowband interference position is zeroed, the corresponding frequency point in the spectrum data is found according to the marked index, the real part of the frequency point data is set to zero, and the imaginary part is also set to zero. The zeroing operation completely eliminates the energy contribution of the frequency point, and the zeroing operation is performed on all frequency points marked as the narrowband interference position to complete the interference suppression, and the normal signal frequency points that are not marked remain unchanged. After the processing is completed, the suppressed spectrum data is obtained, the interference frequency points in the suppressed spectrum data are zeroed, and the useful signal frequency points retain the original values, thereby realizing effective suppression of the narrowband interference in the frequency domain. The suppressed spectrum data removes the interference pollution and retains the spectral characteristics of the signal.

[0030] In a specific embodiment, step S3 comprises: Taking the opposite of the imaginary part of each frequency point in the suppressed spectrum data to obtain conjugated data; The conjugated data is input into an FFT operation unit, a second total number of bits is initialized to zero, j-level butterfly operation is performed on the conjugated data to obtain N intermediate time domain data, the maximum value is found in the real part absolute value and the imaginary part absolute value of the N intermediate time domain data, and the high n+1 bit state of the maximum value is judged. According to the judgment result, the bit cutting indication is determined, and the N intermediate time domain data is cut and the number of bits is accumulated. The cut data is used as the input data of the next level butterfly operation, and j=j+1. The butterfly operation, judgment and cutting operation are repeated until j is greater than L, and the operation result data and the second total number of bits are obtained, where the initial value of j is 1. Taking the opposite of the imaginary part of each data point in the operation result data to obtain the time domain recovery data.

[0031] Specifically, when performing the conjugate transform on the post-suppression frequency spectrum data, the real part and the imaginary part of each frequency point data are extracted one by one, the real part is kept unchanged, the imaginary part is multiplied by -1 to get the opposite number of the imaginary part, and the unchanged real part and the opposite imaginary part are combined to form the conjugate post-suppression data of the frequency point. The conjugate operation of a complex number is geometrically equivalent to mirroring a point on a complex plane about the real axis, and in algebraic operation, the conjugate operation changes the phase sign of the complex number but keeps the amplitude unchanged. After performing the conjugate operation on all N frequency points of the post-suppression frequency spectrum data, the complete conjugate post-suppression data sequence is obtained. According to the mathematical properties of the discrete Fourier transform, the inverse discrete Fourier transform can be represented as performing the discrete Fourier transform on the input sequence after taking the conjugate, and then taking the conjugate of the result. Therefore, the inverse transform IFFT is equivalent to a three-step operation of conjugate-FFT-conjugate. The conjugate post-suppression data is input into the FFT operation unit to reuse the existing FFT processing core to realize the IFFT function. Before inputting the FFT operation unit, the second total number of bits of truncation is initialized to zero. This parameter is used to record the truncation loss in the IFFT operation process.

[0032] When performing the butterfly operation on the conjugate post-suppression data, the initial value of the stage number counter j is set to one. The jth stage butterfly operation processes the conjugate post-suppression data according to the same FFT algorithm rules as step S1, performs the twiddle factor multiplication and the butterfly addition and subtraction operation to obtain N intermediate time domain data, and the bit width of these intermediate time domain data is expanded from m to m+n. After obtaining the N intermediate time domain data, the absolute values of the real parts and the absolute values of the imaginary parts of all data are traversed to find the maximum value, which represents the peak value of the amplitude of the data after the operation of this stage. It is judged whether the high n+1 bits of the maximum value are all zero. If the high n+1 bits are all zero, it means that the effective bit width of the data does not exceed m bits and there is no risk of overflow, so the truncation indication flag is set to zero. If the high n+1 bits are not all zero, it means that the effective bit width of the data exceeds m bits and there is a risk of overflow, so the truncation indication flag is set to one. According to the marked value of the truncation indication, the N intermediate time domain data are subjected to truncation processing. When the truncation indication is zero, the low m bits of these data are truncated to obtain the post-truncation data, and the second total number of bits of truncation remains unchanged. When the truncation indication is one, the high m bits of these data are truncated to obtain the post-truncation data, and the second total number of bits of truncation is accumulated by n bits. The post-truncation data is taken as the input data of the next stage butterfly operation, the stage number counter j is set to j+1, the butterfly operation is repeatedly performed to obtain new N intermediate time domain data, the state of the high bit of the maximum value is judged to determine the truncation indication, the truncation processing is performed according to the truncation indication, and the number of truncated bits is accumulated. This loop continues until j is greater than L, and all L stages of butterfly operation are completed, to obtain N operation result data and the second total number of bits of truncation. The operation result data is the output of the FFT forward transform performed on the conjugate post-suppression data, and the second total number of bits of truncation records the cumulative truncation loss in the process.

[0033] When performing the second conjugate transformation on the operation result data, the real part and the imaginary part of each data point are extracted one by one, the real part value is kept unchanged, the imaginary part is multiplied by -1 to obtain the opposite number of the imaginary part, and the unchanged real part and the negated imaginary part are recombined to form the time domain recovery data of the data point. The second conjugate transformation has the same operation as the first conjugate transformation but different effects. The first conjugate transforms the frequency domain data into the input form of FFT, and the second conjugate transforms the output of FFT into the real IFFT result. After performing the conjugate operation on all N data points of the operation result data, a complete time domain recovery data sequence is obtained. The time domain recovery data is the inverse Fourier transform result of the suppressed spectrum data, and the interference suppression effect in the frequency domain is converted back to the time domain signal. Since the FFT operation performed between the two conjugate transformations adopts the same adaptive clipping strategy as step S1, the second clipping total bit number accurately records the accumulated clipping loss in the IFFT operation process.

[0034] In a specific embodiment, step S4 comprises: obtaining the first clipping total bit number and the second clipping total bit number; performing an addition operation on the first clipping total bit number and the second clipping total bit number to obtain a clipping accumulation value; taking the clipping accumulation value as the total clipping bit number; comparing the logarithmic values of the total clipping bit number and the FFT point number to determine the compensation clipping amount.

[0035] Specifically, when obtaining the first clipping total bit number and the second clipping total bit number, the first clipping total bit number accumulated in the FFT operation process of step S1 is extracted. This value records the sum of the extended bit width n accumulated when the clipping indication is marked as one in each stage of the FFT butterfly operation, and reflects the total bit number moved to the right due to the clipping of high bit data in the FFT operation process. The second clipping total bit number accumulated in the IFFT operation process of step S3 is extracted. This value records the sum of the extended bit width n accumulated when the clipping indication is marked as one in each stage of the IFFT butterfly operation, and reflects the total bit number moved to the right due to the clipping of high bit data in the IFFT operation process. The two clipping total bit numbers respectively quantify the data amplitude attenuation caused by the adaptive clipping strategy in the frequency domain transformation and inverse transformation processes. The larger the first clipping total bit number, the more stages in the FFT operation need to clip high bit data and move to the right. The larger the second clipping total bit number, the more stages in the IFFT operation need to clip high bit data and move to the right.

[0036] When the first partial bit number and the second partial bit number are added, the two values are directly added to obtain a partial bit accumulation value, which represents the total number of bit positions that the data moves right after the complete FFT and IFFT operations. In binary point operation, moving k bit positions to the right is equivalent to dividing the value by 2 raised to the power of k, that is, the amplitude is reduced by 2 raised to the power of k times, so the partial bit accumulation value is equal in value to the amplitude-reduced logarithm with base 2, and the greater the partial bit accumulation value, the more serious the data amplitude attenuation. The partial bit accumulation value comprehensively reflects the cumulative effect of the adaptive partial bit strategy on the signal amplitude in the entire FFT and IFFT operation link. The partial bit accumulation value is taken as the total partial bit number, which is a key parameter for calculating the amplitude compensation amount.

[0037] When the total partial bit number and the logarithm value of the FFT point number are compared, the logarithm with base 2 of the FFT point number N is first calculated to obtain a point number logarithm value. The point number logarithm value theoretically represents that the N-point FFT and IFFT operation will make the data amplitude expand by N times. Because the summation operation of the discrete Fourier transform involves the accumulation of N points, the amplitude is enlarged, and the amplitude expansion by N times is equivalent to moving the point number logarithm value bit positions to the left in binary representation. The point number logarithm value and the total partial bit number are compared and analyzed. The point number logarithm value represents the theoretical amplitude expansion effect of the FFT and IFFT operation, and the total partial bit number represents the actual amplitude reduction effect caused by the adaptive partial bit process. The difference between the two is the net amplitude change amount. If the point number logarithm value is greater than the total partial bit number, it means that the amplitude expansion effect exceeds the amplitude reduction effect, and the overall data amplitude still needs to be enlarged and compensated by moving to the right. If the point number logarithm value is less than the total partial bit number, it means that the amplitude reduction effect exceeds the amplitude expansion effect, and the overall data amplitude needs to be reduced and compensated by moving to the left. If the point number logarithm value is equal to the total partial bit number, it means that the two effects cancel each other out, and the data amplitude remains basically unchanged and does not need to be compensated. The compensation partial bit amount is calculated by subtracting the total partial bit number from the point number logarithm value. The compensation partial bit amount represents the number of bit positions that need to be adjusted to make the amplitude of the output time domain signal consistent with the amplitude of the input zero intermediate frequency time domain signal. When the compensation partial bit amount is positive, the time domain recovery data needs to be moved to the right. When the compensation partial bit amount is negative, the time domain recovery data needs to be moved to the left. The amplitude compensation scheme is determined by calculating the compensation partial bit amount.

[0038] In a specific embodiment, step S5 comprises: taking the logarithm with base 2 of the FFT point number to obtain a point number logarithm value; subtracting the point number logarithm value from the total partial bit number to obtain the compensation partial bit amount; performing a right shift operation on the time domain recovery data based on the compensation partial bit amount, and truncating the low bit width part of the shifted data; padding the high bits of the truncated data with a sign bit to obtain the output time domain signal.

[0039] Specifically, when taking the base-2 logarithm of the FFT point number, the FFT point number N is taken as the real number and two is taken as the base number of the logarithm. The point number logarithm value is calculated. The logarithm operation is mathematically solved by how many times two is equal to N. The point number logarithm value indicates that the N-point FFT and IFFT operations theoretically expand the data amplitude by N times. In binary fixed-point representation, expanding the amplitude by N times is equivalent to moving the data to the left by the point number logarithm value of bits. According to the definition of the discrete Fourier transform, each output frequency point of the N-point DFT is a weighted sum of N input time domain data. The summing process causes the numerical value to accumulate and expand the amplitude. Each output time domain data of the IDFT is also a weighted sum of N input frequency points but contains a normalization factor. The amplitude expansion factor of the combined FFT and IFFT operation is N. The point number logarithm value quantifies the amplitude expansion effect.

[0040] When the point number logarithm value is subtracted from the total clipping bit number, the compensation clipping amount is obtained. The subtraction operation calculates the net difference between the amplitude expansion effect caused by the FFT and IFFT operations and the amplitude reduction effect caused by the adaptive clipping. The point number logarithm value represents the number of bits that the data moves to the left, i.e., the amplitude expansion. The total clipping bit number represents the number of bits that the data moves to the right, i.e., the amplitude reduction. The subtraction of the two values obtains the net moving direction and the number of bits. The compensation clipping amount is positive, indicating that the amplitude expansion effect dominates and the data as a whole moves to the left, which needs to be compensated by moving to the right. The compensation clipping amount is negative, indicating that the amplitude reduction effect dominates and the data as a whole moves to the right, which needs to be compensated by moving to the left.

[0041] When the time domain recovery data is right-shifted based on the compensation clipping amount, the real part and the imaginary part of each data point in the time domain recovery data are right-shifted by the number of bits corresponding to the compensation clipping amount. The right-shift operation moves all the bits of the data to the low bit direction by a specified number of bits at the binary level. The high bit vacated bits are filled with a sign bit to maintain the correctness of the data sign. The low bit bits that exceed the specified number of bits are discarded, causing precision loss. Right-shifting k bits is equivalent to dividing by two to the power of k in numerical value. The amplitude of the time domain recovery data is reduced by two to the power of the compensation clipping amount after the right-shift operation. After shifting, the low bit width of the shifted data is the same as the input data quantization bit width m. The clipping operation continuously extracts m bits from the lowest bit of the shifted data, retains the effective value of the data, and discards the high bit sign extension bit.

[0042] When the high bits of the intercepted data are padded with a sign bit, the value of the highest bit of the intercepted data, i.e., the (m-1)th bit, is determined. The bit is the sign bit of the intercepted data. If the sign bit is zero, it indicates that the data is positive. In this case, all bit positions higher than the (m-1)th bit are filled with zeros. If the sign bit is one, it indicates that the data is negative. In this case, all bit positions higher than the (m-1)th bit are filled with ones. The sign bit padding operation ensures the correctness of the data value during bit width expansion or format conversion, prevents sign errors or abnormal values caused by bit width changes, and obtains an output time domain signal after the sign bit padding. The bit width of the output time domain signal is the same as that of the input zero intermediate frequency time domain signal, and the amplitude is adjusted to a level substantially consistent with the input signal through the compensation bit operation.

[0043] In an embodiment, a right shift operation is performed on the time domain recovery data based on the compensation bit quantity, and a low bit width part of the data after the shift is intercepted, including: The real part and the imaginary part of each data point in the time domain recovery data are right shifted by a number of bit positions corresponding to the compensation bit quantity, to obtain data after the shift; The low bit part with the same bit width as the quantization bit width of the input data is intercepted from the data after the shift, to obtain the low bit width part.

[0044] Specifically, when the real part and the imaginary part of each data point in the time domain recovery data are right shifted, the real part value and the imaginary part value of the data point are first extracted, and then the real part value is right shifted by a number of bit positions corresponding to the compensation bit quantity, and the imaginary part value is also right shifted by the same number of bit positions. The right shift operation moves all bit positions of the data to the low bit direction by a specified number of bits at the binary level. The bit positions vacated by the high bits are filled with the sign bit of the original data. If the original data is positive, the sign bit is zero, and the high bits are filled with zeros. If the original data is negative, the sign bit is one, and the high bits are filled with ones. The bit positions exceeding the low bits are discarded. Right shifting k bit positions is equivalent to dividing the value by 2 raised to the power of k and taking the integer part towards zero. After the right shift operation, the amplitudes of the real part and the imaginary part of the time domain recovery data are reduced by 2 raised to the power of the compensation bit quantity. The right shift operation is performed on all N data points of the time domain recovery data one by one. The real part and the imaginary part of each data point are right shifted by the same number of bits according to the compensation bit quantity. After the operation is completed, a sequence of data after the shift containing N data points is obtained.

[0045] When bit width truncation is performed on the data after shifting, a target bit width for truncation needs to be determined, the target bit width is the same as the quantization bit width m of the input data, and the truncation operation ensures that the bit width of the output data is consistent with the bit width of the input data, thereby meeting the bit width matching requirement of the signal processing link. The real part and the imaginary part of each data point in the data after shifting are subjected to truncation operations respectively, from the lowest bit, i.e., the zeroth bit, of the real part value, m bits are continuously extracted until the (m-1)th bit, and all bits higher than the (m-1)th bit are discarded, and the imaginary part value is subjected to the same truncation to extract the lower m bits and discard the higher bits. If the effective bit width of a certain value in the data after shifting does not exceed m bits, the value after truncation remains unchanged, and if the effective bit width of a certain value in the data after shifting exceeds m bits, the truncation operation retains the effective data of the lower m bits and discards the overflow of the higher bits. After the real part and the imaginary part of all N data points are subjected to the truncation operation, the lower bit width part is obtained, and the bit width of each data point in the lower bit width part is m bits, which is the same as the bit width of the input zero intermediate frequency time domain signal.

[0046] The FFT and IFFT adaptive truncation method in the embodiments of the application is described above, and the FFT and IFFT adaptive truncation system in the embodiments of the application is described below. Please refer to Figure 2 An embodiment of the FFT and IFFT adaptive truncation system in the embodiments of the application includes: An input module is configured to input a zero intermediate frequency time domain signal into an FFT operation unit to perform a butterfly operation, determine a truncation indication by judging the high bit state of the frequency point data after each stage of butterfly operation, perform low bit truncation or high bit truncation on the frequency point data according to the truncation indication, and accumulate the number of truncated bits to obtain spectrum data and a first total number of truncated bits. An identification module is configured to identify the position of narrowband interference according to the amplitude characteristics of each frequency point in the spectrum data, set the interference frequency point to zero, and obtain suppressed spectrum data. A truncation module is configured to perform conjugate transformation on the suppressed spectrum data, input the conjugated data into the FFT operation unit to perform a butterfly operation and perform truncation processing according to the truncation indication, perform conjugate transformation on the operation result again, and obtain time domain recovery data and a second total number of truncated bits. A summation module is configured to sum the first total number of truncated bits and the second total number of truncated bits to obtain a total number of truncated bits. A compensation module is configured to compensate the time domain recovery data based on the difference between the number of FFT points and the total number of truncated bits to obtain an output time domain signal.

[0047] The FFT and IFFT adaptive truncation system in the embodiments of the application is described in detail from the perspective of modular functional entities, and the FFT and IFFT adaptive truncation device in the embodiments of the application is described in detail from the perspective of hardware processing. Figure 2 The FFT and IFFT adaptive truncation system in the embodiments of the application is described in detail from the perspective of modular functional entities, and the FFT and IFFT adaptive truncation device in the embodiments of the application is described in detail from the perspective of hardware processing.

[0048] Referring to Figure 3 In the embodiments of the present application, an FFT and IFFT adaptive clipping device is also provided, which can be a server, and the internal structure of the server can be as shown in Figure 3 The FFT and IFFT adaptive clipping device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is used to provide computing and control capabilities. The memory of the FFT and IFFT adaptive clipping device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the FFT and IFFT adaptive clipping device is used to store the corresponding data in the embodiments. The network interface of the FFT and IFFT adaptive clipping device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the above method.

[0049] Those skilled in the art can understand Figure 3 The structure shown in the above is only a block diagram of part of the structure related to the present application, and does not constitute a limitation on the FFT and IFFT adaptive clipping device to which the present application is applied.

[0050] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium, and can also be a volatile computer readable storage medium. The computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the FFT and IFFT adaptive clipping method.

[0051] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, system and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0052] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing an FFT and IFFT adaptive clipping device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0053] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An adaptive truncation method for FFT and IFFT, characterized in that, The method includes: Step S1: Input the zero intermediate frequency time domain signal into the FFT operation unit for butterfly operation. Determine the truncation indication by judging the high bit state of the frequency point data after each butterfly operation. According to the truncation indication, truncate the frequency point data into low bits or high bits and accumulate the number of truncation bits to obtain the spectrum data and the total number of first truncation bits. Step S2: Identify the location of narrowband interference based on the amplitude characteristics of each frequency point in the spectrum data, set the interference frequency points to zero, and obtain the suppressed spectrum data; Step S3: Perform conjugate transformation on the suppressed spectral data, send the conjugate data into the FFT operation unit to perform butterfly operation and perform truncation processing according to the truncation instruction, perform conjugate transformation on the operation result again to obtain the time domain recovered data and the total number of bits of the second truncation; Step S4: Sum the total number of bits in the first truncation and the total number of bits in the second truncation to obtain the total number of bits in the truncation; Step S5: Based on the difference between the number of FFT points and the total number of truncation bits, the time-domain recovered data is truncated to obtain the output time-domain signal.

2. The adaptive truncation method for FFT and IFFT according to claim 1, characterized in that, Step S1 includes: Set the input data quantization bit width to m, the butterfly operation data extension bit width to n, and the butterfly operation level to L, and initialize the total number of bits in the first truncation to zero; Perform the i-th level butterfly operation on the zero intermediate frequency time domain signal to obtain N intermediate frequency point data, where the initial value of i is 1; Find the maximum value among the absolute values ​​of the real part and the absolute values ​​of the imaginary part of the N intermediate frequency point data, and determine whether the high n+1 bits of the maximum value are all 0. If they are all 0, mark the truncation indicator as 0; otherwise, mark the truncation indicator as 1. When the truncation indicator is 0, the lower m bits of the N intermediate frequency point data are truncated to obtain the truncated frequency point data. When the truncation indicator is 1, the higher m bits of the N intermediate frequency point data are truncated to obtain the truncated frequency point data. The total number of bits of the first truncation is accumulated by n bits. The truncated frequency point data is used as the input data for the next level of butterfly operation and i = i + 1 is set. The butterfly operation, judgment and truncation operation are repeated until i is greater than L to obtain the spectrum data and the total number of bits of the first truncation.

3. The adaptive truncation method for FFT and IFFT according to claim 1, characterized in that, Step S2 includes: The amplitude squared value of each frequency point is obtained by squaring the real part and imaginary part of each frequency point and then summing them. Based on the squared amplitude value, the statistical characteristics of the spectrum energy distribution are calculated, and an interference detection threshold is set. The squared amplitude value is compared with the interference detection threshold, and the frequency points that exceed the interference detection threshold are marked as narrowband interference locations; The real and imaginary parts of the frequency data corresponding to the narrowband interference position are both set to zero to obtain the suppressed spectrum data.

4. The adaptive truncation method for FFT and IFFT according to claim 1, characterized in that, Step S3 includes: Take the opposite of the imaginary part of each frequency point in the suppressed spectral data to obtain the conjugate data; The conjugated data is input into the FFT operation unit, and the total number of bits for the second truncation is initialized to zero. The j-th level butterfly operation is performed on the conjugated data to obtain N intermediate time-domain data. The maximum value is found among the absolute values ​​of the real and imaginary parts of the N intermediate time-domain data, and the state of the high n+1 bits of the maximum value is determined. The truncation indication is determined according to the determination result, and the N intermediate time-domain data are truncated and the number of truncation bits is accumulated. The truncated data is used as the input data for the next level butterfly operation, and j = j + 1 is set. The butterfly operation, determination and truncation operation are repeated until j is greater than L, and the operation result data and the total number of bits for the second truncation are obtained, where the initial value of j is 1. The time-domain recovered data is obtained by taking the inverse of the imaginary part of each data point in the calculation result data.

5. The adaptive truncation method for FFT and IFFT according to claim 1, characterized in that, Step S4 includes: Obtain the total number of bits for the first truncation and the total number of bits for the second truncation; Add the total number of bits in the first truncation to the total number of bits in the second truncation to obtain the cumulative truncation value; The accumulated truncation value is used as the total number of truncation bits; The compensation truncation amount is determined by comparing the total number of truncation bits with the logarithm of the FFT points.

6. The FFT and IFFT adaptive truncation method according to claim 5, characterized in that, Step S5 includes: The FFT point count is logarithmically calculated to base 2 to obtain the logarithmic value of the point count; The compensation truncation amount is obtained by subtracting the logarithmic value of the points from the total number of truncation bits. Based on the compensation truncation amount, the time-domain recovered data is right-shifted to extract the low-width portion of the shifted data; The high-order bits of the truncated data are padded with a sign bit to obtain the output time-domain signal.

7. The adaptive truncation method for FFT and IFFT according to claim 6, characterized in that, The step of right-shifting the time-domain recovered data based on the compensation truncation amount, and truncating the low-width portion of the shifted data, includes: The real and imaginary parts of each data point in the time-domain recovered data are shifted to the right by the number of bits corresponding to the compensation truncation amount to obtain the shifted data. The low-bit portion of the shifted data is truncated to have the same quantization bit width as the input data, thus obtaining the low-bit-width portion.

8. An adaptive truncation system for FFT and IFFT, characterized in that, For implementing the FFT and IFFT adaptive truncation method as described in any one of claims 1-7, the FFT and IFFT adaptive truncation system comprises: The input module is used to input the zero intermediate frequency time domain signal into the FFT operation unit for butterfly operation. By judging the state of the high-bit bits of the frequency point data after each butterfly operation, the truncation indication is determined. According to the truncation indication, the frequency point data is truncated to low-bit or high-bit and the number of truncation bits is accumulated to obtain the spectrum data and the total number of first truncation bits. The identification module is used to identify the location of narrowband interference based on the amplitude characteristics of each frequency point in the spectrum data, set the interference frequency point to zero, and obtain the suppressed spectrum data. The truncation module is used to perform a conjugate transformation on the suppressed spectral data, send the conjugate data to the FFT operation unit to perform butterfly operation and perform truncation processing according to the truncation instruction, and perform a conjugate transformation on the operation result again to obtain the time domain recovered data and the total number of bits of the second truncation. The summation module is used to sum the total number of bits of the first truncation and the total number of bits of the second truncation to obtain the total number of bits of the truncation. The compensation module is used to compensate and truncate the time-domain recovered data based on the difference between the number of FFT points and the total number of truncation bits, so as to obtain the output time-domain signal.

9. An FFT and IFFT adaptive truncation device, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, the processor executing the computer program to implement the FFT and IFFT adaptive truncation methods according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to perform the FFT and IFFT adaptive truncation methods as described in any one of claims 1 to 7.

Images