Data compression method and device, electronic equipment and storage medium

Abstract

The invention relates to a data compression method and device, electronic equipment and a storage medium, and relates to the technical field of wireless communication, and the method comprises the steps: obtaining to-be-compressed data, obtaining a compression bit width C, enabling the to-be-compressed data to be data retransmitted each time under a hybrid automatic retransmission mechanism, enabling the to-be-compressed data to be a floating-point number after the noise whitening operation, and enabling the to-be-compressed data to be the data retransmitted by each time under the hybrid automatic retransmission mechanism, and obtaining the compression bit width C; the compression bit width C is determined based on the current hardware, and the compression bit width C is smaller than the effective bit width of the current hardware; performing conversion from a floating-point number to a fixed-point number on the to-be-compressed data to obtain the fixed-point number corresponding to the to-be-compressed data; and based on the compression bit width C, compressing the fixed-point number corresponding to the to-be-compressed data to obtain compressed data. Therefore, the fixed point number corresponding to the to-be-compressed data can be compressed based on the compression bit width C, so that the storage space occupied by the data retransmitted each time under the hybrid automatic retransmission mechanism is reduced, and the purpose of reducing the hardware cost is achieved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a data compression method, device, electronic device and storage medium. Background Art

[0002] In the wireless communication protocols of New Radio (NR) or Long Term Evolution (LTE), the Hybrid Automatic Repeat reQuest (HARQ) mechanism is usually introduced to ensure the accuracy of data. The working principle of HARQ is that when the receiving end receives a code block, it decodes it and determines whether the decoding is successful. If the decoding fails, the sending end needs to retransmit it until the receiving end decodes it successfully.

[0003] In the related art, since each retransmitted data needs to be stored under the HARQ mechanism, it is easy to cause the problem of large storage space occupied by the data. Therefore, how to compress the data to reduce the storage space occupied by the data has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a data compression method, device, electronic device and storage medium to solve the problem of large data storage space required by the HARQ mechanism in the related art.

[0005] In a first aspect, an embodiment of the present application provides a data compression method, the method comprising: Obtaining data to be compressed, and obtaining a compression bit width C, wherein the data to be compressed is data retransmitted each time under a hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after a noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware; Convert the data to be compressed from floating point numbers to fixed point numbers to obtain fixed point numbers corresponding to the data to be compressed; Based on the compression bit width C, the fixed-point number corresponding to the data to be compressed is compressed to obtain compressed data.

[0006] Optionally, compressing the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data includes: Divide the fixed-point number corresponding to the data to be compressed by 2 C , so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole; The data occupying the bit width C in the high position of the calculated fixed-point number is deleted to obtain the compressed data.

[0007] Optionally, obtaining the data to be compressed includes: Initial data carrying colored noise in a data channel is acquired, and the colored noise is converted into white noise to obtain the data to be compressed.

[0008] Optionally, converting the colored noise into white noise to obtain the data to be compressed includes: Obtaining a covariance matrix corresponding to the colored noise, and decomposing the covariance matrix to obtain a lower triangular matrix, wherein the product of the lower triangular matrix and the conjugate transposed matrix of the lower triangular matrix is ​​equal to the covariance matrix; Based on the lower triangular matrix, determining a target filter, wherein a filter coefficient of the target filter is an inverse matrix of the lower triangular matrix; The target filter is used to convert the colored noise into white noise to obtain the data to be compressed, wherein the values ​​of the data to be compressed are normally distributed.

[0009] Optionally, after compressing the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data, the method further includes: Storing the compressed data; The stored compressed data is combined with the compressed data stored during the last retransmission under the hybrid automatic retransmission mechanism, and the combined data is decoded and verified to determine whether the decoding is successful.

[0010] In a second aspect, an embodiment of the present application further provides a data compression device, the device comprising: An acquisition module is used to acquire data to be compressed and to acquire a compression bit width C, wherein the data to be compressed is data retransmitted each time under a hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after a noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware; A conversion module, used for converting the data to be compressed from floating point numbers to fixed point numbers to obtain fixed point numbers corresponding to the data to be compressed; The compression module is used to compress the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data.

[0011] Optionally, the compression module includes: The calculation submodule is used to divide the fixed-point number corresponding to the data to be compressed by 2 C , so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole; The deletion submodule is used to delete the data occupying the bit width C in the high position of the calculated fixed-point number to obtain the compressed data.

[0012] Optionally, the acquisition module includes: The acquisition submodule is used to acquire the initial data carrying colored noise in the data channel, and convert the colored noise into white noise to obtain the data to be compressed.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the data compression method as described in the first aspect when executing the program stored in the memory.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the data compression method as described in the first aspect.

[0015] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: the method provided by the embodiment of the present application, by obtaining the data to be compressed and the compression bit width C, wherein the data to be compressed is the data to be retransmitted each time under the hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after the noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware; the floating point number to be compressed is converted to a fixed point number to obtain the fixed point number corresponding to the data to be compressed; based on the compression bit width C, the fixed point number corresponding to the data to be compressed is compressed to obtain the compressed data. In the above manner, the fixed point number corresponding to the data to be compressed can be compressed based on the compression bit width C, thereby reducing the storage space occupied by the data to be retransmitted each time under the hybrid automatic retransmission mechanism, and achieving the purpose of reducing hardware costs; and, since the data to be compressed has been subjected to the noise whitening operation, it conforms to the normal distribution characteristics, and thus can effectively reduce the problem of reduced decoding performance caused by reduced data accuracy caused by compression, so as to ensure that the decoding performance is not greatly affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A flowchart of a data compression method provided in an embodiment of the present application; Figure 2 A schematic diagram of a normal distribution provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a data compression device provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] See also Figure 1 , Figure 1 A flow chart of a data compression method provided in an embodiment of the present application. Figure 1 As shown, the data compression method may include the following steps: Step S102, obtaining the data to be compressed and the compression bit width C, wherein the data to be compressed is the data retransmitted each time under the hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after the noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware.

[0021] Specifically, the compression bit width C refers to the bit width required to compress the data to be compressed under the effective bit width of the hardware. The size of the compression bit width C is determined by the current hardware's own conditions, and its value cannot be greater than or equal to the effective bit width of the hardware. For example, assuming that the effective bit width of the hardware is 8 bits, the compression bit width C can be any value from 1 to 7. The letter C here represents the compression bit width in this application. Of course, other letters can also be used to represent the compression bit width, which does not constitute a limitation to this application.

[0022] The above-mentioned data to be compressed is the data retransmitted each time under the hybrid automatic repeat request mechanism HARQ. The reason why we need to consider compressing the data retransmitted each time under the hybrid automatic repeat request mechanism HARQ is that the automatic repeat request mechanism HARQ occupies a huge amount of memory space. Taking LTE as an example, the maximum length of a transport block (Transport Block, referred to as TB) under single-layer transmission is 137792 bits, while the length of a transport block TB under four-layer transmission can reach 502624 bits, and each bit in each transport block TB can correspond to a log-likelihood ratio (Log-Likelihood Ratio, referred to as LLR). When the hardware represents an LLR through 8 bits, about 1MB of storage space is required when storing data for the next transport block TB under single-layer transmission, and about 3.8MB of storage space is required when storing data for the next transport block TB under four-layer transmission. In addition, compared with LTE, NR has a larger amount of transmitted data, and a transport block TB can theoretically have no upper limit, so the storage space required is larger. It can be seen that when hybrid automatic retransmission mechanism HARQ is performed under LTE or NR, a large amount of storage space is required, which not only affects the area required for hardware, but also reduces the access rate and throughput of the memory.

[0023] It should be noted that before compressing each retransmitted data under the hybrid automatic repeat request mechanism HARQ, it is necessary to perform a noise whitening operation on it, that is, to convert the colored noise it carries into white noise so that it conforms to the normal distribution. In this way, the distribution amount of the compressed data in the range of [-1, +1] can reach about 68.27%, thereby effectively retaining the superposition influence of the Markov chain of the decimal part of most of the data to be compressed, effectively reducing the problem of decreased data accuracy caused by compression, so as to minimize the decoding performance loss.

[0024] Step S104: convert the floating point number to the fixed point number for the data to be compressed, and obtain the fixed point number corresponding to the data to be compressed.

[0025] Since the data to be compressed is a floating point number after noise whitening, it needs to be represented as a binary fixed point number before compression. Take an LLR represented by an 8-bit fixed point number as an example, where the first bit on the left is the highest bit, which represents the sign bit, and the other bits are data bits. When the floating point number a is 0.11234, the floating point number can be converted to a fixed point number according to the following formula to obtain the fixed point number a_fix corresponding to the floating point number a: a_fix = b'10000000+0.11234*2 7 = b'10000000+b'1110= b'10001110, where b' represents the binary representation.

[0026] Step S106: compress the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data.

[0027] Considering that the common combination of LLR itself is the integer part plus the decimal part, the integer part determines the positive and negative direction of the data in most cases, and the decimal part determines the superposition influence of the Markov chain when adjusting during decoding. Taking an LLR0 = +100000 as an example, in traditional decoding, if this codeword is wrong and the code length of the transport block (Transport Block, referred to as TB) is 137792 bits, it means that unless all other codewords are correct during decoding, the correction effect of this codeword can be achieved through the superposition of the Markov chain of the decimal part. It can be seen that retaining too much integer part information does not help much for decoding performance. Therefore, when decoding data, the data is mainly considered to be in line with the normal distribution after the noise whitening operation is performed. Based on the principle of normal distribution, it can be seen that the distribution of data in the range of [-1, +1] can reach about 68.27%, that is, most of the data after the noise whitening operation meets the decoding performance requirements. Therefore, when compressing the data to be compressed, the compression principle can be: retain the positive and negative signs of the data to be compressed, and perform corresponding compression on the decimal part. This can minimize the problem of decreased decoding performance caused by decreased data accuracy caused by compression while ensuring the basic direction of decoding.

[0028] Through the above method, the fixed-point number corresponding to the compressed data can be compressed based on the compression bit width C, thereby reducing the storage space occupied by the data retransmitted each time under the hybrid automatic retransmission mechanism, thereby achieving the purpose of reducing hardware costs; and, since the data to be compressed has been subjected to a noise whitening operation, it conforms to the normal distribution characteristics, and can effectively reduce the problem of reduced decoding performance caused by reduced data accuracy due to compression, so as to ensure that the decoding performance is not greatly affected.

[0029] In an optional embodiment, the above step S106, compressing the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data, includes: Divide the fixed-point number corresponding to the data to be compressed by 2 C , so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole; The data occupying the bit width C in the high position of the calculated fixed-point number is deleted to obtain compressed data.

[0030] Specifically, when compressing the fixed-point number corresponding to the compressed data, the fixed-point number corresponding to the compressed data can be divided by 2. C, so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole. When the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole, the rightmost C-bit data of the fixed-point number corresponding to the data to be compressed is lost, and the leftmost C-bit data of the fixed-point number corresponding to the data to be compressed is replaced by 0. Next, the data occupying the bit width C in the high position of the calculated fixed-point number is deleted to obtain the compressed data. The data occupying the bit width C in the high position here refers to the leftmost data occupying the bit width C, because in the binary data representation, the highest bit is located at the leftmost side of the data, and the lowest bit is located at the rightmost side of the data.

[0031] For example, when the floating point number a is 0.11234, the floating point number can be converted to a fixed point number according to the following formula to obtain the fixed point number a_fix corresponding to the floating point number a: a_fix = b'10000000+0.11234*2 7 =b'10000000+b'1110=b'10001110. Assuming that the compression bit width C is 2, when compressing the floating point number a, the binary fixed point number b'10001110 can be first shifted right by 2 bits as a whole to obtain the binary fixed point number b'00100011. Then the highest 2 bits of the binary fixed point number b'00100011 are deleted to obtain the binary fixed point number b'100011, where the leftmost bit is the highest bit, indicating the positive or negative sign of the data, and the other 5 bits represent the decimal part of the floating point number. Converting the binary fixed point number b'100011 from fixed point to floating point can obtain a'=+(b'11 / 2 5 ) = 0.09375. It can be seen that after the floating point number a is compressed, the data loss caused by the decrease in data precision is 0.11234-0.09375=0.01859. Although there will be a certain amount of data loss, the direction it represents is consistent and has little impact on the overall decoding performance.

[0032] Through the above method, the sign bit of the data before and after compression can be kept unchanged, and only the decimal part is compressed accordingly. Therefore, under the premise of ensuring the basic direction of decoding, the problem of reduced decoding performance caused by reduced data accuracy caused by compression can be minimized.

[0033] In an optional embodiment, the above step 102 of obtaining the data to be compressed includes: Initial data carrying colored noise in a data channel is obtained, and the colored noise is converted into white noise to obtain data to be compressed.

[0034] Specifically, the colored noise mentioned above refers to the noise with uneven power distribution at different frequencies. Due to the interference of colored noise, the data in the channel will not conform to the normal distribution. The white noise mentioned above refers to the noise with uniform power spectrum density in the entire frequency domain. Therefore, after the colored noise is converted into white noise, the data in the channel will conform to the normal distribution, such as Figure 2 shown.

[0035] Therefore, when obtaining the data to be compressed, the initial data carrying colored noise in the data channel can be obtained, and then the colored noise can be converted into white noise using a noise whitening operation to obtain the data to be compressed, so that the data to be compressed conforms to the normal distribution and can meet the decoding performance requirements after data compression.

[0036] In an optional embodiment, the above step of converting colored noise into white noise to obtain data to be compressed includes: Obtain the covariance matrix corresponding to the colored noise, and decompose the covariance matrix to obtain a lower triangular matrix, wherein the product of the lower triangular matrix and the conjugate transposed matrix of the lower triangular matrix is ​​equal to the covariance matrix; Based on the lower triangular matrix, a target filter is determined, wherein a filter coefficient of the target filter is an inverse matrix of the lower triangular matrix; The colored noise is converted into white noise by using a target filter to obtain the data to be compressed, wherein the values ​​of the data to be compressed are normally distributed.

[0037] Specifically, when the colored noise is converted into white noise, the covariance matrix corresponding to the colored noise can be obtained: , and decompose the covariance matrix to obtain the lower triangular matrix , where the product of the lower triangular matrix and the conjugate transposed matrix of the lower triangular matrix is ​​equal to the covariance matrix, satisfying the following formula: ; in, represents the covariance matrix corresponding to colored noise, represents the lower triangular matrix and H represents the conjugate transposed matrix.

[0038] Then we can use the lower triangular matrix , determine the target filter, where the filter coefficient of the target filter is the inverse matrix of the lower triangular matrix, that is, the filter coefficient of the target filter is Then, the target filter can be used to convert the colored noise into white noise to obtain the data to be compressed. In this way, the data to be compressed can conform to the normal distribution and meet the decoding performance requirements after data compression.

[0039] In an optional embodiment, in the above step 106, after compressing the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain the compressed data, the method further includes: Storing the compressed data; The stored compressed data is combined with the compressed data stored during the last retransmission under the hybrid automatic retransmission mechanism, and the combined data is decoded and verified to determine whether the decoding is successful.

[0040] Specifically, after compressing the compressed data, the compressed data corresponding to the compressed data can also be stored. In this way, the stored compressed data can be combined with the compressed data stored during the last retransmission under the hybrid automatic retransmission mechanism, and the combined data can be decoded and verified to determine whether the decoding is successful, thereby realizing the automatic retransmission mechanism HARQ and ensuring the accuracy of the data.

[0041] In order to verify the compression performance loss of the data compression method provided in the embodiment of the present application, considering that 8 bits are often used in hardware to represent one LLR data, for different code rate ranges, LLR is compressed to 6 bits, 4 bits and 2 bits respectively, and simulation tests are performed. The final decoding performance loss is shown in the following Table 1:

[0042] Since different code rate ranges will determine the coding diagram of the Low-Density Parity-Check Code (LDPC) used under the 3rd Generation Partnership Project (3GPP), different coding diagrams will have different decoding performance impacts. Therefore, in order to characterize the compression effect of the present invention in different scenarios, it is necessary to cover as many different coding diagrams as possible. The coding performance loss here refers to the loss value of the final decoded block error rate (BLER) mapped to the signal-to-noise ratio (SNR).

[0043] As can be seen from the above table, when the data compression method provided by the embodiment of the present application is used for data compression, the data accuracy will be lost, but since the data to be compressed conforms to the normal distribution, the data loss after compression can be controlled as low as possible within a low range to reduce the impact of the data loss after compression on the accuracy of the final decoding. As shown in the simulation results, when the data is compressed from 8 bits to 6 bits, the compression rate is 25%, and the average loss of different code rate ranges is 0dB, but because the normal distribution is not perfect, the influence of noise causes jitter in some cases, so there will be a performance degradation of 0dB to 0.4dB under the worst loss. Similarly, when the data is compressed from 8 bits to 2 bits, the compression rate is as high as 75%, and the normal distribution characteristics of the data are also affected more, so the average loss of different code rate ranges has a performance degradation from 0.1dB to 1.3dB, and the performance loss is smaller as the compression coding code rate increases, because the larger the amount of data, the more its normal distribution characteristics can be guaranteed. Therefore, by using the data compression method provided by the embodiment of the present application, data compression can be achieved through a smaller compression performance loss, reducing the demand for memory, thereby achieving the purpose of reducing hardware area and cost.

[0044] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a data compression device provided in an embodiment of the present application. Figure 3 As shown, the data compression device 300 includes: The acquisition module 302 is used to acquire the data to be compressed and the compression bit width C, wherein the data to be compressed is the data retransmitted each time under the hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after the noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware; The conversion module 304 is used to convert the floating point number to the fixed point number of the data to be compressed, so as to obtain the fixed point number corresponding to the data to be compressed; The compression module 306 is used to compress the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data.

[0045] Further, the compression module 306 includes: The calculation submodule is used to divide the fixed-point number corresponding to the data to be compressed by 2 C , so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole; The deletion submodule is used to delete the data occupying the bit width C in the high position of the calculated fixed-point number to obtain compressed data.

[0046] Furthermore, the acquisition module 302 includes: The acquisition submodule is used to acquire the initial data carrying colored noise in the data channel and convert the colored noise into white noise to obtain the data to be compressed.

[0047] Furthermore, the acquisition submodule includes: An acquisition unit, used for acquiring a covariance matrix corresponding to the colored noise, and decomposing the covariance matrix to obtain a lower triangular matrix, wherein the product of the lower triangular matrix and the conjugate transposed matrix of the lower triangular matrix is ​​equal to the covariance matrix; A determination unit, configured to determine a target filter based on a lower triangular matrix, wherein a filter coefficient of the target filter is an inverse matrix of the lower triangular matrix; The conversion unit is used to convert the colored noise into white noise by using a target filter to obtain data to be compressed, wherein the values ​​of the data to be compressed are normally distributed.

[0048] Furthermore, the data compression device 300 further includes: A storage module, used for storing compressed data; The combining module is used to combine the stored compressed data with the compressed data stored during the last retransmission under the hybrid automatic retransmission mechanism, and decode and verify the combined data to determine whether the decoding is successful.

[0049] It should be noted that the data compression device 300 can implement the steps of the data compression method provided by any of the aforementioned method embodiments and can achieve the same technical effects, which will not be described in detail herein.

[0050] like Figure 4 As shown, the embodiment of the present application further provides an electronic device, including a processor 411, a communication interface 412, a memory 413 and a communication bus 414, wherein the processor 411, the communication interface 412, and the memory 413 communicate with each other through the communication bus 414. Memory 413, used for storing computer programs; In one embodiment of the present application, the processor 411 is used to implement the steps of the data compression method provided by any of the aforementioned method embodiments when executing the program stored in the memory 413.

[0051] The electronic device provided in the embodiment of the present application may specifically be a module capable of realizing a communication function or a terminal device including the module, etc. The terminal device may be a mobile terminal or a smart terminal. The mobile terminal may specifically be at least one of a mobile phone, a tablet computer, a laptop computer, etc.; the smart terminal may specifically be a terminal including a wireless communication module such as a smart car, a smart watch, a shared bicycle, a smart cabinet, etc.; the module may specifically be a wireless communication module, such as any one of a 2G communication module, a 3G communication module, a 4G communication module, a 5G communication module, and a NB-IOT communication module.

[0052] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the data compression method provided in any of the aforementioned method embodiments are implemented.

[0053] The embodiment of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device performs the steps of the above-mentioned data compression method.

[0054] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0055] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A data compression method, characterized in that: The method comprises: Obtaining data to be compressed, and obtaining a compression bit width C, wherein the data to be compressed is data retransmitted each time under a hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after a noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware; Convert the data to be compressed from floating point numbers to fixed point numbers to obtain fixed point numbers corresponding to the data to be compressed; Based on the compression bit width C, the fixed-point number corresponding to the data to be compressed is compressed to obtain compressed data.

2. The data compression method according to claim 1, characterized in that: The step of compressing the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data includes: Divide the fixed-point number corresponding to the data to be compressed by 2 C , so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole; The data occupying the bit width C in the high position of the calculated fixed-point number is deleted to obtain the compressed data.

3. The data compression method according to claim 1, characterized in that: The obtaining of the data to be compressed comprises: Initial data carrying colored noise in a data channel is acquired, and the colored noise is converted into white noise to obtain the data to be compressed.

4. The data compression method according to claim 3, characterized in that: The step of converting the colored noise into white noise to obtain the data to be compressed includes: Obtaining a covariance matrix corresponding to the colored noise, and decomposing the covariance matrix to obtain a lower triangular matrix, wherein the product of the lower triangular matrix and the conjugate transposed matrix of the lower triangular matrix is ​​equal to the covariance matrix; Based on the lower triangular matrix, determining a target filter, wherein a filter coefficient of the target filter is an inverse matrix of the lower triangular matrix; The target filter is used to convert the colored noise into white noise to obtain the data to be compressed, wherein the values ​​of the data to be compressed are normally distributed.

5. The data compression method according to claim 1, characterized in that: After compressing the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data, the method further includes: Storing the compressed data; The stored compressed data is combined with the compressed data stored during the last retransmission under the hybrid automatic retransmission mechanism, and the combined data is decoded and verified to determine whether the decoding is successful.

6. A data compression device, characterized in that: The device comprises: An acquisition module is used to acquire data to be compressed and to acquire a compression bit width C, wherein the data to be compressed is data retransmitted each time under a hybrid automatic retransmission mechanism, and the data to be compressed is a floating point number after a noise whitening operation is performed, and the compression bit width C is determined based on the current hardware itself, and the compression bit width C is smaller than the effective bit width of the current hardware; A conversion module, used for converting the data to be compressed from floating point numbers to fixed point numbers to obtain fixed point numbers corresponding to the data to be compressed; The compression module is used to compress the fixed-point number corresponding to the data to be compressed based on the compression bit width C to obtain compressed data.

7. The data compression device according to claim 6, characterized in that: The compression module comprises: The calculation submodule is used to divide the fixed-point number corresponding to the data to be compressed by 2 C , so that the fixed-point number corresponding to the data to be compressed is shifted right by C bits as a whole; The deletion submodule is used to delete the data occupying the bit width C in the high position of the calculated fixed-point number to obtain the compressed data.

8. The data compression device according to claim 6, characterized in that: The acquisition module comprises: The acquisition submodule is used to acquire the initial data carrying colored noise in the data channel, and convert the colored noise into white noise to obtain the data to be compressed.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the data compression method according to any one of claims 1 to 5 when executing a program stored in a memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data compression method according to any one of claims 1 to 5 is implemented.

Images